Post Syndicated from Kari Rivas original https://www.backblaze.com/blog/the-fine-print-how-minimum-data-retention-fees-affect-cloud-costs/

You probably won’t notice a little asterisked footnote tucked at the bottom of the page the first time you read through a cloud storage vendor’s pricing tables. You probably won’t notice it the second or third time either. But you’ll definitely notice it when your bill comes in with charges for data you thought you deleted weeks ago. 

That footnote explains an often overlooked challenge to your budget: minimum data retention periods. These policies, used by cloud providers like AWS, Azure, Google Cloud, and Wasabi, can lead to unexpected cost increases and complicated data management strategies. 

Today, I’m breaking down cloud storage retention minimums and common scenarios where they directly impact storage budgets and data management policies. 

What are minimum data retention periods?

Retention minimums specify the minimum amount of time that data must be stored before it can be deleted, overwritten, or moved to a different storage tier without incurring additional charges. 

Cloud storage providers with multiple tiers like AWS or Google Cloud use minimum retention policies to ensure that customers cannot frequently move data between storage tiers to exploit lower-cost storage classes for short-term storage. For cloud providers that have a single class of storage, these policies allow providers to stabilize their resource usage and maintain predictable pricing structures.

Minimum retention periods can vary significantly between providers, and even between different storage tiers offered by the same provider. For example, AWS S3 Standard has no minimum retention period, but S3 Standard-IA has a 30 day minimum, Glacier has a 90 day minimum, and Deep Archive has a 180 day minimum.

Despite their significance, information about these retention periods is often buried in the fine print of service agreements or technical documentation. 

What are delete fees?

Delete fees are a direct consequence of deleting or moving files before the retention minimum is met. Cloud providers charge these fees to ensure that the infrastructure allocated for the data is compensated for the resources it would have otherwise used during the retention period. This fee is typically prorated, representing the remaining days in the retention period that the data was meant to occupy in a storage class. 

The terms “delete fees,” “minimum storage duration,” and “minimum retention fees” all refer to a similar policy.

How are delete fees incurred?

Early deletion fees can be triggered by various actions, not just the obvious deletion of files. Some examples include:

• Moving data from a higher-cost tier to a lower-cost tier before the minimum retention period has been met: This scenario often catches organizations off guard when they attempt to optimize costs by transferring infrequently accessed data to a cheaper storage class.
• Overwriting existing files: When a file is overwritten, the cloud provider typically treats this as a delete operation followed by a new write operation. If the original file hasn’t met its minimum retention period, the organization may be charged for the remaining time, even though they’re still using the same amount of storage space.

• Implementation of automated lifecycle policies: Many organizations set up rules to automatically move or delete data based on its age or access patterns. However, if these policies don’t account for minimum retention periods, they can inadvertently trigger early delete fees on a large scale.
• Renaming files or folders: Even seemingly benign actions like renaming files or folders can sometimes be interpreted as delete-and-rewrite operations by certain cloud storage systems, potentially triggering these fees. 

Additionally, in multi-user or multi-team environments, lack of communication about retention policies can lead to unexpected charges. One team might delete or move data without realizing the financial implications for the entire organization. 

The financial impact of minimum data retention periods

Minimum data retention periods, particularly in cold storage tiers, can have significant impacts on IT budgets. What may have seemed like a cost-saving storage tier can actually increase expenses when operations require frequent deletions or movements of data before the minimum retention period is over. But even in hot storage, these policies can unexpectedly inflate overall costs.

To illustrate the real-world impact of retention minimums, let’s examine a few common scenarios:

1. Backup strategy

Let’s say you have a 30 day backup strategy for your critical infrastructure, and you opt for Wasabi object storage to save costs vs. AWS. You plan to keep a month’s worth of backups in the cloud and will then replace them with the newer backups.

Wasabi’s minimum retention policy is 90 days for its Pay as You Go storage (and 30 days for its Reserved Capacity Storage). 

You store an initial 50TB of backups in Wasabi on Day 1. On Day 31, the older backup is deleted and replaced with the newer backup. So, you incur costs for 30 days of Timed Active Storage (50TB) and 60 days of Timed Deleted Storage (50TB). These charges are incurred every time the backup is replaced.

With Wasabi’s Pay as You Go storage, your monthly bill will look like this:

50TB x $6.99/TB/month x 3 = $1048.50

We multiply by 3 because the 90 day minimum retention policy equals three months’ time. One of those you’ve actually stored for, and the other two are because you’ve replaced your backups with the new ones.  

Compare this to Backblaze B2 Cloud Storage, which has no minimum retention policy and costs $6 per TB/month for its Pay as You Go storage:

50TB x $6/TB/month = $300

The minimum retention policy effectively triples the anticipated storage expenses. When scaled across multiple backup sets or extended periods, the impact on the IT budget can be substantial.

2. Application storage

In application storage use cases, retention minimums can impact cloud spend significantly when the data has a short lifecycle. Applications with high transaction volumes—such as e-commerce, user-generated content applications, or surveillance platforms—frequently upload and delete as part of their daily operations. 

For example, most video surveillance platforms may only need 30 days of history for footage that’s been uploaded and processed, so something like a 90-day retention period doesn’t make financial or operational sense. E-commerce customers can also be affected; these businesses have users that frequently upload and delete content to manage storefronts, creating unpredictable data usage patterns. In these cases, you are at the mercy of your end users—if users churn through files quickly you will pay the retention penalties.

3. Video production

Retention minimums also affect video production workflows particularly when you need to make revisions once a project has been archived in cold storage—a common workflow many studios and broadcasting agencies use to get more affordable storage rates for seldom-accessed data. 

Whether due to last minute changes in branding, edits to visuals, or adjustments to sound, the project needs to be pulled from storage for further modification. Because the files were moved to colder storage under a 90 day retention policy, accessing and modifying them before that period ends can trigger significant early delete fees.

If you routinely archive files immediately after a project completes anticipating that no further changes will be required, these early delete fees can add up quickly.

The hidden complexities of minimum data retention periods

Retention minimums can significantly impact your bottom line. These policies, often buried in the fine print, can lead to unexpected costs and complicate data management strategies across various industries.

Understanding the nuances of minimum data retention periods and their associated costs is crucial for developing an effective and economically sound cloud storage strategy. It enables organizations to make more informed decisions, avoid unexpected expenses, and better align their storage choices with their specific data management needs and budget constraints.

The post The Fine Print: How Minimum Data Retention Fees Affect Cloud Costs appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Post Syndicated from Bala Krishna Gangisetty original https://www.backblaze.com/blog/using-b2-event-notifications/

No one likes being left out in the cold, least of all your data. With Backblaze B2 Event Notifications—now generally available—you can receive real-time notifications about object changes. That means that you can build more responsive and automated workflows across best-of-breed cloud platforms, saving time and money and improving your end users’ experiences. And, you can be alerted to changes in your data that may speed time to action.

Here’s how it works: With Backblaze B2 Event Notifications, any data changes within B2 Cloud Storage—like uploads, updates, or deletions—can automatically trigger actions in a workflow, including transcoding video files, spooling up data analytics, delivering finished assets to end users, and many others. Importantly, unlike many other solutions currently available, Backblaze’s service doesn’t lock you into one platform or require you to use legacy tools from AWS.

So, to businesses that want to create an automated workflow that combines different compute, content delivery networks (CDN), data analytics, and whatever other cloud service: Now you can, with the bonus of cloud storage at a fifth of the rates of other solutions and free egress.

Key capabilities

• Flexible implementation: Event Notifications are sent as HTTP POST requests to the desired service or endpoint within your infrastructure or any other cloud service. This flexibility ensures seamless integration with your existing workflows. For instance, your endpoint could be Fastly Compute, AWS Lambda, Azure Functions, or Google Cloud Functions, etc.
• Event categories: Specify the types of events you want to be notified about, such as when files are uploaded and deleted. This allows you to receive notifications tailored to your specific needs. For instance, you have the flexibility to specify different methods of object creation, such as copying, uploading, or multipart replication, to trigger event notifications. You can also manage Event Notification rules through UI or API.
• Filter by prefix: Define prefixes to filter events, enabling you to narrow down notifications to specific sets of objects or directories within your storage on Backblaze B2. For instance, if your bucket contains audio, video, and text files organized into separate prefixes, you can specify the prefix for audio files in order to receive Event Notifications exclusively for audio files.
• Custom headers: Include personalized HTTP headers in your Event Notifications to provide additional authentication or contextual information when communicating with your target endpoint. For example, you can use these headers to add necessary authentication tokens or API keys for your target endpoint, or include any extra metadata related to the payload to offer contextual information to your webhook endpoint, and more.
• Signed notification messages: You can configure outgoing messages to be signed by the Event Notifications service, allowing you to validate signatures and verify that each message was generated by Backblaze B2 and not tampered with in transit.
• Test rule functionality: Validate the functionality of your target endpoint by testing Event Notifications before deploying them into production. This allows you to ensure that your integration with your target endpoint is set up correctly and functioning as expected.
• Retries: Event Notifications are automatically re-sent if the initial delivery attempt fails. This feature increases the reliability of Event Notifications by ensuring that temporary issues do not result in missed events, thus maintaining the integrity of your event-driven workflows.
• Delivery: Event Notifications are designed for the at-least-once delivery guarantee to ensure Event Notifications are delivered reliably, even in the presence of network or system failures.

Versatile use cases

This past April, we announced Event Notifications in preview, and folks have put Event Notifications to work in some incredible ways. Today, we’re sharing some of the key use cases that came out of the preview to simplify your own workflows so you can focus on extracting insights from your data, rather than managing the logistics of data processing.

Automated media processing

Video transcoding: Many customers use Event Notifications to automate their video transcoding workflows. When a new video is uploaded to a Backblaze B2 Bucket, an Event Notification can trigger a transcoding process to generate all videos in the desired format. 

Image processing: Similarly, customers also use Event Notifications to set up automated image processing pipelines, such as generating thumbnails or applying filters when new images are added to a Backblaze B2 Bucket.

Media processing is not limited to video transcoding or image processing. It can be extended to any other media processing workflow, minimizing the number of steps in the workflow.

Backup monitoring

Customers can receive notifications when backups are successfully uploaded to a Backblaze B2 Bucket with Event Notifications, providing peace of mind and ensuring data protection. Whether you want to track your nightly or monthly backups, you can get a notification when they are completed.

Presigned URL monitoring

Using a presigned URL is a standard way to share a file without giving the full access to your Backblaze B2 Bucket. Customers are using Event Notifications to know when their clients upload files via presigned URLs to Backblaze B2. They can get a callback to confirm that the upload is complete.

Security and access control

Unauthorized access detection: Customers are using Event Notifications to track access to highly confidential video files and report back to their clients as needed. Event Notifications help them detect any unauthorized access and take immediate action.

Audit trails: Some customers are using Event Notifications to create a detailed audit log of supported bucket activities through Event Notifications, which is useful for their compliance and security purposes.

Anomaly/malware detection: Event Notifications can strengthen security by detecting unusual access patterns, like malware that deletes or overwrites backups, by getting notifications of changes to Backblaze B2 Buckets.

Integration with external systems

Database synchronization: Customers use Event Notifications to keep databases in sync with the state of their Backblaze B2 Buckets. It’s critical to ensure data consistency across systems as their applications run on the databases.

Document management system: Some customers use Event Notifications with a workflow system to track document revisions, uploads, and deletes, or to notify team members when specific documents are uploaded or deleted.

Analytics and reporting

Performance analytics: Some customers use Event Notifications to monitor their backup performance and completion times, helping to optimize their data management strategies.

Usage tracking: Event Notifications can help track storage consumption by individual users or projects, facilitating better resource management and cost allocation.

These are just a few of the use cases our preview customers shared with us, and the sky is truly the limit for ways Event Notifications can empower you to simplify and streamline your workflows. 

Ready to get started?

For existing customers working with a Backblaze account manager, Event Notifications is enabled for you today. If you need assistance, your account manager is happy to help.

For existing customers who are not currently working with an account manager, please contact our Support team to request access.

New to Backblaze? Contact our Sales team to learn more about how Event Notifications can benefit your business and how to get started.

Once Event Notifications is enabled on your account, log in to your Backblaze B2 account, navigate to the Buckets page, and click on the Event Notifications section. From there, you can set up notification rules for the events you want to track or configure notifications through our API. 

For detailed instructions and best practices, check out the Event Notifications documentation.

What’s next?

Please do share how you’re leveraging Event Notifications to build more efficient, automated, and responsive workflows so that other organizations and developers can benefit from what you find. If you have any questions or feedback, please don’t hesitate to reach out to us.

The post Backblaze B2 Event Notifications Now Generally Available appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Post Syndicated from Kari Rivas original https://www.backblaze.com/blog/three-surprising-factors-that-affect-cloud-performance/

When you think about cloud performance, metrics like latency and throughput are probably the first things that come to mind. We covered those metrics pretty extensively here and here. So, today, I’m walking through some factors that affect cloud performance that may not get talked about as often, including:

• The size of your files.
• The number of parts you upload or download.
• Block (part) size.

These factors may not be “surprising” per se especially if you remember the pain of trying to download The Matrix over dial up. But they are all things that you should consider (and that you have more control over) when thinking about cloud performance overall. 

Let’s dig in.

1. The size of your files

This one is pretty obvious. Larger files take longer because they require more data to be transferred. If you have a 10Mbps upload connection, a 1GB file will take approximately 800 seconds (13 minutes and 20 seconds) to upload, whereas a 100MB file will take about 80 seconds (a minute and 20 seconds). Most enterprise-grade internet connections offer higher upload speeds, but 10Mbps makes the math approachable for the sake of argument.  

Small files—that is, those less than 5GB—can be uploaded in a single API call. (Note: this can vary based on cloud storage provider and configuration.) Larger files up to 10TB can be uploaded as “parts” in multiple API calls. Each part has to be a minimum of 5MB and a maximum of 5GB. 

You’ll notice that there is quite an overlap here! For uploading files between 5MB and 5GB, is it better to upload them in a single API call, or split them into parts? What is the optimum part size? For backup applications, which typically split all data into equally sized blocks, storing each block as a file, what is the optimum block size? As with many questions, the answer is: it depends.

2. The number of parts you upload or download

Each API call incurs a more-or-less fixed overhead due to latency. For a 1GB file, assuming a single thread of execution, uploading all 1GB in a single API call will be faster than 10 API calls each uploading a 100MB part, since those additional nine API calls each incur some latency overhead. So, bigger is better, right?

3. Block (part) size

Not necessarily, and that brings us to part size. Multi-threading, as mentioned above, affords us the opportunity to upload multiple parts simultaneously, which improves performance—but there are trade-offs. Typically, each part must be stored in memory as it is uploaded, so more threads means more memory consumption. If the number of threads multiplied by the part size exceeds available memory, then either the application will fail with an out of memory error, or data will be swapped to disk, reducing performance.

Downloading data offers even more flexibility, since applications can specify any portion of the file to download in each API call. Whether uploading or downloading, there is a maximum number of threads that will drive throughput to consume all of the available bandwidth. Exceeding this maximum will consume more memory, but provide no performance benefit. 

So, what to do to get the best performance possible for your use case? 

Simple: Customize your settings

Most backup and file transfer tools allow you to configure the number of threads and the amount of data to be transferred per API call, whether that’s block size or part size. If you are writing your own application, you should allow for these parameters to be configured. When it comes to deployment, some experimentation may be required to achieve maximum throughput given available memory.

The big takeaway: When it comes to cloud performance, the metrics you need to care about and the performance you actually need are highly dependent on your use case, your own infrastructure, your workload, and all the network connections between your infrastructure and the cloud provider as well. So, when you’re deciding how to store and use your data, it’s worth taking some extra time to consider the above factors for optimum performance. 

The post Three Surprising Factors that Affect Cloud Performance appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Post Syndicated from David Johnson original https://www.backblaze.com/blog/calculate-cost-cloud-storage/

With spending on public cloud services expected to double by 2028, many businesses are looking for ways to cut cloud costs—or at least gain predictability in their spend. Forecasting cloud storage costs should be straightforward once you know what to look for.

Here are five tips you can use when doing your due diligence on the cloud storage vendors you are considering. The goal is to create a cloud storage forecast that you can rely on each and every month.

Tip 1: Navigate tiered pricing structures carefully

Many cloud providers still use tiered pricing structures, which can be misleading if not carefully understood. For example:

AWS S3 Storage Pricing Example

For this post, we’re comparing with hypothetical data stored in AWS S3’s U.S. East Region (N. Virginia) using pricing available at the time of publishing. Note that many factors may affect your final price, including selecting a different region, choosing a different storage tier, etc.

• First 50 TB/month = $0.023 per GB
• Next 450 TB/month = $0.022 per GB
• Over 500 TB/month = $0.021 per GB

In order to receive lower pricing, you have to reach a specific amount of data stored. But, the lower rate only applies to data above the threshold for that tier. In other words, you don’t get a discount on the cumulative amount—each pricing tier is reflected in the data you’re storing. 

The mistake sometimes made is estimating your entire storage cost based on the level for the total data stored. For example, if you had 600TB of storage, you could wrongly calculate as follows:

600,000GB x $0.021 = $12,600/month

When, in fact, you should do the following:

(50,000GB x $0.023) + (450,000GB x $0.022) + (100,000GB x $0.021) = $13,150/month

That was just for storage. Make sure you consider the tiered pricing tables for data retrieval, and API transactions as well.

Tip 2: Don’t choose the wrong storage class

Many cloud providers, especially hyperscalers, now offer a wider array of storage classes than ever before. The idea is that you can trade service capabilities for lower costs. If you don’t need immediate access to your files or don’t want data replication or 11 nines of durability, you can choose to downgrade your service and gain cost savings. The biggest problem with this method is that you have to know what you are going to do with your data to pick the right service—as well as correctly anticipate future business needs—because mistakes can get very expensive. For example:

• You choose a low cost, cold storage tier that takes hours or days to restore your data. What can go wrong? You need some files back immediately (if, for example, your backups are corrupted by ransomware) and you end up paying 10-20 times the cost to expedite your restore.
• You choose one storage class and decide you want to upload some data to a compute-based application or to another region—features not part of your current service. The good news? You can usually move the data. The bad news? Even if you’re transferring within the same cloud storage company’s infrastructure, you’re often charged a transfer fee to move the data because you didn’t choose the right storage class when you started. These fees often eradicate any “savings” you had gotten from the lower priced tier.

Basically, if your needs change as they pertain to the data you have stored, you will pay more than you expect to get your data where you need it to be.

Tip 3: Don’t pay for deleted (or modified) files

Some cloud storage companies have a minimum amount of time you are charged for storage for each file uploaded. Typically this minimum period is between 30 and 90 days. You are charged even if you delete the file before the minimum period. For example (assuming a 90 day minimum period), if you upload a file today and delete the file tomorrow, you still have to pay for storing that deleted file for the next 88 days.

This “feature” often extends to files deleted due to versioning. If you set your system to keep three versions of each file, with older versions automatically deleted, you end up paying for those deleted versions for the full minimum duration.

In a typical backup workflow, let’s say you are using a cloud storage service to store your files and your backup program is set to a 30 day retention. That means you will be perpetually paying for an additional 60 days worth of storage (for files that were pruned at 30 days). In other words, you would be paying for a 90 day retention period even though you only have 30 days worth of backups.

Tip 4: Beware of hidden minimums

As the cloud storage market has matured, pricing models have become more complicated. To create an accurate budget, it’s crucial to understand all potential cost components, including some that might not be immediately obvious. Here are two key areas to examine:

1. Minimum monthly charges: Some providers charge a set fee regardless of how little you store. For instance, you might pay for 1TB even if you only use 100GB.
2. Minimum file sizes: Some services round up small files to a minimum billable size, often 128KB. While this might seem insignificant, it can add up quickly if you have millions of small files.

Tip 5: Be suspicious of the fine print

Misdirection is the art of getting you to focus on one thing so you don’t focus on other things going on. Practiced by magicians and some cloud storage companies, the idea is to get you to focus on certain features and capabilities without delving below the surface into the fine print. (And, sometimes the prices this technique generates feels like someone has pulled a rabbit out of a hat—to your company’s detriment.)

Read the fine print and as you scroll through the multi-page pricing tables and linked pages of all of the rules that shape how you can use a given cloud storage service. Stop and ask, “What are they trying to hide?” If you find phrases like: “We reserve the right to limit your egress traffic,” or “New users get free usage tier for 12 months,” or “Provisioned requests should be used when you need a guarantee that your retrieval capacity will be available when you need it,” take heed. 

And, even if it seems like you can turn the tables and use things like free credits in the short term, remember that you’ll want to have a plan for your long-term infrastructure when those credits run out as well. 

How to build a predictable cloud storage budget

As organizations increasingly rely on cloud storage for everything from day-to-day operations to long-term data archiving, the ability to accurately forecast and control these costs can significantly impact overall IT budgets and business planning.

The first place to start is data storage as it’s generally the easiest for a company to calculate. For a given month, you can calculate your data volume as follows:

Data stored = current data + new data – deleted data

Take that total and multiple by the monthly storage rate and you’ll get your monthly storage costs. 

Things can get more complicated if your business regularly uploads and downloads data. The data stored at the end of the month should get you at least in the ballpark. But, creating a predictable cloud storage budget requires a holistic understanding of your data needs, usage patterns, and the pricing structures of your chosen provider. It’s not just about estimating how much data you’ll store, but also how you’ll interact with that data over time. Will you be frequently accessing and modifying files, or primarily using the storage for long-term archiving? Are there seasonal fluctuations in your data storage or retrieval patterns? These factors can all influence your overall costs, and we’ll walk through a scenario to show that next.

Let’s do the math

To illustrate how to calculate your cloud storage costs, let’s work through an example using current Backblaze B2 pricing. We’ll focus on a single month for a growing business that is backing up business data to the cloud and verifying their backups have zero errors during recovery:

• Initial storage at the beginning of the month: 100TB
• New data added during the month: 10TB
• Data deleted during the month: 5TB
• Downloads during the month (egress): 75TB

Backblaze has built a cloud storage calculator that computes costs for all of the major cloud storage providers. Using this calculator, we find that Amazon S3 would cost $2,675 to store this data for a month, while Backblaze B2 would charge just $630.

Using those numbers for storage and assuming you download 75TB a month for backup validation testing, you get a total monthly cost of $8,725 for Amazon S3; Backblaze B2 would be $630 a month. 

The additional cost you see from AWS S3 is from download costs, also known as egress fees, and they can certainly take a toll on your budget. Backblaze offers free egress up to three times the amount you have stored so you can move data when and where you prefer.

The chart below provides the breakdown of the expected cost.

	Backblaze B2	Amazon S3
Storage	$630	$2,675
Egress	Free*	$6,050
Totals:	$630	$8,725

*Up to 3x of average monthly data stored, then $0.01/GB for additional egress.

Of course each month you will add and delete storage, so you’ll have to account for that in your forecast. And, as we mentioned above, there may also be other fees like minimum storage duration fees or API transaction fees. Using the cloud storage calculator noted above, you can get a reasonable estimate of your total cost over the budget forecasting period.

Finally, you can use the Backblaze B2 storage calculator to address potential use cases that are outside of your normal operations, such as if you delete a large project from your storage or you need to download a large amount of data. Running the calculator for these types of actions lets you obtain a solid estimate for their effect on your budget before they happen and lets you plan accordingly.

Understanding cloud storage pricing gives you options

Creating a predictable cloud storage forecast is key to taking full advantage of all of the value in cloud storage. Organizations like Austin City Limits, Amplify, and Runbiz were able to move to the cloud because they could reliably predict their cloud storage cost with Backblaze B2. You don’t have to let pricing tiers, hidden costs, and fine print stop you. Backblaze makes predicting your cloud storage costs easy.

The post Five Tips for Creating a Predictable Cloud Storage Budget appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Post Syndicated from Stephanie Doyle original https://www.backblaze.com/blog/proper-address-ipv4-vs-ipv6/

Ah, the 1980s. It brought us such classics as Ghostbusters, The Princess Bride, Tina Turner’s triumphant comeback, Pac-Man, and the original Apple Macintosh. Also, it gave us the birth of the internet, in which we figured out how to make all our computers one giant, powerful network held together initially by internet protocols (IPs) and, eventually, by a mutual love of cat videos. 

Now, each of our devices that connect to the internet require a way to find and send information back and forth, which means they need an IP address. Most folks don’t type IP addresses into their search bar though—we use domain names (for example, www.backblaze.com). Which IP addresses correspond to which domain names is stored in a hierarchical and distributed database system known as the domain name system (DNS), which is also an internet protocol. 

Today, let’s talk about IP addresses: What are IPv4 and IPv6, why is IPv6 necessary, and what impact will it have on networking?

Let’s set the scene

Any time you’re sending and receiving data, be it a letter in the mail, dialing a phone number, or loading a website, you’ve got to have an identifiable address reach the proper person and/or device. What all of these types of addresses have in common is that as our population has exploded, we’ve had to re-work how addresses work in order to include more possible data locations. U.S. zip codes were established in 1963. Area codes were established in 1947, and a great expansion was necessary only three(ish) decades later, and that plan was implemented starting in the late 1980s and ending in the mid ’90s.

IP addresses, meanwhile, have been operating on the first and only protocol we introduced in the 1980s, called IPv4. Not only has the world population almost doubled since then, but there has also been a nonlinear explosion in internet-connected devices per person. When IP addresses were first invented, it was unfathomable that most folks would be walking around with a computer in their pocket, remotely checking who’s ringing their doorbells while adjusting their thermostat in anticipation of returning home. All of those internet-connected devices use an IP address, in one way or another. 

So, it’s no surprise that we’re now seeing an adoption of a new IP address standard. In keeping with tradition, the versions aren’t sequential: Right now we’re jumping from IPv4 to IPv6. (What happened to IPv5? It was skipped, sort of.)

What is IPv4?

IPv4 is an internet protocol that assigns addresses to devices. It uses a 32-bit address, represented by four numbers (octets), each between 0 and 255, separated by dots (e.g., 192.168.1.100), and uses decimal notation. 

Remember that each bit represents one of two possible values, a 0 or a 1. So, for a 32-bit value, there are 2^32 possible addresses, or 4,294,967,296 IP addresses total. Several IPv4 address blocks were also reserved for private networks and multicast addresses, about 286 million total. Between the two reserved blocks of addresses, that’s about 7% of the total addresses in existence.

What is IPv6?

IPv6 uses a 128-bit address, represented by a longer string of numbers and letters (e.g., 2001:0db8:85a3:0000:0000:8a2e:0370:7334) in hexadecimal code, aka hex code. If you’ve ever designed a MySpace page (hi, Tom!) or a webpage, you’re likely familiar with the hex codes used to identify precise colors.

Doing the math as we did above, there are 2^128 possible IPv6 addresses, which is 340 undecillion. (That’s the 11th order of magnitude if you’re going, million, billion, trillion, and so on.) And, just like IPv4, there are some reserved addresses, but they represent such a comparatively smaller number of total available addresses that it’s not even worth calculating a percentage. 

Woah, how have we been surviving in the meantime?

We mentioned above that we’ve known we’re running out of IP addresses for a while. But, important detail: There was evidence of the problem as early as 1981, and mitigation efforts were enacted by 1992. Before we get into what mitigation strategies have been used over the years, a bit of a refinement of the above information—IP addresses consist of two main parts, one that identifies the network (or, sometimes, the subnet) and the host, or the destination on that network. (That’s true of both IPv4 and IPv6.)

Classful networking

In the original iteration of IPv4, the bits that identified the subnet were fixed, and that meant a lot of wasted space. In 1981, we implemented classful networking. Instead of keeping a fixed number of bits to identify a network, the three most significant bits identified the size of the network prefix, and that sent you to different classes. That meant that existing addresses didn’t have to change. Here’s a handy table:

Class	Most significant bits	Network prefix size (bits)	Host identifier size (bits)	Address range	Maximum number of networks	Maximum number of hosts per network
A	0	8	24	0.0.0.0–127.255.255.255	128 networks	16,777,216 hosts per network
B	10	16	16	128.0.0.0–191.255.255.255	16,384 networks	65,386 hosts per network
C	110	24	8	192.0.0.0–223.255.255.255	2,097,152 networks	256 hosts per network
D (multicast) E (reserved)	1110 1111	—	—	224.0.0.0–255.255.255.255	—	—

All that sounds a bit like gobbley-gook. An analogy: You live in a city that wants to improve mail delivery, so it’s introduced the option to choose from a small, medium, or large mailbox. The sizes are actually pretty disproportionate—the small is about the size of a toaster, whereas the medium is the size of a kitchen trash can. (And large is the size of your car. Who gets that much mail?) No matter which size mailbox you (or your neighbor) chooses, your physical address didn’t change when this system was implemented. You usually get more mail than the toaster would accommodate, but never even come close to filling your trash can-sized mailbox. So, that extra space just sits empty and unused, never fulfilling its mail volume potential.  

Note that classful networking is now largely defunct, replaced by…  

Classless inter-domain routing (CIDR)

The biggest issue of the above system was its inflexibility. Adding classes gave us more flexibility than the original design, but you were still restricted to 8, 16, or 24 bits to identify the network. That means you can end up with a lot of unused IP addresses, as indicated by our above analogy. Here’s the math behind why: 

The number of addresses available on a network is the inverse of how many bits you use to define it. So, in a 32-bit address, if you use 16 bits to define the network, you have 8 bits leftover to define the host. That’s our Class C network, which contained 2^8 (256) IP addresses—not enough for most use cases. And, the next smallest subset, Class B, represented 2^16 IP addresses (65,536 total), which most organizations could not use efficiently. After DNS became the norm, it became clear that classful networking wasn’t scalable, and thus CIDR rose to prominence.  

CIDR is based on variable-length subnet masking (VLSM), which lets each network be divided into subnetworks of various power-of-two sizes. This method optimizes the allocation of IPv4 addresses by allowing for more flexible address blocks. 

Using our analogy, instead of assigning mailbox size based on household size, you might just have a system in which folks walk up to the post office and find their name on a list associated with a mailbox. If someone has more or less mail that month, then they can be assigned the properly sized mailbox. 

Network address translation (NAT)

NAT allows multiple devices to share a single public IPv4 address by modifying the IP header when it’s in transit. This is super useful when you’re talking about private networks—you can assign a single IP address to multiple devices. For example, if you have several internet of thing (IoT) devices in your home, they can all appear to the public network as one IP address, and your local network can figure out what traffic goes where. It also makes it so that if a network moves, the host doesn’t necessarily have to be assigned a new IP address, such as if an internet provider like Cox decides to stop doing business in your region, and Spectrum takes over their IP address allocation—though likely they’d just change your public IP address in that specific scenario.

In our mail analogy, NAT is like those group mailboxes you see in rural areas, apartment buildings, or in neighborhoods. Everyone in the same location gets their mail delivered to the same physical address, and your box number is used to further identify your house within the group mailbox. 

The secondary market of IP addresses

If we can learn anything from the above workarounds, flexibility and possibility is key. So, it’s unsurprising to know that a secondary market has cropped up, introducing things like address recycling, address trading, and address leasing. IPv6 will solve the scarcity issue—but what else can it do?

What are the benefits of IPv6?

So far we’ve talked about the primary benefit of IPv6—more IP addresses that we clearly need. But, there are other benefits as well. Here’s a summary: 

Improved Efficiency

• Simpler header: The IPv6 header is simpler than IPv4’s, leading to faster packet processing and reduced overhead.
• Efficient routing: IPv6’s design allows for more efficient routing, potentially reducing latency and improving network performance. Arguably, most folks won’t see a huge performance improvement unless they reconfigure their own network architecture, but the possibility is there. 
• Autoconfiguration: IPv6 supports automatic configuration of network interfaces, simplifying setup and reducing administrative overhead.

Enhanced Security

• Built-in security features: IPv6 offers built-in security mechanisms like IPsec, potentially providing better protection against attacks. In practice, it’s not typically implemented as most encryption is typically handled at the transport layer security (TLS) IP layer. 

Quality of Service (QoS)

• Improved QoS: IPv6 provides better support for QoS, allowing for prioritization of different types of traffic, ensuring a better user experience for applications like video conferencing and online gaming.

Other Benefits

• Reduced reliance on NAT: IPv6 reduces the need for NAT, simplifying network configurations and improving end-to-end connectivity.
• Support for new services: IPv6 is better suited for emerging technologies and applications that require a large number of addresses and advanced features.

What’s next? Will we run out again?

Given the amount of addresses for IPv4 vs. IPv6 (4.2 billion vs. 340 undecillion, respectively), you can understand how we might have needed to shore up our IPv4 addresses. Honestly, if you assume one device per person, we already outnumber IPv4 addresses—in fact, we outnumbered IP addresses in the 1970s, before IPv4 was even invented! You shouldn’t assume one device per person, by the way. While many countries with widespread broadband access have several devices per person—in the U.S., Consumer Affairs was reporting 21 per U.S. household in 2023, and the average U.S. household for that same year was 2.51 people. Globally, that same source reports 3.6 internet-connected devices per person.   

Changes like this can certainly be disruptive, but the good news on that front is that most devices will be dual-stacked for quite a while. That means that you’ll have both versions of an IP address, and this change can roll out organically (so to speak). In the end, we’ll have a better-performing internet, ready to grow with us for the foreseeable future.

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Post Syndicated from Vinodh Subramanian original https://www.backblaze.com/blog/container-orchestration-managing-applications-at-scale/

The use of containers for software deployment has emerged as a powerful method for packaging applications and their dependencies into single, portable units. Containers enable developers to create, deploy, and run applications consistently across various environments. However, as containerized applications grow in scale and complexity, efficiently deploying, managing, and terminating containers can become a challenging task.

The growing need for streamlined container management has led to the rise of container orchestration—an automated approach to deploying, scaling, and managing containerized applications. Because it simplifies the management of large-scale, dynamic container environments, container orchestration has become a crucial component in modern application development and deployment. 

In this blog post, we’ll explore what container orchestration is, how it works, its benefits, and the leading tools that make it possible. Whether you are new to using containers or looking to optimize your existing strategy, this guide will provide insights that you can leverage for more efficient and scalable application deployment. 

What are containers?

Before containers, developers often faced the “it works on my machine” problem, where an application would run perfectly on a developer’s computer but fail in other environments due to differences in operating systems (OS), dependencies, or configuration. 

Containers solve this problem by packaging applications with all their dependencies into single, portable units, improving consistency across different environments. This greatly reduces the compatibility issues and simplifies the deployment process. 

As a lightweight software package, containers include everything needed to run an application such as code, runtime environment, system tools, libraries, binaries, settings, and so on. They run on top of the host OS, sharing the same OS kernel, and can run anywhere—on a laptop, server, in the cloud, etc. On top of that, containers remain isolated from each other, making them more lightweight and efficient than virtual machines (VMs), which require a full OS for each instance. Check out our article to learn more about the difference between containers and VMs here. 

Containers provide consistent environments, higher resource efficiency, faster startup times, and portability. They differ from VMs in that they share the host OS kernel. While VMs virtualize hardware for strong isolation, containers isolate at the process level. By solving the longstanding issues of environment consistency and resource efficiency, containers have become an essential tool in modern application development. 

What is container orchestration?

As container adoption has grown, developers have encountered new challenges that highlight the need for container orchestration. While containers simplify application deployment by ensuring consistency across environments, managing containers at scale introduces complexities that manual processes can’t handle efficiently, such as:

1. Scalability: In a production environment, applications often require hundreds or thousands of containers running simultaneously. Manually managing such a large number of containers becomes impractical and error-prone. 
2. Resource management: Efficiently utilizing resources across multiple containers is critical. Manual resource allocation leads to underutilization or overloading of hardware, negatively impacting performance and cost-effectiveness. 
3. Container failure management: In dynamic environments, containers can fail or become unresponsive. Developers need a way to create a self-healing environment, in which failed containers are automatically detected, then recover without manual intervention to ensure high availability and reliability. 
4. Rolling updates: Deploying updates to applications without downtime and the ability to quickly roll back in case of issues are crucial for maintaining service continuity. Manual updates can be risky and cumbersome. 

Container orchestration automates the deployment, scaling, and management of containers, addressing the complexities that arise in large-scale, dynamic application environments. It ensures that applications run smoothly and efficiently, enabling developers to focus on building features rather than managing infrastructure. Container orchestration tools provide various features such as automated scheduling, self-healing, load balancing, and resource optimization to deploy and manage applications more effectively to ensure reliability, performance, and scalability. 

What are the benefits of container orchestration?

Container orchestration offers many different advantages that streamline the deployment and management of containerized applications. We’ve touched on a few of them, but here’s a concise list: 

• Improved resource utilization: Orchestration tools can efficiently pack containers onto hosts, maximizing hardware usage. 
• Enhanced scalability: Easily scale applications up or down to meet changing demands. 
• Increased reliability: Automatic health checks and container replacement ensure high availability. 
• Simplified management: Centralized control and automation reduce the complexity of managing large-scale containerized applications. 
• Faster deployments: Orchestrators enable rapid and consistent deployments across different environments. 
• Cost efficiency: Better resource utilization and automation, leading to cost savings. 

How does container orchestration work?

Now that we understand what container orchestration is, let’s take a look at how container orchestration works using the example of Kubernetes, one of the most popular container orchestration platforms. 

In the above diagram, we see an example of container orchestration in action. The system is divided into two main sections: the control plane and the worker nodes. 

Control plane

The control plane is the brain of the container orchestration system. It manages the entire system, ensuring that the desired state of the applications is maintained. Key components of the control plane include:

• Configuration store (etcd): A distributed key-value store that holds all the cluster data, such as the configuration and state information. Think of it as a central database for the cluster. 
• API server: The front-end of the control plane, exposing the orchestration API. It handles all the communication within the cluster and with external clients. 
• Scheduler: Assigns workloads to nodes based on resource availability and scheduling policies, ensuring efficient resource utilization. 
• Controller manager: Runs various controllers that handle routine tasks to maintain the cluster’s desired state. 
• Cloud control manager: Interacts with cloud provider APIs to manage cloud specific resources, integrating the cluster with cloud infrastructure. 

Worker nodes

Worker nodes, virtual machines, and bare metal servers are all common options for where to run application workloads. Each worker node has the following components: 

• Node agent (kubelet): An agent that ensures the containers are running as expected. It communicates with the control plane to receive instructions and report back on the status of the nodes. 
• Network proxy (kube-proxy): Maintains network rules on each node, facilitating communication between containers and services within the cluster. 

Within the worker nodes, pods are the smallest deployable units. Each pod can contain one or more containers that run the application and its dependencies. The diagram shows multiple pods within the worker nodes, indicating how applications are deployed and managed. 

The cloud provider API directs how the orchestration system dynamically interacts with cloud infrastructure to provision resources as needed, making it a flexible and powerful tool for managing containerized applications across various environments. 

Popular container orchestration tools

Several container orchestration tools have emerged as the leaders in the industry, each offering unique features and capabilities. Here are some of the most popular tools:

Kubernetes

Kubernetes, often referred to as K8s, is an open-source container orchestration platform initially developed by Google. It has become the industry standard for managing containerized applications at scale. K8s is ideal for handling complex, multi-container applications, making it suitable for large-scale microservices architectures and multi-cloud deployments. Its strong community support and flexibility with various container runtimes contribute to its widespread adoption.

Docker Swarm

Docker Swarm is Docker’s native container orchestration tool, providing a simpler alternative to Kubernetes. It integrates seamlessly with Docker containers, making it a natural choice for teams already using Docker. Known for its ease of setup and use, Docker Swarm allows quick scaling of services with straightforward commands, making it ideal for small to medium-sized applications and rapid development cycles. 

Amazon Elastic Container Service (ECS)

Amazon ECS (Elastic Container Service) is a fully managed container orchestration service provided by AWS, designed to simplify running containerized applications. ECS integrates deeply with AWS services for networking, security, and monitoring. ECS leverages the extensive range of AWS services, making it a straightforward orchestration solution for enterprises using AWS infrastructure.

Red Hat OpenShift

Red Hat OpenShift is an enterprise-grade Kubernetes container orchestration platform that extends Kubernetes with additional tools for developers and operations, integrated security, and lifecycle management. OpenShift supports multiple cloud and on-premise environments, providing a consistent foundation for building and scaling containerized applications.

Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is a managed Kubernetes service offered by Google Cloud Platform (GCP). It provides a scalable environment for deploying, managing, and scaling containerized applications using Kubernetes. GKE simplifies cluster management with automated upgrades, monitoring, and scalability features. Its deep integration with GCP services and Google’s expertise in running Kubernetes at scale make GKE an attractive option for complex application architectures.

Embracing the future of application deployment

Container orchestration has undoubtedly revolutionized the way we deploy, manage, and scale applications in today’s complex and dynamic software environments. By automating critical tasks such as scheduling, scaling, load balancing, and health monitoring, container orchestration enables organizations to achieve greater efficiency, reliability, and scalability in their application deployments. 

The choice of orchestration platform should be carefully considered based on your specific needs, team expertise and long term goals. It is not just a technical solution but a strategic enabler, providing you with significant advantages in your development and operational workflows.

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Post Syndicated from Stephanie Doyle original https://www.backblaze.com/blog/ai-101-why-rag-is-all-the-rage/

At the risk of being called the stick in the mud of the tech world, we here at Backblaze have often bemoaned our industry’s love of making up new acronyms. The most recent culprit, hailing from the fast-moving artificial intelligence/machine learning (AI/ML) space, is truly memorable: RAG, aka retrieval-augmented generation. For the record, its creator has apologized for inflicting it upon the world.

Given how useful it is, we’re willing to forgive. (I’m sure he was holding his breath for that news.) Today, our AI 101 series is back to talk about what RAG is—and the big problem it solves. 

Let’s start with large language models (LLMs)

LLMs are the most recognizable expression of AI in our current zeitgeist. (Arguably, you could append that with “that we’re all paying attention to,” given that ML algorithms have been behind many tools for decades now.) LLMs underpin tools like ChatGPT, Google Gemini, and Claude, as well as things like service-oriented chatbots, natural language processing tasks, and so on. They’re trained on vast amounts of data with algorithmic guardrails known as parameters and hyperparameters guiding their training. Once trained, we query them through a process known as inference. 

Fabulous! The possibilities are endless. However, one of the biggest challenges we’ve experienced (and laughed about on the internet) is that LLMs can return inaccurate results, while sounding very, very reasonable. Additionally, LLMs don’t know what they don’t know. Their answers can only be as good as the data they draw from—so, if their training dataset is outdated or contains a systematic bias, it will impact your results. As AI tools have become more widely adopted, we’ve seen LLM inaccuracies range from “funny and widely mocked” to “oh, that’s actually serious.”

Enter retrieval-augmented generation (Fine! RAG)

RAG is a solution to these problems. Instead of relying on only an LLM’s dataset, RAG queries external sources before returning a response. It’s more complicated than “let me google that for you,” as the process takes that external data, turns it into a vectored database, and then balances external data with an LLM’s “general knowledge” generated response and skill at responding to conversational queries. 

This has several advantages. Users now have sources they can cite, and recent information is taken into account. From a development perspective, it means that you don’t have to re-train a model as frequently. And, it can be implemented in as few as five lines of code. 

One important nuance is that when you’re building RAG into your product, you can set its sources. For industries like medicine and law, that means you can point them towards industry journals and trusted sources, outweighing the often misquoted or mis-cited examples you might see in a general database. 

Another example: For a technical documentation portal, you can take an LLM, trained on general information and the nuts and bolts of conversational querying, and direct it to rely on your organization’s help articles as its most important sources. Your organization controls the authoritative data, and how often/when changes are made. Users can trust that they’re getting the most recent security patches and correct code. And, you can do so quickly, easily, and—most importantly—cost-effectively. 

RAG doesn’t mean foolproof AI

RAG is a great, straightforward method for keeping LLM tools updated with current, high-quality information and giving users more transparency around where their answers are coming from. However, as we mentioned above, AI is only ever as good as the data it uses. Keep in mind, that’s a deceptively simple thing to say. It’s an entire, specialized job to validate datasets, and that expertise is built into the research and monitoring that happens while training an LLM. 

RAG gives a new source of data a privileged position—you’re saying “this data is more authoritative than that data” and, since the LLM doesn’t have anything in its general database, it may not have a counter argument. If you’re not paying attention to your RAG data source standards, and doing so on an ongoing basis, it’s possible, and even likely, that data bias, low quality data, etc. could creep into your model. 

Think of it this way: If you’re pointing to a new feature in your tech docs and there’s an error, that impact is magnified because an LLM will give more weight to the RAG data. At least in that case, you’re the one who controls the source data. In our other examples of legal or medical AI tools pointing to journal updates, things can get, well, more complicated. If (when) you’re setting up an AI that uses RAG, it’s imperative to make sure you’re also setting yourself up with reliable sources that are regularly updated. 

But, given its impact, and how low of a lift it is to integrate into existing products, we can see why RAG is all the RAGe—and, as always, we look forward to more to come in the AI landscape. For now, we can already see the impact it’s having on the market, with SaaS companies and startups alike exploring the possibilities.

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