Post Syndicated from Tonya Comer original https://backblaze.com/blog/video-surveillance-data-storage-cloud-vs-on-prem-vs-hybrid/

Depending on your industry, you may need to install and run video surveillance. And once you have footage, you might be required to store it for a set period of days, months, or even years. This leads to the question: Where are you supposed to keep it all?

Not all storage systems are created equal, so it’s important to weigh the benefits and drawbacks of each option before making a decision. In some cases, government and industry regulations will require you to use a certain type of storage system. Ultimately, you will benefit from knowing how the system functions, what risks are involved, and how to select a technology provider.

This article will help you consider the pros and cons of on-premises, cloud, and hybrid storage systems. As you read, keep in mind that the amount of storage you need for your enterprise will depend on the number of cameras you have, the quality of the video footage, the length of time you are required to retain the footage, and various other factors. 

First Things First: Your Backup Strategy

No matter how or where you store your video surveillance footage, the most important thing you should do is establish a backup strategy that follows the 3-2-1 backup approach. That means you should have three copies of your data on two different media with one stored off-site. In this post, we’ll weigh the pros and cons of whether you keep that off-site copy stored at an off-site location like, say, an Iron Mountain storage facility, a remote office, or data center, or whether you keep that off-site copy in the cloud. 

You might think we’re biased as a cloud provider. Of course, we’d love it if you choose to keep your backups with Backblaze! But the main thing we want to emphasize is that you should have a backup plan for your video surveillance footage (or any data, really!) whether it includes Backblaze or not. And, because you have to store one of those copies off-site, it’s miles easier (pun intended) to store in the cloud than to physically drive or mail hard drives to a secondary location.  

What Is On-Premises Storage?

Storing video footage on-premises means your data is stored on physical media—that is, servers, network attached storage (NAS), storage area network (SAN), LTO tape (linear tape open), etc.—in a physical location on your premises. We’ll talk about two forms of on-premises storage as they pertain to video footage: NAS and SAN.

Are NAS Devices Good for Storing Video Footage?

NAS devices have a large data storage capacity that provides file-based data storage services to other devices on a network. Usually, they also have a client or web portal interface, as well as services like QNAP’s Hybrid Backup Sync or Synology’s Hyper Backup to help manage your files.

One of the benefits of NAS is that it’s easy to set up and use, and you can upgrade internal drives over time. The main drawback when it comes to storing video surveillance footage is that its storage capacity is limited. Even if you buy a bigger device than you need right now, eventually you’ll run out of space and need to buy more, especially if you’re storing large amounts of video surveillance footage.

Is a SAN Good for Storing Video Footage?

On the other end of the spectrum, SANs are engineered for high-performance and mission-critical applications. They function by connecting multiple storage devices, such as disk arrays or tape libraries, to a dedicated network that is separate from the main local area network (LAN).

SANs offer high-speed data access, critical for handling large video streams from multiple cameras and allow for seamless scalability. As video surveillance systems grow, SANs can accommodate additional cameras and storage without disrupting ongoing operations. They also provide enhanced data security by isolating block-level storage within the operating system layer, to protect against failures and unauthorized access. Managing SANs can be a bit complex, necessitating skilled administrators familiar with SAN architecture. Additionally, implementing SANs incurs upfront expenses for hardware, software, and expertise, while their reliance on centralized controllers poses a risk of impacting multiple cameras in case of failure.

What Is Cloud Storage?

Cloud storage enables you to securely store data and files in an off-site location. You can access this data through the public internet.

When you transfer data off-site for storage, the cloud storage provider (CSP) hosts, secures, manages, and maintains the servers and associated infrastructure, ensuring that you have seamless access to your data whenever you need it.

What Are the Benefits of Cloud Storage for Video Surveillance Footage?

1. Scalability: Cloud storage services allow you to dynamically adjust capacity as your video surveillance data volumes fluctuate. 
2. Avoid capital expenses (CapEx): By leveraging cloud storage for video surveillance, your organization benefits from paying for storage technology and capacity as a service, rather than incurring the capital expenses associated with constructing and upkeeping in-house storage networks. As data volumes grow over time, your costs may increase, but there’s no need to overprovision storage networks in anticipation of future data expansion. 
3. Security: Cloud surveillance systems enhance data security with unique user accounts and data encryption ensure that only authorized personnel can access the footage. This controlled access minimizes the risk of unauthorized viewing or tampering.
4. Accessibility: Cloud storage relies on an internet or network connection so authorized users can access surveillance footage remotely from anywhere using smart devices or web browsers. Whether you’re at the office, traveling, or even at home, you can review camera feeds without being physically present on-site. Keep in mind if the connection is lost or disrupted, access to video footage becomes challenging. This dependency can impact real-time monitoring and retrieval of critical data.

Just like our other storage strategies, there are drawbacks to cloud storage. For example, it relies on a stable internet connection. Video surveillance files are large, even when you apply compression techniques, which means that they take time and proper network connections to upload. So, if your internet connection goes down, it takes longer to get data properly stored or backed up than it would with other file types. That means you may not have real-time access to your data, or (in the worst cases) that you potentially risk file corruption if you don’t have a robust enough local storage infrastructure. 

Similarly, businesses should evaluate the privacy and data ownership concerns. Storing video footage in the cloud means entrusting sensitive data to a third-party service provider. Make sure that your CSP meets or exceeds all regulatory or compliance requirements, like SOC 2 or ISO 27001, before you store data on their platforms. 

All things considered, cloud storage offers scalability, ease of access, fine–tuned file control, and minimal maintenance, which are essential when dealing with the complexities of storing video surveillance footage.

Direct-to-Cloud Video Surveillance

Some companies choose to transfer video surveillance off-site to the cloud for backup purposes, while others push video footage directly to the cloud as a primary storage location, especially as there are several camera models and video surveillance solutions that are designed to easily push footage directly to cloud storage. When you’re choosing video surveillance hardware, it’s worth looking into whether they have this functionality, and if so, how much control you have over setting your storage destination to optimize costs. 

And, if you’re using cloud storage as the primary storage for video footage, a multi-cloud setup can be used to ensure the primary copy in the cloud is backed up. A multi-cloud setup involves using multiple cloud service providers simultaneously—so, if your video surveillance platform stores footage in their own cloud, you can still set up a workflow that backs up to a different CSP. For backup and archive purposes, organizations can distribute their data across different clouds to enhance reliability, reduce risk, create geographic diversity in storage locations for disaster recovery purposes, and to comply with data retention policies. This approach ensures data availability even if one cloud provider experiences issues.

What Is Hybrid Cloud Storage?

Hybrid cloud storage combines elements from both public clouds and private clouds (typically on-premises systems). It’s essentially a unified management approach where an integrated infrastructure enables seamless movement of workloads and data between the private and public clouds.

Using a hybrid cloud for video surveillance makes sense for lots of use cases, including backup and archive. Let’s talk about how. 

Backup: To deploy a hybrid approach for a video surveillance backup use case, you’d store all of your video surveillance footage in your on-premises systems, then store your backups in the cloud. Many NAS devices, for example, come with on-board backup utilities that allow you to store backups of your video surveillance footage directly in the cloud. You could also use third-party backup software to automatically back up your systems to the cloud. This hybrid approach gives you fast access to your footage via your on-premises storage, while protecting it with cloud backups.

Archive: To deploy a hybrid approach for a video surveillance archive use case, you’d store recent live recordings of your video surveillance footage on-premises. After a recurring cutoff date—whether in days or months—you then move old footage to a public cloud. This hybrid system allows you to access recent footage quickly while archiving older footage, particularly if you have retention requirements for compliance or cyber insurance purposes. If done right, this system can help your company comply with both short- and long-term industry requirements.

For a more in-depth look at hybrid cloud storage, check out our blog on hybrid cloud. 

Is Hybrid Cloud Good for Video Surveillance Footage?

Leveraging hybrid cloud storage provides a dual advantage for video surveillance: swift local access to your video surveillance footage while simultaneously safeguarding it through off-site backups or off-loading it through a cloud archive. This strategic approach allows you to harness the strengths of both public and private clouds. Moreover, it offers enhanced scalability and flexibility compared to traditional on-premises solutions.

However, it’s essential to note that implementing a private cloud system can be cost-intensive. It necessitates budgeting for hardware acquisitions and replacements over time. Additionally, you’ll likely need to allocate resources for dedicated staff to maintain servers and backup strategies.

The Verdict: Which Type of Storage Is Best for Video Surveillance?

Choosing the right video surveillance storage solution is a critical decision for any organization. On-premises, cloud, and hybrid cloud each have their merits and drawbacks. While on-premises solutions offer large data storage capacity that is easy to set up and use, they require significant infrastructure investment. Cloud storage provides data accessibility and scales seamlessly while optimizing cost-effectiveness. Hybrid cloud provides both rapid local access to your video surveillance footage and secure off-site backups. 

Ultimately, the choice depends on your specific needs, budget, and long-term strategy. Consider the trade-offs carefully to ensure seamless and reliable video storage for your surveillance system.

The post Video Surveillance Data Storage: Cloud vs. On-Prem vs. Hybrid appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Post Syndicated from Stephanie Doyle original https://backblazeprod.wpenginepowered.com/blog/ai-101-what-is-model-serving/

If you read a blog article that starts with “In today’s fast-paced business landscape…” you can be 99% sure that content is AI generated. While large language models (LLMs) like ChatGPT, Gemini, and Claude may be the shiniest of AI applications from a consumer standpoint, they still have a ways to go from a creativity standpoint. 

That said, there are exciting possibilities for artificial intelligence and machine learning (AI/ML) algorithms to improve and create products now and in the future, many of which focus on replicated operations, split second database predictions, natural language processing, threat analysis, and more. As you might imagine, deployment of those algorithms comes with its own set of complexities. 

To solve for those complexities, specialized operations platforms have sprung up—specifically, AI/ML model serving platforms. Let’s talk about AI/ML model serving and how it fits into “today’s fast-paced business landscape.” (Don’t worry—we wrote that one.)

What Is AI/ML Model Serving?

AI/ML model serving refers to the process of deploying machine learning models into production environments where they can be used to make predictions or perform tasks based on real-time or batch input data. 

Trained machine learning models are made accessible via APIs or other interfaces, allowing external applications or systems to send real-world data to the models for inference. The served models process the incoming data and return predictions, classifications, or other outputs based on the learned patterns encoded in the model parameters. 

Practically, you can compare building an application that uses an AI/ML algorithm to a car engine. The whole application (the engine) is built to solve a problem; in this case “transport me faster than walking.” There are various subtasks to help you solve that problem well. Let’s take the exhaust system as an example. The exhaust fundamentally does the same thing from car to car—it moves hot air off the engine—but once you upgrade your exhaust system (i.e. add an AI algorithm to your application), you can tell how your engine works differently by comparing your car’s performance to a base-level model of the same one. 

Now let’s plug in our “smart” element, and it’s more like your exhaust has the ability to see that your car has terrible fuel efficiency, identifies that it’s because you’re not removing hot air off the engine well enough, and re-route the pathway it’s using through your pipes, mufflers, and catalytic converters to improve itself. (Saving you money on gas—wins all around.) 

Model serving, in this example, would be a shop that specializes in installing and maintaining exhausts. They’re experts at plugging in your new exhaust and having it work well with the rest of the engine even if it’s a newer type of tech (so, interoperability via API), and they have thought through and created frameworks for how to make sure the exhaust is functioning once you’re driving around (i.e. metrics). They’ve got a ton of ready-made parts and exhaust systems to recommend (that’s your model registry). When they install your new system in your engine, they might have some tweaks that work specifically in your system, too (versioning over time to serve your specific product).  

Ok, back to the technical details. From an architecture standpoint, model serving also lets you separate your production model from the base AI/ML model in addition to creating an accessible endpoint (read: an API or HTTPS access point, etc.). This separation has benefits—making tracking model drift and versioning simpler, for instance. 

Like traditional software engineering, most AI/ML model serving platforms also have code libraries of fully or partially trained models—the model registry in the image above. For example, if you’re running a photo management application, you might grab an image recognition model and plug it into your larger application. 

This is a tad more complex than other types of code deployment because you can’t really tell if an AI/ML model is functioning correctly until it’s working on real-world data. Certainly, that’s somewhat true of all code deployments—you always find more bugs when you’re live—but because AI/ML models are performing complex tasks like making predictions, natural language processing, etc., even a trained model has more room for “error” that becomes evident when it’s in a live environment. And, in many use cases—like fraud detection or network intrusion detection—models need to have very low latency to perform properly. 

Because of that, deciding what kind of code deployment to use can have a high impact on your end users. For example, lots of experts recommend leveraging shadow deployment techniques, where your AI/ML model is ingesting live data, but running on a parallel environment invisible to end users, for phase one of your deployment. 

Machine Learning Operations (MLOps) vs. AI/ML Model Serving

In reading about model serving, you’ll inevitably also come across folks talking about MLOps as well. (An Ops for every occasion, as they say. “They” being me.) You can think of MLOps as being responsible for the entire, end-to-end process, whereas AI/ML model serving focuses on one part of the process. Here’s a handy diagram that outlines the whole MLOps lifecycle:

And, of course, you’ll see one box on there that’s called “model serving”.

How to Choose a Model Serving Platform

AI model serving platforms typically provide features such as scalability to handle varying workloads, low latency for real-time predictions, monitoring capabilities to track model performance and health, versioning to manage multiple model versions, and integration with other software systems or frameworks. 

Choosing the right one is not a one-size-fits-all approach. Model serving platforms give you a whole host of benefits, operationally speaking—they deliver better performance, scale easily with your business, integrate well with other applications, and give you valuable monitoring tools from both a performance and security perspective. But, there are a ton of other factors that can come into play that aren’t immediately apparent, such as preferred code languages (Python is right up there), the processing/hardware platform you’re using, budget, what level of control and fine-tuning you want over APIs, how much management you want to do in-house vs. outsourcing, how much support/engagement there is in the developer community, and so on.

Popular Model Serving Platforms

Now that you know what model serving is, you might be wondering how you can use it yourself. We rounded up some of the more popular platforms so you can get a sense of the diversity in the marketplace: 

• TensorFlow Serving: An open-source serving system for deploying machine learning models built with TensorFlow. It provides efficient and scalable serving of TensorFlow models for both online and batch predictions. 
• Amazon SageMaker: A fully managed service provided by Amazon Web Services (AWS) for building, training, and deploying machine learning models at scale. SageMaker includes built-in model serving capabilities for deploying models to production.
• Google Cloud AI Platform: A suite of cloud-based machine learning services provided by Google Cloud Platform (GCP). It offers tools for training, evaluation, and deployment of machine learning models, including model serving features for deploying models in production environments.
• Microsoft Azure Machine Learning: A cloud-based service offered by Microsoft Azure for building, training, and deploying machine learning models. Azure Machine Learning includes features for deploying models as web services for real-time scoring and batch inferencing.
• Kubernetes (K8s): While not a model serving platform in itself, Kubernetes is a popular open-source container orchestration platform that is often used for deploying and managing machine learning models at scale. Several tools and frameworks, such as Kubeflow and KFServing, provide extensions for serving models on Kubernetes clusters.
• Hugging Face: Known for its open-source libraries for natural language processing (NLP), Hugging Face also provides a model serving platform for deploying and managing natural language processing models in production environments.

The Practical Approach

In short, AI/ML model serving platforms make ML algorithms much more manageable and accessible for all kinds of applications. Choosing the right one (as always) comes down to your particular use case—so, test thoroughly, and let us know what’s working for you in the comments.

The post AI 101: What Is Model Serving? appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Post Syndicated from Brent Nowak original https://backblazeprod.wpenginepowered.com/blog/backblaze-network-stats-real-world-measurements-from-the-backblaze-us-west-region/

As you’re sitting down to watch yet another episode of “Mystery Science Theater 3000” (Anyone else? Just me?), you might be thinking about how you’re receiving more than 16 billion signals over your home internet connection that need to be interpreted and sent to decoding software in order to be viewed on a screen as images. (Anyone else? Just me?)

In telecommunications, we study how signals (electrical and optical) propagate in a medium (copper wires, optical fiber, or through the air). Signals bounce off objects, degrade as they travel, and arrive at the receiving end (hopefully) intact enough to be interpreted as a binary 0 or 1, eventually to be decoded to form images that we see on our screens.

Today in the latest installment of Network Stats, I’m going to talk about one of the most important things that I’ve learned about the application of telecommunication to computer networks: the study of how long operations take. It’s a complicated process, but that doesn’t make buffering any less annoying, am I right? And if you’re using cloud storage in any way, you rely on signals to run applications, back up your data, and maybe even stream out those movies yourself. 

So, let’s get into how we measure data transmission, what things we can do to improve transmission time, and some real-world measurements from the Backblaze US-West region.

Networking and Nanoseconds: A Primer

At the risk of being too basic, when we study network communication, we’re measuring how long signals take to get from point A to point B, which implies that there is some amount of distance between them. This is also known as latency. As networks have evolved, the time it takes to get from point A to point B has gone from being measured in hours to being measured in fractions of a second. Since we live in more human relatable terms like minutes, hours, and days, it can be hard for us to understand concepts like nanoseconds (a billionth of a second). 

Here’s a breakdown of how many milli, micro, and nanoseconds are in one second.

For reference, taking a deep breath takes approximately one second. When you’re watching TV, you start to notice audio delays at approximately 10–20ms, and if your cat pushes your pen off your desk, it will hit the ground in approximately 400ms.

Nanosecond Wire: Making Nanoseconds Real

In the 1960s, Grace Murray Hopper (1906–1992) explained computer operations and what a nanosecond means in a succinct, tangible fashion. An early computer scientist who also served in the U.S. Navy, Hopper was often asked by folks like generals and admirals why it took so long for signals to reach satellites. So, Hopper had pieces of wire cut to 30cm (11.8in) in length, which is the distance that it takes light to travel in perfect conditions—that is, a vacuum—in one nanosecond. (Remember: we humans express speed as distance over time.) 

You could touch the wire, feel the distance from point A to point B, and see what a nanosecond in light-time meant. Put a bunch of them together, and you’d see what that looks like on a human scale. In literal terms, she was forcing people to measure the distance to a satellite (anywhere between 160–35,786 km) in terms of the long side of a piece of printer paper. (Rough math: it’s about 741,058,823 to 1,657,529,411,764 pieces of paper end-to-end.) That’ll definitely make you realize that there are a lot of nanoseconds between you and the next city over or a satellite in space.

A fun side note: if we go up a factor from a nanosecond to a microsecond, the wire would be almost 300 meters (984 feet)—which is the height of the Eiffel Tower, minus the broadcast antennae. It gets even more fun to think about because we still have to scale up to two more orders of magnitude to get to a millisecond, and then again to get a second. 

I love when a difficult topic can be grasped with an elegant explanation. It’s one of the skills that I strive to develop as an engineer—how can I relate a complicated concept to a larger audience that doesn’t have years of study in my field, and make it easily digestible and memorable?

Added Application Time

We don’t live in an ideal, perfect vacuum where signals can propagate in a line-of-sight fashion and travel in an optimal path. We have wires in buildings and in the ground that wind around metro regions, follow long-haul railroad tracks as they curve, and have to pass over hills and along mountainous terrain that add elevation to the wire length, all adding light-time. 

These physical factors are not the only component in the way of receiving your data. There are computer operations that add time to our transactions. We have to send a “hello” message to the server and wait for an acknowledgement, negotiate our security protocols so that we can transmit data without anyone snooping in on the conversation, spend time receiving the bytes that make up our file, and acknowledge chunks of information as received so the server can send more.

How much do geography and software operations add to the time it takes to get a file? That’s what we’re going to explore here. So, if we’re requesting a file that’s stored on Backblaze’s servers in the US-West region, what does that look like if we are delivering the file to different locations across the continental U.S.? How long does it take?

Building a Latency Test

Today we’re going to focus on network statistics for our US-West location and how the performance profile looks as we travel east and the various components that contribute to the change in translation time.

Below we’re going to compare theoretical best case numbers to the real world. There are three categories in our analysis that contribute to the total time it takes to get a request from a person in Los Angeles, Chicago, or New York, to our servers in the US-West region. Let’s take a look at each one:

1. Ideal Line: Let’s draw an imaginary line between our US-West location and each major city in our testing sample. Then we can calculate the time it takes light to be sent and received as RTT (Round Trip Time) one time between the two points. This number gives us the Ideal Line time, or the time it takes for a light signal to travel between two points in a perfect line, in vacuum conditions, with no obstructions. Hardly how we live, so we have to add a few other data points!
2. Fiber: Fiber optics in the real world have to pass through optical equipment, connect to aerial fiber on telephone poles where ground access is limited, route around pesky obstructions like mountains or municipal services, and sometimes travel along non-ideal paths to reach major connection points where long-haul fiber providers have offices to improve and reamplify the signal. This RTT number is taken from testing services that we have running across the country.
3. Software: This measurement shows the time spent in Software tasks (as opposed to Setup or Download tasks, as defined by Google) that are required to initiate network connections, negotiate mutual settings between sender and receiver, wait for data to start to be received, and encrypt/decrypt messages. We’re also getting this number from our testing services and will explore all the inner workings of the Software components a little later on.
4. Total: The interesting part! Real world RTT for retrieving a sample file from various locations.

Fun fact: You don’t need any monitoring infrastructure in order to take a deeper dive—every Chrome web browser has the ability to show load times for all the elements that are needed to present a website.

Do note that test results may vary based on your ISP connectivity, hardware capabilities, software stack, or improvements Backblaze makes over time.

To show more detailed information, open Chrome:

• Go to Chrome Options > More Tools > Developer Tools 
• Select Network Tab
• Browse to a website to see results sourced from your machine

A deeper dive into this can be found on Google’s developer.chrome.com website.

If you wish to run agent based tests, you can start with Google’s Chromium Project, as it offers a free and open source method to simulate and perform profiling.

Here are the results from just one test we ran:

It’s important, at this stage, to caveat these numbers with a few things. First, they include a decent amount of overhead from being within our (or any) infrastructure, which can be affected by things like your browser, security, and lookup time needed to connect to a server infrastructure. And, if a user is running a different browser, has different layers of security, and so on, those things can affect RTT results. 

Second, they don’t accurately talk about performance without context. Every type of application has its own requirements for what is a “good” or “bad” RTT time, and these numbers can change based on how you store your data; where you store, access, and serve your data; if you use a content delivery network (CDN); and more. As with anything related to performance in cloud storage, your use case determines your cloud storage strategy, not the other way around.

Unpacking the Software Measurement

In addition to the Chrome tools we talk about above, we have access to agents running in various geographical locations across the world that run periodic tests to our services. These agents can simulate client activity and record metrics for us that we use for alerting and trend analysis. Simulating client operations helps alert our operations teams to potential issues, helping us to better support our clients and be more proactive.

With this type of agent based testing, we have greater insight into the network that lets us break down the Software step and observe if any one step in the transfer pipeline is underperforming. We’re not only looking at the entire round trip time of downloading a file, but also including all the browser, security, and lookup time needed to connect to our server infrastructure. And, as always, the biggest addition in time it takes to deliver files is often distance-based latency, or the fact that even with ideal conditions, the further away an end user is, the longer it takes to transport data across networks. 

Unpacking Software Results

The below chart shows how long in milliseconds it takes to get a sample file from our US-West cluster from agents running in different locations across the U.S. and all the Software steps involved.

You can find definitions for all these terms in the Chromium dev docs, but here’s a cheat sheet for our purposes: 

• Queueing: Browser queues up connection.
• DNS Lookup: Resolving the request’s IP address.
• SSL: Time spent negotiating a secure session.
• Initial Connection: TCP handshake.
• Request Sent: This is pretty self explanatory—the request is sent to the server. 
• TTFB (Time to First Byte): Waiting for the first byte of a response. Includes one round trip of latency and the time the server took to prepare the response.
• Content Download: Total amount of time receiving the requested file.

Pie Slices

Let’s zoom in on the Los Angeles and New York tests and group just the Download (Content Download) and all the other Setup items (Queueing, DNS Lookup, SSL, Initial Connection, TTFB) and see if they differ drastically. 

In the Los Angeles test, which is the closest geographical test to the US-West cluster, the total transaction time is 71ms. It takes our storage system 23.8ms to start to send the file, and we’re spending 47ms (66%) of the total time in setup. 

If we go further east to New York, we can see how much more time it takes to retrieve our test file from the West (71ms vs 470ms), but the ratio between download and setup doesn’t differ all that drastically. This is because all of our operations are the same, but we’re spending more sending each file over the network, so it all scales up.

Note that no matter where the client request is coming from, the data center is doing the same amount of work to serve up the file.

Customer Considerations: The Importance of Location in Data Storage

Latency is certainly a factor to consider when you choose where to store your data, especially if you are running extremely time sensitive processes like content delivery—as we’ve noted here and elsewhere, the closer you are to your end user, the greater the speed of delivery. Content delivery networks (CDNs) can offer one way to layer your network capabilities for greater speed of delivery, and Backblaze offers completely free egress through many CDN partners. (This is in addition to our normal amount of free egress, which is up to 3x the data stored and includes the vast majority of use cases.) 

There are other reasons to consider different regions for storage as well, such as compliance and disaster resilience. Our EU datacenter, for instance, helps to support GDPR compliance. Also, if you’re storing data in only one location, you’re more vulnerable to natural disasters. Redundancy is key to a good disaster recovery or business continuity plan, and you want to make sure to consider power regions in that analysis. In short, as with all things in storage optimization, considering your use case is key to balancing performance and cost.

Milliseconds Matter

I started this article talking about Grace Murray Hopper demonstrating nanoseconds with pieces of wire, and we’re concluding what can be considered light years (ha) away from that point. The biggest thing to remember, as a network engineering team, is that even though approximately 600ms from the US-West to the US-East regions seems miniscule, the amount of times we travel that distance very quickly takes us up those orders of magnitude from milliseconds to seconds. And, when you, the user, are choosing where to store your data—knowing that we register audio lag at as little as 10ms—those inconsequential numbers start to get to human relative terms very, very quickly. 

So, when we find that peering shaves a few milliseconds off of a file delivery time, that’s a big, human-sized win. You can see some of the ways we’re optimizing our network in the first article of this series, and the types of tests we’re running above give us good insights—and inspiration—for more, better, and ongoing improvements. We’ll keep sharing what we’re measuring and how we’re improving, and we’re looking forward to seeing you all in the comments.

The post Backblaze Network Stats: Real-World Measurements from the Backblaze US-West Region appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Post Syndicated from Stephanie Doyle original https://www.backblaze.com/blog/data-centers-temperature-and-power/

It’s easy to open a data center, right? All you have to do is connect a bunch of hard drives to power and the internet, find a building, and you’re off to the races.  

Well, not exactly. Building and using one Storage Pod is quite a bit different than managing exabytes of data. As the world has grown more connected, the demand for data centers has grown—and then along comes artificial intelligence (AI), with processing and storage demands that amp up the need even more. 

That, of course, has real-world impacts, and we’re here to chat about why. Today we’re going to talk about power, one of the single biggest costs to running a data center, how it has impacts far beyond a simple utility bill, and what role temperature plays in things.

How Much Power Does a Data Center Use?

There’s no “normal” when it comes to the total amount of power a data center will need, as data centers vary in size. Here are a few figures that can help us get us on the same page about scale: 

• The largest data center market in the world is in Northern Virginia in the United States and it has a 2,552 megawatt capacity. (By the way: 1MW = 1 million watts. For context, one megawatt is enough energy to power about 200 American homes for a year.)
• In 2023, data centers consumed 7.4 gigawatts of power. (1GW = 1 billion watts.) 
• That’s approximately 1–1.3% of total power in the world. Expand that to all IT energy demand, and that number is about 10% of global energy consumption.  

The goal of a data center is to be always online. That means that there are redundant systems of power—so, what comes in from the grid as well as generators and high-tech battery systems like uninterruptible power supplies (UPS)—running 24 hours a day to keep servers storing and processing data and connected to networks. In order to keep all that equipment running well, they need to stay in a healthy temperature (and humidity) range, which sounds much, much simpler than it is.  

Measuring Power Usage

One of the most popular metrics for tracking power efficiency in data centers is power usage effectiveness (PUE), which is the ratio of the total amount of energy used by a data center to the energy delivered to computing equipment. 

Note that this metric divides power usage into two main categories: what you spend keeping devices online (which we’ll call “IT load” for shorthand purposes), and “overhead”, which is largely comprised of the power dedicated to cooling your data center down. 

There are valid criticisms of the metric, including that improvements to IT load will actually make your metric worse: You’re being more efficient about IT power, but your overhead stays the same—so less efficiency even though you’re using less power overall. Still, it gives companies a repeatable way to measure against themselves and others over time, including directly comparing seasons year to year, so it’s a widely adopted metric. 

Calculating your IT load is a relatively predictable number. Manufacturers tell you the wattage of your device (or you can calculate it based on your device’s specs), then you take that number and plan for it being always online. The sum of all your devices running 24 hours a day is your IT power spend. 

Comparatively, doing the same for cooling is a bit more complicated—and it accounts for approximately 40% of power usage. 

What Increases Temperature in a Data Center?

Any time you’re using power, you’re creating heat. So the first thing you consider is always your IT load. You don’t want your servers overtaxed—most folks agree that you want to run at about 80% of capacity to keep things kosher—but you also don’t want to have a bunch of servers sitting around idle when you return to off-peak usage. Even at rest, they’re still consuming power. 

So, the methodology around temperature mitigation always starts at power reduction—which means that growth, IT efficiencies, right-sizing for your capacity, and even device provisioning are an inextricable part of the conversation. And, you create more heat when you’re asking an electrical component to work harder—so, more processing for things like AI tasks means more power and more heat. 

And, there are a number of other things that can compound or create heat: the types of drives or processors in the servers, the layout of the servers within the data center, people, lights, and the ambient temperature just on the other side of the data center walls. 

Brief reminder that servers look like this: 

When you’re building a server, fundamentally what you’re doing is shoving a bunch of electrical components in a box. Yes, there are design choices about those boxes that help mitigate temperature, but just like a smaller room heating up more quickly than a warehouse, you are containing and concentrating a heat source.

We humans generate heat and need lights to see, so the folks who work in data centers have to be taken into account when considering the overall temperature of the data center. Check out these formulas or this nifty calculator for rough numbers (with the caveat that you should always consult an expert and monitor your systems when you’re talking about real data centers):

• Heat produced by people = maximum number of people in the facility at one time x 100 
• Heat output of lighting = 2.0 x floor area in square feet or 21.53 x floor area in square meters

Also, your data center exists in the real world, and we haven’t (yet) learned to control the weather—so you also have to factor in fighting the external temperature when you’re bringing things back to ideal conditions. That’s led to a movement towards building data centers in new locations. It’s important to note that there are other reasons you might not want to move, however, including network infrastructure.

Accounting for people and the real world also means that there will be peak usage times, which is to say that even in a global economy, there are times when more people are asking to use their data (and their dryers, so if you’re reliant on a consumer power grid, you’ll also see the price of power spike). Aside from the cost, more people using their data = more processing = more power.

How Is Temperature Mitigated in Data Centers?

Cooling down your data center with fans, air conditioners, and water also uses power (and generates heat). Different methods of cooling use different amounts of power—water cooling in server doors vs. traditional high-capacity air conditioners, for example. 

Talking about real numbers here gets a bit tricky. Data centers aren’t a standard size. As data centers get larger, the environment gets more complex, expanding the potential types of problems, while also increasing the net benefit of changes that might not have a visible impact in smaller data centers. It’s like any economy of scale: The field of “what is possible” is wider; rewards are bigger, and the relationship between change vs. impact is not linear. Studies have shown that creating larger data centers creates all sorts of benefits (which is an article in and of itself), and one of those specific benefits is greater power efficiency. 

Most folks talk about the impact of different cooling technologies in a comparative way, i.e., we saw a 30% reduction in heat. And, many of the methods of mitigating temperature are about preventing the need to use power in the first place. For that reason, it’s arguably more useful to think about the total power usage of the system. In that context, it’s useful to know that a single fan takes x amount of power and produces x amount of heat, but it’s more useful to think of them in relation to the net change on the overall temperature bottom line. With that in mind, let’s talk about some tactics data centers use to reduce temperature. 

Customizing and Monitoring the Facility 

One of the best ways to keep temperature regulated in your data center is to never let it get hotter than it needs to be in the first place, and every choice you make contributes to that overall total. For example, when you’re talking about adding or removing servers from your pool, that reduces your IT power consumption and affects temperature. 

There are a whole host of things that come down to data centers being a purpose-built space, and most of them have to do with ensuring healthy airflow based on the system you’ve designed to move hot air out and cold air in. 

No matter what tactics you’re using, monitoring your data center environment is essential to keeping your system healthy. Some devices in your environment will come with internal indicators, like SMART stats on drives, and, of course, folks also set up sensors that connect to a central monitoring system. Even if you’ve designed a “perfect” system in theory, things change over time, whether you’re accounting for adding new capacity or just dealing with good old entropy. 

Here’s a non-inclusive list of some of ways data centers customize their environments: 

• Raised Floors: This allows airflow or liquid cooling under the server rack in addition to the top, bottom, and sides. 
• Containment, or Hot and Cold Rows: The strategy here is to keep the hot side of your servers facing each other and the cold parts facing outward. That means that you can create a cyclical air flow with the exhaust strategically pulling hot air out of hot space, cooling it, then pushing the cold air over the servers.  
• Calibrated Vector Cooling: Basically, concentrated active cooling measures in areas you know are going to be hotter. This allows you to use fewer resources by cooling at the source of the heat instead of generally cooling the room. 
• Cable Management: Keeping cords organized isn’t just pretty, it also makes sure you’re not restricting airflow.  
• Blanking Panels: This is a fancy way of saying that you should plug up the holes between devices.

Source.

Air vs. Liquid-Based Cooling

Why not both? Most data centers end up using a combination of air and water based cooling at different points in the overall environment. And, other liquids have led to some very exciting innovations. Let’s go into a bit more detail. 

Air-Based Cooling

Air based cooling is all about understanding air flow and using that knowledge to extract hot air and move cold air over your servers.  

Air-based cooling is good up to a certain temperature threshold—about 20 kilowatts (kW) per rack. Newer hardware can easily reach 30kw or higher, and high processing workloads can take that even higher. That said, air-based cooling has benefitted by becoming more targeted, and people talk about building strategies based on room, row, or rack. 

Water-Based Cooling

From here, it’s actually a pretty easy jump into water-based cooling. Water and other liquids are much better at transferring heat than air, about 50 to 1,000 times more, depending on the liquid you’re talking about. And, lots of traditional “air” cooling methods run warm air through a compressor (like in an air conditioner), which stores cold water and cools off the air, recirculating it into the data center. So, one fairly direct combination of this is the evaporative cooling tower: 

Obviously water and electricity don’t naturally blend well, and one of the main concerns of using this method is leakage. Over time, folks have come up with some good, safe methods, designed around effectively containing the liquid. This increases the up-front cost, but has big payoffs for temperature mitigation. You find this methodology in rear door heat exchangers, which create a heat exchanger in—you guessed it—the rear door of a server, and direct-to-chip cooling, which contains the liquid into a plate, then embeds that plate directly in the hardware component. 

So, we’ve got a piece of hardware, a server rack—the next step is the full data center turning itself into a heat exchange, and that’s when you get Nautilus—a data center built over a body of water. 

(Other) Liquid-Based Cooling, or Immersion Cooling

With the same sort of daring thought process of the people who said, “I bet we can fly if we jump off this cliff with some wings,” somewhere along the way, someone said, “It would cool down a lot faster if we just dunked it in liquid.” Liquid-based cooling utilizes dielectric liquids, which can safely come in contact with electrical components. Single phase immersion uses fluids that don’t boil or undergo a phase change (think: similar to an oil), while two phase immersion uses liquids that boil at low temperatures, which releases heat by converting to a gas. 

You’ll see components being cooled this way either in enclosed chassis, which can be used in rack-style environments, in open baths, which require specialized equipment, or a hybrid approach. 

How Necessary Is This?

Let’s bring it back: we’re talking about all those technologies efficiently removing heat from a system because hotter environments break devices, which leads to downtime. And, we want to use efficient methods to remove heat because it means we can ask our devices to work harder without having to spend electricity to do it. 

Recently, folks have started to question exactly how cool data centers need to be. Even allowing a few more degrees of tolerance can make a huge difference to how much time and money you spend on cooling. Whether it has longer term effects on the device performance is questionable—manufacturers are fairly opaque about data around how these standards are set, though exceeding recommended temperatures can have other impacts, like voiding device warranties.

Power, Infrastructure, Growth, and Sustainability

But the simple question of “Is it necessary?” is definitely answered “yes,” because power isn’t infinite. And, all this matters because improving power usage has a direct impact on both cost and long-term sustainability. According to a recent MIT article, the data centers now have a greater carbon footprint than the airline industry, and a single data center can consume the same amount of energy as 50,000 homes. 

Let’s contextualize that last number, because it’s a tad controversial. The MIT research paper in question was published in 2022, and that last number is cited from “A Prehistory of the Cloud” by Tung-Hui Hu, published in 2006. Beyond just the sheer growth in the industry since 2006, data centers are notoriously reticent about publishing specific numbers when it comes to these metrics—Google didn’t release numbers until 2011, and they were founded in 1998. 

Based on our 1MW = 200 homes metric the number from the MIT article number represents 250MW. One of the largest data centers in the world has a 650MW capacity. So, while you can take that MIT number with a grain of salt, you should also pay attention to market reports like this one—the aggregate numbers clearly show that power availability and consumption is one of the biggest concerns for future growth. 

So, we have less-than-ideal reporting and numbers, and well-understood environmental impacts of creating electricity, and that brings us to the complicated relationship between the two factors. Costs of power have gone up significantly, and are fairly volatile when you’re talking about non-renewable energy sources. International agencies report that renewable energy sources are now the cheapest form of energy worldwide, but the challenge is integrating renewables into existing grids. While the U.S. power grid is reliable (and the U.S. accounts for half of the world’s hyperscale data center capacity), the Energy Department recently announced that the network of transmission lines may need to expand by more than two-thirds to carry that data nationwide—and invested $1.3 billion to make that happen.

What’s Next?

It’s easy to say, “It’s important that data centers stay online,” as we sort of glossed over above, but the true importance becomes clear when you consider what that data does—it keeps planes in the air, hospitals online, and so many other vital functions. Downtime is not an option, which leads us full circle to our introduction.   

We (that is, we, humans) are only going to build more data centers. Incremental savings in power have high impact—just take a look at Google’s demand response initiative, which “shift[s] compute tasks and their associated energy consumption to the times and places where carbon-free energy is available on the grid.” 

It’s definitely out of scope for this article to talk about the efficiencies of different types of energy sources. That kind of inefficiency doesn’t directly impact a data center, but it certainly has downstream effects in power availability—and it’s probably one reason why Microsoft, considering both its growth in power need and those realities, decided to set up a team dedicated to building nuclear power plants to directly power some of their data centers and then dropped $650 million to acquire a nuclear-powered data center campus. 

Which is all to say: this is an exciting time for innovation in the cloud, and many of the opportunities are happening below the surface, so to speak. Understanding how the fundamental principles of physics and compute work—now more than ever—is a great place to start thinking about what the future holds and how it will impact our world, technologically, environmentally, and otherwise. And, data centers sit at the center of that “hot” debate. 

The post Data Centers, Temperature, and Power appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Post Syndicated from Tonya Comer original https://www.backblaze.com/blog/how-much-storage-do-i-need-to-backup-my-video-surveillance-footage/

We all have things we want to protect, and if you’re responsible for physically or virtually protecting a business from all types of threats, you probably have some kind of system in place to monitor your physical space. If you’ve ever dealt with video surveillance footage, you know managing it can be a monumental task. Ensuring the safety and security of monitored spaces relies on collecting, storing, and analyzing large amounts of data from cameras, sensors, and other devices. The requirements to back up and retain footage to support investigations are only getting more stringent. Anyone dealing with surveillance data, whether in business or security, needs to ensure that surveillance data is not only backed up, but also protected and accessible. 

In this post, we’ll talk through why you should back up surveillance footage, the factors you should consider to understand how much storage you need, and how you can use cloud storage in your video surveillance backup strategy. 

The Importance of Backing Up Video Surveillance Footage

Backup storage plays a critical part in maintaining the security of video surveillance footage. Here’s why it’s so important:

1. Risk Reduction: Without backup storage, surveillance system data stored on a single hard drive or storage device is susceptible to crashes, corruption, or theft. Having a redundant copy ensures that critical footage is not lost in case of system failures or data corruption.
2. Fast Recovery: Video surveillance systems rely on continuous recording to monitor and record all activities. In the event of system failures, backup storage enables swift recovery, minimizing downtime and ensuring uninterrupted surveillance.
3. Compliance and Legal Requirements: Many industries, including security, have legal obligations to retain surveillance footage for a specified duration. Backup storage ensures compliance with these requirements and provides evidence when needed.
4. Verification and Recall: Backup recordings allow you to verify actions, recall events, and keep track of activities. Having access to historical footage is valuable for potential investigations and future decision making.

Each piece of information about video surveillance requirements will affect how much space your video files take up and, consequently, your storage requirements. Let’s walk through each of these general requirements so you don’t end up underestimating how much backup storage you’ll need.

Video Surveillance Storage Considerations

When you’re implementing a backup strategy for video surveillance, there are several factors that can impact your choices. The number and resolution of cameras, frame rates, retention periods, and more can influence how you design your backup storage system. Consider the following factors when thinking about how much storage you’ll need for your video surveillance footage:

• Placement and Coverage: When it comes to video surveillance camera placement, strategic positioning is crucial for optimal security and regulatory compliance. Consider ground floor doors and windows, main stairs or hallways, common areas, and driveways. Install cameras both inside and outside entry points. Generally, the wider the field of view, the fewer cameras you’ll likely need overall. The FBI provides extensive recommendations for setting up your surveillance system properly.
• Resolution: The resolution determines the clarity of the video footage, which is measured in the number of pixels (px). A higher resolution means more pixels and a sharper image. While there’s no universal minimum for admissible surveillance footage, a resolution of 480 x 640 px is recommended. However, mandated minimums can differ based on local regulations and specific use cases. Note that some regulations may not provide a minimum resolution requirement, and some minimum requirements may not meet the intended purpose of surveillance. Often, it’s better to go with a camera that can record at a higher resolution than the mandated minimum.
• Frame Rate: All videos are made up of individual frames. A higher frame rate—measured in frames per second (FPS)—means a smoother, less clunky image. This is because there are more frames being packed into each second. Like your cameras’ resolution, there are no universal requirements specified by regulations. However, it’s better to go with a camera that can record at a higher FPS so that you have more images to choose from if there’s ever an open investigation.
• Recording Length: Surveillance cameras are required to run all day, every day, which requires a lot of storage. To help reduce the instance of storing videos that aren’t of interest, some cameras can come with artificial intelligence (AI) tools that will only record footage when it identifies something of interest, such as movement or a vehicle. But if you’re protecting a business with heavy activity, this use of AI may be moot. 
• Retention Length: Video surveillance retention requirements can vary significantly based on local, state, and federal regulations. These laws dictate how long companies must store their video surveillance footage. For example, medical marijuana dispensaries in Ohio require 24/7 security video to be retained for a minimum of six months, and the footage must be made available to the licensing board upon request. The required length can be prolonged even further if a piece of footage is required for an ongoing investigation. Additionally, backing up your video footage (i.e., saving a copy of it in another area) is different from archiving it for long-term use. You’ll want to be sure that you select a storage system that helps you meet those requirements—more on that later.

Each point on this list affects how much storage capacity you need. More cameras mean more footage being generated, which means more video files. Additionally, a higher resolution and frame rate mean larger file sizes. Multiply this by the round-the-clock operation of surveillance cameras and the required retention length, and you’ll likely have more video data than you know what to do with.

Scoping Video Surveillance Storage: An Example

To illustrate how much footage you can expect to collect, it can be helpful to see an example of how a given business’s video surveillance may operate. Note that this example may not apply to you specifically. You should review your local area’s regulations and consult with an industry professional to make sure you are compliant. 

Let’s say that you need to install surveillance cameras for a bank. Customers enter through the lobby and wait for the next available bank teller to assist them at a teller station. No items are put on display, only the exchange of paper and cash between the teller and the customer. Only authorized employees are allowed within the teller area. After customers complete their transactions, they walk back through the lobby area and exit via the building’s front entry.

As an estimate, let’s say you need at least 10 cameras around your building: one for the entrance; another for the lobby; eight more to cover the general back area, including the door to the teller terminals, the teller terminals themselves, the door to the safe, and inside of the safe; and, of course, one for the room where the surveillance equipment is housed. You may need more than 10 to cover the exterior of your building plus your ATM and drive through banking, but for the sake of an example, we’ll leave it at 10.

Now, suppose all your cameras record at 1080p resolution (1920 x 1080 px), 15 FPS, and a color depth of 14 bits (basically, how many colors the camera captures). For one 24 hour recording on one camera, you’re looking at 4.703 terabytes (TB). Over 30 days of storage, this can grow to 141.1TB. In other words, if the average person today needs a 2TB hard disk for their PC, it will take more than 70 PCs to hold all the information from just one camera.  

How Cloud Storage Can Help Back Up Surveillance Footage

Backing up surveillance footage is essential for ensuring data security and accountability. It provides a reliable record of events, aids in investigations, and helps prevent wrongdoing by acting as a deterrent. But the right backup strategy is key to preserving your footage.

The 3-2-1 backup strategy is an accepted foundational structure that recommends keeping three copies of all important data (one primary copy and two backup copies) on two different media types (to diversify risk) and storing at least one copy off-site. With surveillance data utilizing high-capacity data storage systems, adhering to the 3-2-1 rule is important in order to access footage in case of an investigation. The 3-2-1 rule mitigates single points of failure, enhances data availability, and protects against corruption. By adhering to this rule, you increase the resilience of your surveillance footage, making it easier to recover even in unexpected events or disasters.

Having an on-site backup copy is a great start for the 3-2-1 backup strategy, but having an off-site backup is a key component in having a complete backup strategy. Having a backup copy in the cloud provides an easy to maintain, reliable off-site copy, safeguarding against a host of potential data losses including:

• Natural Disasters: If your business is harmed by a natural disaster, the devices you use for your primary storage or on-site backup may be damaged, resulting in a loss of data.
• Tampering and Theft: Even if someone doesn’t try to steal or manipulate your surveillance footage, an employee can still move, change, or delete files accidentally. You’ll need to safeguard footage with proper security protocols, such as authorization codes, data immutability, and encryption keys. These protocols may require constant, professional, and IT administration and maintenance that are often automatically built into the cloud.
• Lack of Backup and Archive Protocols: Unless your primary storage source uses   specialized software to automatically save copies of your footage or move them to long-term storage, any of your data may be lost.

The cloud has transformed backup strategies and made it easy to ensure the integrity of large data sets, like surveillance footage. Here’s how the cloud helps achieve the 3-2-1 back strategy affordably:

•  Scalability: With the cloud, your backup storage space is no longer limited to what servers you can afford. The cloud provider will continue to build and deploy new servers to keep up with customer demand, meaning you can simply rent out the storage space and pay for more as needed.
• Reliability: Most cloud providers share information on their durability and reliability and are heavily invested in building systems and processes to mitigate the impact of failures. Their systems are built to be fault-tolerant. 
• Security: Cloud providers protect data you store with them with enterprise-grade security measures and offer features like access controls and encryption to allow users the ability to better protect their data.
• Affordability: Cloud storage helps you use your storage budgets effectively by not paying to provision and maintain physical off-site backup locations yourself.
• Disaster Recovery: If unexpected disasters occur, such as natural disasters, theft, or hardware failure, you’ll know exactly where your data lives in the cloud and how to restore it so you can get back up and running quickly.
• Compliance: By adhering to a set of standards and regulations cloud solutions meet compliance requirements to ensure data stored and managed in the cloud is protected and used responsibly.

Protect The Footage You Invested In to Protect Yourself

No matter the size, operation, or location of your business, it’s critical to remain compliant with all industry laws and regulations—especially when it comes to surveillance. Protect your business by partnering with a cloud provider that understands your unique business requirements, offering scalable, reliable, and secure services at a fraction of the cost compared with other platforms.

As a leading specialized cloud provider, Backblaze B2 Cloud Storage can secure your surveillance footage for both primary backup and long-term protection. B2 offers a range of security options—from encryption to Object Lock to Cloud Replication and access management controls—to help you protect your data and achieve industry compliance. Learn more about Backblaze B2 for surveillance data or contact our Sales Team today.

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