devopsy.kinsta.cloud

Category: Cloud Management

  • Consider Performance, Growth & Budget When Buying Data Analytics

    Consider Performance, Growth & Budget When Buying Data Analytics

    So, your business needs to invest in data analytics technology to improve efficiencies, competitive advantage or business outcomes. There are a ton of things to consider, but underlying each decision are three driving factors:

    • Performance – The need to meet service level agreements.
    • Growth – The need to accommodate success, as well as deal with inevitable industry changes.
    • Budget – The need to do both of the above while keeping costs low enough to not erode the profit gain from analytics.

    These three forces underpin nearly every technology selection decision, but what frequently isn’t noticed is how much these three drivers interact.

    1. Concurrency and Growth

    Test both for current levels of concurrency and the level you hope to achieve in the next few years as the organization grows.

    When evaluating benchmarks between vendors, consider that benchmarks are designed to make that company’s software look good. For example, if they are good at concurrency, you will see various levels of concurrency represented in the benchmark. If the benchmark shows only one workload at a time, or maybe ten, you can safely assume that concurrency is probably not something that technology is good at. Similarly, if the benchmark is done at 10 TB scale, the vendor probably isn’t that good at high-scale analytics.

    When bringing vendors in for a proof-of-concept, one mistake I’ve seen (more times than I can count) is testing the capabilities of the software with only one user at a time. People do not line up politely to use analytics one at a time.

    In addition to current levels of concurrency, over time, hopefully, the organization will grow, and the number of both internal analytics users and external customers will increase. Testing for the number of users you have now may not uncover problems that will invariably reveal themselves when a lot of people hit the system at once. The last thing your organization needs is to turn away customers because they’re overloading your systems.

    2. Performance & Price

    Evaluate price/performance, not performance alone.

    Everyone understands the importance of performance. Not one user has ever gone to a data architect or engineer and requested for their analytics to “please execute more slowly.” But performance isn’t simply how fast the analytical software can process queries, how fast the data pipelines can move data or how fast the response is between data input and reaction output. There’s also a monetary aspect of performance to bear in mind.

    In a recent benchmark, three data management and analytics stacks were compared with 60 simulated concurrent users/workloads. The difference in queries per hour on 250 TB of data was minor, perhaps 16 queries per hour for the slowest and 23 queries per hour for the fastest.

    (Mcknight Benchmark – SaaS Data Analytics Platform Comparison – vendor names removed)

    It looks like performance is just not that big a consideration for this technology. But there is no such thing as an unlimited budget. Let’s look at how much it would cost to get this level of performance out of each stack.

    (Mcknight Benchmark – SaaS Data Analytics Platform Comparison – vendor names removed)

    Price-performance is a far more useful metric of value to the organization. How much the software can do is irrelevant if the budget limits how much the business can afford to do with it.

    3. Software Efficiency

    Consider how much efficiency and control you’re willing to trade for ease-of-use.

    When you analyze data in the cloud, you’re not just paying for software, you’re also paying for hardware, a.k.a. cloud compute instances. Bundling the two costs in one bill makes things easier. But it also means that cloud providers make more money the more instances, or compute nodes, you use. You trade ease-of-use in billing for a higher price. And they no longer have a financial incentive to provide efficient software.

    Similarly, to make a cloud-based analytics software scale up in performance and concurrency with zero effort on your part, the software has to use only defaults for all settings internally. It can scale only by throwing more compute at the problem; compute that you’re paying for every time you use it. You’re trading control of the software–tuning, scaling guidelines, shut down conditions, etc.–for ease-of-use, again for a higher price.

    Throwing more compute at a problem isn’t always the best solution. Recently, I saw a POC where the customer implementation included 278 EC2 nodes on Amazon cloud. The organization didn’t initiate the POC because of inefficiency. They were having performance problems, even with that much compute power. A competing product was able to run on only nine EC2 nodes with the same workload and better performance. The company was paying 25 times as much for the compute hardware because it was convenient.

    Be aware of the tradeoffs.

    4. Affordable Scalability

    Paying for only what you use can become impractical when you use more and more.

    The volume of data that is being created, consumed and captured is growing exponentially. By 2025, it is estimated that nearly 30% of data being generated will be real-time. Instead of pulling data in batches, data will be pulled in continuously. Queries will be responded to in seconds, not hours, or in milliseconds, not seconds.

    Going to the cloud can make tremendous sense when a company or a workload in that company is new. But studies show that as businesses scale and growth slows, the pressure the cloud puts on business margins eventually outweighs its benefits. This is why most enterprises today are considering repatriating some or all of their workloads off the cloud.

    5. Flexibility for Change

    Make certain your stack will continue to work even as conditions change, and you won’t have to rebuild from scratch to do the same thing in a different location.

    When you’re building a long-term architectural strategy, you want to ensure you cover as many bases as possible because, the fact is, you really don’t know what the future holds. Ten years ago, maybe you had a Hadoop on-premises strategy or a data warehouse appliance. Today, you may have a cloud-focused strategy. Tomorrow, maybe you’ll move some or all of those workloads back on-premises due to performance, security, costs, regulations or other reasons.

    This is why you want flexibility that doesn’t mandate a “cloud-only” or an “on-premises-only” or a “this-cloud-only” architecture and which doesn’t integrate with only the same vendors’ software. You want an architecture that can work equally well both on the cloud as well as on-premises, hybrid or multi-cloud, providing the flexibility of going where business needs are best met.

    Containerization offers possibly the highest level of flexibility in deployment, but it can also add to deployment complexity, so again, be aware of what you’re trading. If the software you choose is platform-agnostic—not tied to any particular location, ecosystem or deployment model—then that will make future choices a lot more flexible.

    Regardless of what analytics you choose, what you decide to build or what use cases you have to meet, striving for balance in performance, growth and budget will create a strong data architecture, now and for the future:

      1. The analytics architecture is high-performance: it should meet SLAs, get things done on time and meet or exceed expectations and requirements.
      2. The analytics architecture is resilient to growth and change; even if there is rapid growth or sudden or extreme changes, it’s future-ready.
      3. The analytics architecture should control costs and live within an allocated budget.

    Keep these guidelines in mind when choosing an analytics provider. The decisions you make today will have direct ramifications on the growth, profitability and success of the business tomorrow.

  • Survey: Much Greater Focus on Reining in IT Costs

    Survey: Much Greater Focus on Reining in IT Costs

    A survey from Vega Cloud found 62% of respondents are more closely monitoring all IT costs, with more than half (52%) more closely evaluating their cloud service provider options.

    The survey polled 100 vice presidents from organizations with more than 1,000 employees and was published today by Vega Cloud, a provider of a platform for managing multiple clouds.

    Overall, nearly half of respondents (48%) said they expected to reduce their IT spending a little or a lot in 2023, with 65% identifying the reduction of cloud spending as a specific goal. Three quarters (75%) are looking to cut cloud costs by 10-50% in 2023, the survey found.

    A full 93% reported they have invested at least a fair amount of money on tools to optimize cloud costs, with 82% having invested in FinOps staff or people with FinOps experience. The challenge is that 70% of respondents said it was difficult to find people with these skill sets, the survey found.

    The best way for organizations to reduce cloud costs is to become more informed about what they’re spending (40%), and 33% said that the best approach is to consistently optimize use of cloud resources. A total of 61% identified reserved instances/savings plans as the best way to optimize cloud use, while 55% said optimization was best achieved through waste elimination followed closely by right-sizing (54%).

    Vega Cloud CEO Kris Bliesner said it’s clear the days when organizations allowed developers to consume cloud resources at will are coming to an end as cloud services consume a much larger percentage of the IT budget. In fact, the survey noted that 91% of respondents are spending at least one million dollars annually on cloud infrastructure alone.

    The issue is that finance departments not only want to reduce cloud spend but also want cloud spending to become more predictable from a budget perspective. Nearly half of survey respondents (45%) said they’d received a surprise in their cloud bills “a number of times” over the past 12 months, with 70% of respondents noting surprises have happened at least a half dozen times in the past year.

    A total of 40% admitted they had exceeded budgets multiple times in the past year, and 38% said they had to modify their forecast cloud spending budget numerous times over the past 12 months. More than half of respondents (55%) said database costs are the metric that matters most when looking at cloud usage followed by bandwidth costs (51%), storage costs (49%) and compute costs (47%). Nearly two-thirds (64%) said they track metrics at least somewhat often, while 46% said they have somewhat optimized their cloud use. A total of 11% said they’re not optimized at all.

    A full 88% said they’re using artificial intelligence (AI) at least somewhat to optimize cloud use and costs, with nearly three-quarters (74%) noting they think ChatGPT or a similar generative AI tool could be used to optimize cloud use/costs further. Specifically, 42% said they thought it could improve decision making and 30% said it could automate decision making.

    Among the cloud service providers, Google is cited as doing the most to help customers manage and reduce cloud costs (38%), followed by Amazon Web Services (34%) and Microsoft (28%)—but a full 85% said they could all be doing more.

    Of course, changing economic conditions are naturally driving organizations to look harder at cloud spending. Well over half of respondents (56%) said they are concerned about the economy, and 95% of those respondents said those concerns have, at least somewhat, impacted their IT strategy.

    It’s not clear whether organizations will continue to exercise greater control over cloud spending should the economy recover. For now, at least, there’s clearly more desire for control than ever.

  • 5 Best Practices for Using VMs on Azure Cloud

    5 Best Practices for Using VMs on Azure Cloud

    When beginning a cloud improvement/optimization project, an easy place to start is to focus on the initial low-hanging fruit of your virtual machine (VM) environments. This article takes a closer look at these steps, using a Microsoft Azure VM environment as an example.

    Here are five best practices that can be easily achieved when using VMs on Azure cloud.

    1. Properly Size Your Virtual Machines: To maximize performance and minimize costs, it’s important to size your VMs appropriately. You can use the Azure portal to determine the right size for your workloads and then select the right VM size based on the CPU, memory and storage requirements of your application. For example, you can use the Azure VM sizing calculator tool to get an estimate of the virtual machine size based on your workload requirements.

    Let’s say you want to host a web application that requires 2 vCPUs, 8 GB of memory and 50 GB of storage. You can use the Azure VM sizing calculator tool to determine the appropriate VM size based on these requirements. The tool may suggest using a “Standard B2s” VM which has 2 vCPUs, 4 GB of memory and a maximum disk size of 32 GB.

    In this case, you would need to increase the disk size to 50 GB. You can do this by adding a data disk to the VM or using a larger VM size with enough disk space. For example, you could use a “Standard B4ms” VM with 4 vCPUs, 16 GB of memory, and a maximum disk size of 64 GB.

    It’s important to note that the actual VM size may vary based on your workload requirements and the specifics of your application. The Azure VM sizing calculator tool provides a helpful starting point, but you should also perform your own testing and validation to ensure that the VM size meets your needs.

    2. Use Managed Disks: Managed disks provide increased reliability, scalability and security compared to traditional storage accounts. For example, you can use managed disks to automatically scale the disk size and performance as the data grows and to replicate data for disaster recovery.

    For example, to create a 100 GB managed disk, follow these steps:

    • In the Azure portal, go to the “Disks” section and click on “Add.”
    • Select “Blank disk” as the source and specify the disk size as 100 GB.
    • Choose the appropriate storage account type (Premium or Standard), replication options and disk type (HDD or SSD).
    • Give the disk a name and create it in the same resource group as your VM.
    • Once the disk is created, you can attach it to your VM by going to the virtual machine’s “Disks” section, clicking on “Attach,” and selecting the newly created disk. By using managed disks, you benefit from automatic disk management and replication as well as improved reliability and security compared to traditional storage accounts. Additionally, managed disks simplify the process of attaching and detaching disks to and from VMs, making it easier to manage your storage resources.

    3. Enable Monitoring and Diagnostics: To monitor the health and performance of your VMs, you can use Azure Monitor which provides real-time insights into resource utilization, application performance and other key metrics. You can also configure alerts to notify you of potential issues and diagnose problems using Azure Log Analytics. For example, you can set up an alert to notify you if the CPU usage of a VM exceeds a certain threshold.

    You can also use Azure Monitor to gain real-time insights into the virtual machine’s resource utilization, application performance and other key metrics by following these steps:

    • In the Azure portal, go to the virtual machine’s “Monitoring” section.
    • Click on “Diagnostic settings” and then click on “Add diagnostic setting.”
    • Give the diagnostic setting a name, select the VM you want to monitor and choose the metrics and logs you want to collect.
    • Choose the storage account you want to use to store the monitoring data and specify the retention policy for the data.
    • Click “Save” to apply the changes.

    By enabling monitoring and diagnostics for your virtual machines, you can proactively monitor the health and performance of your VMs and quickly identify and resolve potential issues. Additionally, you can use the collected data to generate reports, visualize trends and perform analysis to gain deeper insights into the performance of your VMs.

    4. Implement Security Best Practices: To secure your virtual machines, you can use Azure Security Center to implement security best practices and monitor for potential threats. You can also use Azure Active Directory to control access to VMs and secure your identity management. For example, you can use Azure Security Center to assess the security of your VMs and implement security recommendations such as enabling network security group (NSG) rules to restrict inbound and outbound traffic.

    If you have a VM that you want to connect to other resources in your Azure environment, you can use a managed virtual network to provide a secure and scalable networking solution for your VM.

    Here is how to create a managed virtual network in Azure:

    • In the Azure portal, go to the “Virtual network” section and click on “Add.”
    • Give the virtual network a name, select the appropriate subscription and resource group and specify the address space.
    • Create one or more subnets within the virtual network, and specify the subnet address range and subnet name.
    • Associate the virtual network with a network security group (NSG) to control inbound and outbound traffic.
    • Create a network interface for your VM and associate it with the virtual network and subnet you created.

    By using a managed virtual network, you can create a secure and isolated network environment for your VM and connect it to other resources in your Azure environment. Additionally, you can use the virtual network to control and manage network traffic and implement advanced networking scenarios such as load balancing, VPN connectivity and more.

    5. Keep Virtual Machines Up-to-Date: Regularly updating your VMs with the latest security patches and software updates helps ensure that they are protected against potential threats and vulnerabilities. You can use Azure Update Management to automate the update process and keep your VMs updated. For example, you can schedule regular updates for your VMs to ensure they always have the latest security patches and software updates installed.

    When planning public cloud environments, two commonly used words are “progress” and “results.” Following the best practices above will help position your cloud journey for success.

  • Orgs Shift Toward Centralizing Automation Initiatives

    Orgs Shift Toward Centralizing Automation Initiatives

    A survey published today found that 75% of respondents recognized that automation silos hamper their ability to unify cloud operations across functions.

    Conducted by CloudBolt Software, the survey polled 350 senior IT and DevOps leaders at organizations with more than 3,000 employees. On a positive note, the survey found 57% of respondents were starting to pursue a more centralized approach to automation, with a total of 88% acknowledging that having all previous and new automation centralized for the entire organization would ensure compliance, bolster security, enable business continuity, encourage reuse and reduce risk.

    Rather than taking a more holistic approach, well over half of respondents (57%) reported automation efforts are limited to specific silos. On average, organizations are employing 27 different automation tools their organizations have deployed, using one or a few as they see fit, with little to no consistency across teams. More than half (55%) of respondents said they have four or more separate automation teams, with 95% noting that each of those teams operates in their own silo.

    CloudBolt CTO Rick Kilcoyne said that lack of continuity clearly creates inefficiencies and redundancies that conspire to raise the total cost of IT. The challenge is that each team tends to favor one approach to automating a silo with little to no regard for a larger overall strategy to create an automation standard, he said.

    That approach also creates issues whenever a member of one of those teams leaves an organization, he noted. In fact, the survey found 77% of respondents reported that continuity is at risk any time a vital member of the team leaves.

    CloudBolt has been making a case for a federated automation platform that can be invoked either via application programming interfaces (APIs) or a graphical user interface (GUI) that provides access to low-code tools. The goal is to make the platform equally accessible to DevOps teams or IT administrators that tend to lack programming skills, and do so in a way that doesn’t require individual teams to replace their automation platform of choice. Instead, each of the automation platforms can be programmatically invoked via a central framework, said Kilcoyne. That approach eliminates any political debate over the potential need to replace one team’s favored automation platform with another, noted Kilcoyne.

    Regardless of who is accessing the platform, most organizations would be well-advised to develop a center of excellence for automation, he added.

    DevOps teams have always been committed to ruthlessly automating as many IT processes as possible, but in many cases, that has resulted in a plethora of automation tools being employed. In effect, automation may have become too much of a good thing. One approach to regaining control over those platforms is to create a platform engineering team that centralizes the management of DevOps workflows.

    Regardless of approach, Kilcoyne said the most important thing for any team to do before automating an IT workflow is to first understand how to manually set it up. Too many IT teams are trying to apply automation to processes they don’t really understand with mixed results, he added.

    The one thing that is certain, especially with the rise of artificial intelligence (AI), is the management of IT is going to become more automated.

  • Vega Cloud Commits to Reducing Cloud Costs

    Vega Cloud Commits to Reducing Cloud Costs

    Vega Cloud today made a commitment to save customers at least 10% on their cloud infrastructure spending or it won’t charge for its software. Vega Cloud’s software automates cloud infrastructure management.

    The Vega Cloud platform enables IT teams to connect, automate, manage and optimize new and existing networks, applications and other multi-cloud IT environments from a single console. As part of those capabilities, the company includes cost allocation and cost identification tools using a curated set of data to surface daily recommendations for cost improvements.

    Vega Cloud CEO Kris Bliesner said the goal is to provide a financial operations (FinOps) capability within the context of a platform capable of acting on those recommendations that, on average, reduces cloud costs by 15% to 25%.

    During uncertain economic times, IT leaders are under more pressure than ever to reduce costs. FinOps has emerged as a discipline for applying a set of best practices for specifically reducing cloud costs. In addition to reducing cloud costs, Vega Cloud is also making a case for reducing the total cost of IT by relying on a single automation framework to manage multiple clouds.

    In the cloud era, many organizations have found themselves employing multiple clouds, each of which requires a separate set of tools to manage. IT teams then need to hire additional staff to manage each of those tools. As a result, the total cost of IT increases with each additional platform added to the overall IT estate.

    Many organizations, of course, have allowed the number of platforms they manage to proliferate. This is because many application deployment decisions were driven by developers who can easily provision cloud infrastructure on their own. However, as concerns over the cost of cloud computing continue to rise, many organizations are revisiting how cloud infrastructure resources are used. Many of those reviews are being driven by finance teams that want cloud costs to become more predictable as cloud computing continues to account for a much larger percentage of the IT budget, noted Bliesner.

    In fact, one of the challenges IT leaders have is finding a way to make sure developers and DevOps teams are more motivated to reduce costs rather than focusing entirely on how fast they can build and deploy applications, he added.

    The reality is the average developer doesn’t need application build infrastructure running 24/7, so application development and testing is a great place to focus on cutting cloud costs initially, noted Bliesner. There’s also plenty of opportunity to better schedule workloads to reduce costs by relying more on lower-cost instances of cloud infrastructure, he said.

    DevOps engineers, by nature, don’t want to be inefficient, so the issue comes down to providing them with the tools needed to identify resources that are being wasted, added Bliesner.

    Most cloud service providers, of course, provide tools that enable IT teams to optimize spending, but Bliesner said those tools don’t go deep enough in terms of exposing issues. As a provider of an independent management framework, Vega Cloud is more committed to providing cost optimization capabilities that can be applied to multiple clouds, he added.

    In general, interest in controlling cloud costs has risen sharply. Vega Cloud has been engaged with more customers researching this issue in the last four weeks than in the last three years, said Bliesner. The challenge—and the opportunity—is to not just cut cloud costs but also identify patterns that make it easier to predict them, noted Bliesner.

    At this juncture, cloud costs are coming down. The only issue left to be determined is whether that goal will be accomplished with precision versus a fiat that adversely impacts an organization’s ability to continue to build and deploy software.

  • Cloud Management Issues Are Coming to a Head

    Cloud Management Issues Are Coming to a Head

    A survey of 1,300 technology and data executives found that more than a decade after the initial rise of cloud computing, organizations are still struggling to manage these environments.

    Conducted by Wakefield Research on behalf of NetApp, the survey found 98% of respondents reported their business has been impacted by everything from the increasing complexity of data and heightened cybersecurity risk to lack of visibility into business operations and staff burnout.

    Top challenges include cybersecurity risk (45%), staff not taking full advantage of business applications (44%), increased skepticism about the cloud from leadership (44%), lack of visibility into business operations (42%), going over budget (37%) and burning out staff (31%).

    A full 88% continue to encounter technical challenges, including data mobility between clouds (33%), security risks (33%) and interoperability between cloud services (30%).

    The top drivers of cloud investments are scaling artificial intelligence (AI) and automation (39%), followed by driving business innovation (35%) and creating digital experiences (34%).

    Organizational challenges encountered include having a clear vision for cloud strategy (32%), establishing the right level of governance (31%) and managing costs (30%). A full 84% are under pressure to show short-term return on investments (ROI) in cloud computing platforms, with 76% noting CFOs and other business leaders are skeptical of savings. Nearly half of the tech executive respondents (49%) reported that when cloud strategy discussions occur, cost concerns come up often or all the time. As a result, nearly three-fourths of executives (74%) said tiered workloads are either very or extremely important, the survey found.

    Other factors impacting multi-cloud strategy include environmental, social and governance (ESG) concerns (79%) and data sovereignty (74%), the survey found.

    Ronen Schwartz, senior vice president and general manager for cloud volumes at NetApp, said as more organizations employ multiple clouds, total costs tend to rise. Each cloud platform requires mastering another control plane, which means someone in the IT organization must have the right skills to manage, he noted. IT leaders are attempting to exercise more control over cloud deployments, but developers, for a variety of reasons, have already deployed workloads across multiple cloud computing environments, added Schwartz. One approach to achieving that goal is to make sure data is stored on the least expensive tiered service possible for any given specific use case, he noted.

    Less clear is whether an uncertain economy might push organizations to consolidate workloads on fewer cloud computing platforms. Even then, the cost of moving a workload is not trivial given the proprietary application programming interfaces (APIs) that many developers have likely invoked as DevOps built and deployed cloud applications.

    Of course, there is no going back; the issue now becomes how to define a set of best practices for managing cloud computing environments in a way that minimizes costs. It turns out that cloud computing can be more costly than an on-premises IT environment simply because the infrastructure resources made available to developers are almost infinite. In the final analysis, however, that can turn out to be too much of a good thing—especially if organizations wind up spending more on IT to support these environments. Many of those environments are not rigorously managed because, for example, developers may not be inclined to spin down a development environment on a Friday because it takes too long to spin it back up the following Monday.

    One way or another, however, a level of long overdue scrutiny of cloud computing costs may be on the horizon.

  • Enfabrica Makes Cloud Networking Performance Advances

    Enfabrica Makes Cloud Networking Performance Advances

    Enfabrica Corp. today emerged from stealth at the MemCon conference to launch a class of processors for networking in cloud computing environments that are optimized for moving data between CPUs, graphical processor units (GPUs), accelerators and memory.

    Enfabrica CEO Rochan Sankar said the Accelerated Compute Fabric (ACF) enables scalable, streaming, multi-terabit-per-second movement of data in a way that also reduces the total cost of cloud networking. That’s critical as the amount of data being generated by applications infused with artificial intelligence (AI) capabilities that require access to larger amounts of data continues to increase, he added.

    At the same time, DevOps workflows will need to evolve as it becomes possible to compose AI fabrics of compute, memory and network resources capable of scaling to tens of thousands of nodes versus relying on top-of-rack network switches, network interface cards (NICs), PCIe switches and CPU-controlled DRAM that creates too many bottlenecks when moving large amounts of data.

    Enfabrica’s first chip, the Accelerated Compute Fabric Switch (ACF-S), supports standards-based interfaces, including multi-port 800 Gigabit Ethernet networking and high-radix PCIe Gen5 and CXL 2.0+ interfaces. The company claims that Enfabrica’s ACF-S is the first data center silicon product in the industry to deliver headless memory scaling to any accelerator by incorporating CXK memory bridging to enable a single GPU rack to have direct, low-latency, uncontended access to local DDR5 DRAM at more than 50 times greater capacity than GPU-native High-Bandwidth Memory (HBM). That approach should enable customers to cut their cost of GPU compute by an estimated 50% for large language model (LLM) inferencing and 75% for deep learning recommendation model (DLRM) inferencing, the company claimed.

    Simulation tests suggested ACF-enabled systems could achieve the same target inference performance using only half the number of GPUs and CPU hosts compared to the latest GPU servers.

    The cost of building AI applications today is prohibitive for many organizations, but as processor advances are made, there may come a time when the cost of building and training AI models substantially decreases. Advances in networking processor technologies will, as a result, accelerate the pace at which network operations are integrated into programmable DevOps workflows versus relying on dedicated networking teams to manage top-of-rack switches.

    It’s not clear yet whether cloud service providers will embrace ACF, but the chip makes it clear there is a significant opportunity to reduce the amount of networking infrastructure needed to build and deploy AI applications.

    Less clear, of course, is to what degree machine learning operations (MLOps) might ultimately be folded into DevOps workflows. While developers of AI models create a unique type of software artifact, they still need to be integrated with application code running in a production environment via application programming interfaces (APIs). One way or another, the total cost of building and deploying applications infused with AI models will need to decline.

    In the meantime, the networking bottlenecks that conspire to make deploying distributed computing applications difficult today may soon become overcome as latency issues are increasingly overcome.

  • Dylibso Releases Tool for Tracking and Validating Wasm Modules

    Dylibso Releases Tool for Tracking and Validating Wasm Modules

    Dylibso today made generally available a system of record for tracking, investigating, searching and validating modules built using the WebAssembly (Wasm) binary instruction format.

    Fresh from raising $6.6 million in additional funding, Dylibso CEO Steve Manuel said Modsurfer exposes the internal contents to enable developers to debug Wasm modules and ensure compatibility with runtime environments.

    Modsurfer provides development teams with a console for accessing a fully auditable database of modules indexed for search and debugging. In addition, development teams have the ability to extract critical information from binary formats in a way that can be integrated with DevOps pipelines. In effect, Modsurfer provides application development teams with X-ray vision into previously opaque Wasm modules, said Manuel.

    Dylibso previously developed Extism, an open source universal plug-in system that supports multiple programming languages used to build Wasm modules. That reduces the level of effort required to integrate Wasm modules and provided the foundation upon which Modsurfer was created, noted Manuel.

    Wasm, at its core, is a portable binary instruction format for building software that describes a memory-safe, sandboxed execution environment. The World Wide Web Consortium (W3C) drove the development of Wasm as part of an effort to create a common format for browsers executing JavaScript code. Wasm is now being extended beyond browsers and JavaScript to enable developers to create a set of universal binaries that could work on any platform without modification.

    That approach replaces the current predominant method for building software that relies on the aggregation of software components that tend to lack distinct boundaries between them. One of the issues of that approach is that it becomes relatively simple for malware to infect all the components of an application.

    It may be some time before WASM becomes the preferred artifact for building applications, but it’s clear the walls that have separated one IT platform from another are finally starting to crumble. Once applications become much more portable, the longstanding goal of writing an application once and then deploying it anywhere may finally be achieved. That’s not only critical in terms of overall application developer productivity; it also has major implications in terms of streamlining the management of DevOps workflows as it becomes easier to both initially deploy applications and continuously update them.

    As organizations continue to review their application development practices to better secure software supply chains, it’s also become apparent that legacy approaches to building applications are fundamentally flawed. Rather than attempting to reengineer processes to encourage developers to better secure applications, it may be more effective to change the way applications are built. After all, considering all the processes required to secure legacy development approaches, it may be cheaper and easier to adopt a new method for building applications that lowers overall risk, said Manuel.

    Of course, the amount of Wasm expertise that exists in the developer community is still limited. However, DevOps teams should expect to see more Wasm modules moving through DevOps pipelines as Wasm tooling continues to improve.

  • Understanding Cloud APIs

    Understanding Cloud APIs

    Six out of every 10 businesses had moved their workloads to the cloud as of last year. More steady traffic in the cloud and more direct cloud instances mean more to juggle–and the need for greater customization and integration. That’s where APIs come in. APIs are doing for the cloud what they did for on-premises resources: Segregating, simplifying, specializing and integrating services across a network–only this time, it’s future-proofing the architecture by building it in the cloud. As one cybersecurity researcher quipped, “Everyone wants to use APIs to build innovative solutions, but no one wants to invest in building them.” Cloud APIs will soon spell the difference between companies that can keep up with digitization and those that can’t.

    Ultimately, it will come down to which organizations take the time to understand them and use them to their best benefit.

    What is a Cloud API?

    Simply put, “a cloud API is a software interface that allows developers to link cloud computing services together.” APIs allow services to share functionality and information for other programs to use, and nowhere is this more important than in the interconnected ecosystem of the cloud.

    Because APIs act as a waiter in a restaurant, the better the waiter, the simpler ordering will be for you and for your customers. The best cloud API connects backend cloud components, services and platforms and does all the busy work so all your users see on the front end is a seamless customer experience. Since most of the work of tomorrow will be in the cloud (and is moving there in ever-increasing numbers), to not build up this functionality now is to take a deliberate risk.

    Types of Cloud APIs

    There are several types of cloud APIs based on the four major areas where cloud computing will require integration–either with another platform or another cloud provider.

    ● PaaS APIs (Service level): These platform-as-a-service APIs are designed to integrate with databases, portals, storage components and messaging systems, providing access and functionality in the cloud.
    ● SaaS APIs (Service level): Referred to as software-as-a-service APIs, these connect application layer resources with the cloud and underlying IT infrastructure. A CRM is a good example of where these types of APIs can be used to create a “cloud application extension” for your ecosystem.
    ● IaaS APIs (Service level): Infrastructure-as-a-service APIs control the distribution and deployment of specific cloud resources, performing such tasks as rapid provisioning and de-provisioning, network configurations and workload (VM) management in the cloud.
    ● Cross-platform APIs: Because many environments use multiple cloud providers and platforms, cross-platform compatibility is required. Cross-platform APIs streamline data management by allowing tenants to access their cloud-based resources and workloads from any of their cloud providers, not just their primary one. They provide a higher level of abstraction by translating a specific provider’s API calls into a more generic form, making accessibility, manageability and interoperability in the cloud easier.

    Cloud API Security

    In many respects, the security threats faced by cloud APIs are the same that any cloud-based resource would encounter and present the same challenges. That being said, malicious API traffic surged over 117% last year, from an average of 12 million to over 26 million calls, according to one industry report. The same study revealed that over a third of respondents experienced authentication and data exposure-related incidents with their APIs, and 54% delayed rolling out a new application because of API security concerns.

    Unguarded, a cloud API could present the keys to the kingdom and allow an attacker to access the hub of not just one but several cloud providers and their workloads, resources and assets. Protecting cloud-based APIs with standard authentication and authorization practices such as OAuth2.0 and OpenID Connect can reduce the chance of a lateral attack.

    Why Cloud APIs

    Cloud usage is not set to slow down and will soon become ubiquitous. Companies that aren’t on it are migrating their workloads in greater numbers and entrepreneurial ventures are spinning up as cloud-native. Cloud APIs are simply the logical response to the complexities that come with the terrain. Without cloud APIs, the user experience will be negatively impacted and will only become more apparent when compared with companies that do use cloud APIs.

    For companies looking to innovate in 2023 and release new applications, APIs are the way to cut-and-paste the process safely. Similarly, as more cloud-based services become API-reliant, application reusability is another factor requiring cloud APIs. And lastly, the complexity of cloud products, platforms and ecosystems (even within one organization) makes cloud APIs a necessity for smooth interoperability. And they help with scalability–multiple components of a cloud-based service can be parsed out, updated and patched individually and refined separately so that one area can develop without being dragged down by the whole. Cloud APIs allow for that granular level of customization that can help an organization refine its edge.

    Simply put, organizations will need these APIs to not only compete but stay relevant as businesses continue to take their fight to the cloud.

  • Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco has reduced the number of licensing options for its Intersight operations platform to two as part of an effort to make the software-as-a-service (SaaS) platform for managing on-premises and cloud computing environments more accessible.

    In addition, Cisco has added a Cisco UCS X-series Modular System to its portfolio based on a two-socket server configured with 4th Gen Intel Xeon Scalable processors and an instance of Intersight that has been extended to make it possible to apply policies that limit energy consumption.

    Mahesh Natarajan, senior director of product management of cloud and compute for Cisco, said Intersight is a complement to the management platform the company already bundles with its Unified Compute Systems (UCS) platforms for on-premises IT environments.

    Essentials enables management of server profiles/policies, managing firmware updates and proactive health monitoring and security advisories along with many other capabilities. The Advantage option adds low-code/no-code orchestration, more advanced server operations, multi-domain visibility and third-party integration with, for example, third-party IT service management (ITSM) platforms.

    In the wake of the COVID-19 pandemic, there’s been a shift toward cloud-based platforms that make it simpler for IT teams to centrally manage multiple IT environments from anywhere. Cisco Intersight, in addition to supporting Cisco servers, has been integrated with cloud services from Amazon Web Services (AWS), Google and Microsoft to achieve that goal.

    In fact, as the management of IT continues to converge across multiple platforms, the concept of a data center has become something of a misnomer, said Natarajan. Integrated application workloads are now routinely distributed across on-premises and cloud computing platforms that are simpler to manage using a consistent set of policies, he added.

    A SaaS platform also provides the added benefit of reducing the total cost of IT because the need to deploy management platforms for each environment is sharply reduced, noted Natarajan.

    Very few organizations are running all their application workloads in one environment, so the need to reduce costs by finding more efficient ways to manage IT operations is becoming a more pressing issue. Most IT teams are trying to limit the number of full-time employees they might otherwise hire by trying to improve the overall productivity of the teams they do have.

    Of course, centralizing the management of IT is as much a cultural challenge as it is technical. Each IT team that deploys a workload on a platform has a natural bias toward using the management framework made available by the platform provider. Each of those platform providers is now extending its reach as part of an effort to become the one control plane that rules them all. Cloud service providers, for example, are extending the reach of their management frameworks into both local data centers and edge computing environments.

    Regardless of preferences, the cost of licensing multiple management platforms can quickly become prohibitive, especially if they provide redundant capabilities. Organizations looking to reduce the total cost of IT would much rather reduce licensing costs than lay off the IT staff that drive a wide range of digital processes. The challenge, as always, is getting everyone in the IT organization to agree on what that one management platform should be.