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Tag: hyperscale

  • The Inevitability of Multi-Cloud-Native Apps

    The Inevitability of Multi-Cloud-Native Apps

    We all know the story: Enterprises moved to the cloud. They learned about data center virtualization and multi-tenancy and self-service developer provisioning. Along came Kubernetes, and they moved to cloud-native, where they learned about automation, DevOps and infrastructure-as-code (IaC). Now, even though most enterprises are still figuring out how to do cloud-native at scale, there’s a new imperative they must master: Multi-cloud-native applications. And for almost every enterprise with a global footprint, the move to multi-cloud-native applications is not optional—it’s inevitable. 

    But before we explore why this is the case, let’s first retrace our steps. For almost every enterprise out there, the journey to the cloud started with adopting a public or private self-service infrastructure provisioning and management model. These enterprises quickly realized that this new IT model required a new set of tools to solve challenges they’d never before faced—enabling their developers with self-service provisioning, managing and operating infrastructure. Enterprises figured it out, but as soon as they figured out the cloud, they quickly had to gear up for the second phase: Automating and operationalizing an enterprise-wide rollout of cloud operating models throughout their entire developer organization. 

    Of course, this phase has its own set of challenges, and a rich ecosystem of tools emerged to address them. Even so, we all can agree that the majority of mainstream enterprises are still in this second phase. They’ve adopted cloud, they’ve expanded its use, and they’re grappling with the challenges around multi-tenancy, security, DevSecOps and governance. Hand-in-hand with this goes a whole new set of challenges around cost management. Suddenly, enterprises are faced with managing the variable costs of public cloud infrastructure: Optimizing compute infrastructure, utilization and capacity to rein in cloud spend. It’s an issue Sarah Wang and Martin Cassado took head-on in their research on the Trillion-Dollar Paradox.

    So, enterprises moved to the cloud and are now, for the most part, successfully grappling with scaling it. But nothing stays the same for long, and businesses drive IT to innovate. This is where cloud-native entered the picture to help enterprise software development teams become more agile and help businesses transform to deliver new services and experiences faster. In fact, the consumer experience this model makes possible has rapidly become the norm.

    Consistently delivering rapid software iteration across a global footprint forces DevOps organizations to grapple with an entirely new set of technical challenges: Leveraging containerized applications and microservices architectures in production across multiple Kubernetes clusters running in multiple geographies. Customers want an on-demand experience. This third phase is what we call multi-cloud-native, and it was pioneered by hyperscale IaaS players like Google, AWS, Azure and Tencent.

    The reality is, of course, that hyperscalers aren’t the only ones who have figured out how to deliver multi-cloud-native apps. Webscale innovators like Doordash, Uber, Twitter and Netflix have done it, too. To get there, they had to make and share their multi-cloud-native apps across every geography where their customers live. And, in turn, to make that happen they had to tackle a new set of challenges: Develop new tools and techniques like geographically distributed, planet-scale databases and analytics engines, application architectures that run apps on the backend close to the consumer in a multi-cloud-native way. This enabled them to do arbitrage across fleets of infrastructure capacity across regions, all while giving their customers that instant experience they increasingly grew to expect.

    What enterprises must realize is that this experience consumers have grown to expect from webscalers is rapidly becoming the same experience they expect when accessing their bank account, their insurance apps and their tax records. For almost every planet-scale enterprise, this sets up a whole new set of challenges: To span multiple clouds and multiple geographies running multiple Kubernetes clusters, you now must worry about new ways to do autoscaling, self-healing, disaster recovery, operations management and cost optimization—all of which are things that you’re just now getting figured out for virtual machines (VMs) and clusters in single regions.

    Enterprises must face the reality that cloud-native application models are not the destination but rather a waypoint on a journey to becoming multi-cloud-native. Because the consumer experiences delivered by webscalers make this the inevitable destination.

    The Enterprise Journey: Cloud, Cloud-Native and Multi-Cloud-Native

    Don’t take my word for it.

    Uber and Twitter public disclosures describe how the multi-cloud-native model is an integral part of their strategic game plan. Hybrid and multi-cloud are part of this, too, leveraging Kubernetes as a cluster manager, but they still need tools to manage all those clusters across multiple environments in multiple regions at planet scale.

    The bottom line is that the architecture that webscale companies are using today is the same one enterprises will need to adopt tomorrow to remain competitive. Alternatively, these enterprises will cede market share and relevance to the next webscale innovator to disrupt an industry. In adopting this model of building multi-cloud-native apps, enterprises will have to deal with unified access management, controlling multiple clusters, resource management and more, all across multiple regions and clusters of capacity, aggregating versus disaggregating cluster resources, using logical services that span clouds versus namespaces that span a single cluster.

    The Enterprise Solution: Planet-Scale Cluster Manager

    They will need a planet-scale cluster manager to do this; something that can observe costs, operations, performance and SLAs across clusters, across public and private clouds, spanning VMs and Kubernetes through a single pane of glass so they can operate above the clouds in an automated, easy-to-use way. Band-aid solutions like running multiple spreadsheet models to figure out how to optimize are not scalable. What’s needed are tools like machine learning to automate and optimize at scale, bin packing technology to intelligently and automatically optimize across a planet-scale infrastructure fleet and high levels of automation either with humans in or out of the loop. That’s how hyperscalers and webscalers are already doing it.

    Every planet-scale enterprise is multi-cloud-native, running data-driven apps. It’s not a fiction: It’s already happening across webscale companies, not just hyperscalers. Multi-cloud-native is the path that every enterprise is going to need to follow if they want to transform themselves to compete with the next webscale upstart. The good news is, the pioneers have left us a map to follow.

    To hear more about cloud-native topics, join the Cloud Native Computing Foundation and the cloud-native community at KubeCon+CloudNativeCon North America 2021 – October 11-15, 2021

  • The Real-Time Innovation Dividend

    The Real-Time Innovation Dividend

    Time is compressing. The attention span of individuals and organizations is becoming ever shorter. There are only brief windows that can be devoted to any given transaction.

    Expectations around situational knowledge, updated in real-time, are compounding. Customers expect the applications they interact with know where they are, what they are looking for and what their preferences are in terms of price, value, design and timeliness of delivery. If the proper options aren’t provided immediately, the customer will move on to another provider.

    While the time available to an algorithm or application is contracting, the complexity of the tasks for successful interactions is increasing. Digital businesses must do more in ever shorter time windows. Differentiation in the digital world has a lot to do with a deep understanding of the customer based on multiple data streams and driving decisions that provide a deeply personalized experience.

    In business, we need to have a current picture of our situation. We build models and analytic dashboards, but these fall short in allowing us a real-time view. The latency of these models may be measured in seconds, minutes, hours, even days and weeks. As latency expands, the picture is not of the moment and that difference defines operating risk. The phrase “time is money” has never been more accurate.

    Brokerages require as close to a real-time view of positions allowing them to manage risk more closely, in terms of their aggregate position, based on a transactionally correct view of precisely what they have on their books to respond in real-time to global market changes.

    For retailers, it means knowing the content a customer has seen and the environment where they encountered it. Did they just come from a brick and mortar store? What digital journeys have they been on? What online content have they been perusing? Continually adaptive models use the answers to these questions to select active customers and recommend options that address what they are searching for.

    The digital shopping experience is composed of multiple linked processes, working in concert to continually understand the customer experience. Different system elements–user profiles, decisioning, recommendation engines and machine learning and AI–require data across different time windows. Some of these systems deal with terabytes of data and some petabytes. Some require absolute consistency and for some eventual consistency is good enough. In many of these systems, there is a demand for hyperscale within tight time windows.

    The ability to take in more data within a given time window feeds the currency of the context. It is not enough to have a lot of data, but you also need the most current view of that data. Operating at hyperscale enables an organization to deliver exponentially more data than traditional databases. This makes models more accurate and, by extension, more valuable.

    Scale Plus Speed Equals More Time

    In some sense the combination of scale with speed creates time, giving you the ability to do more within a given time window. Consider this a dividend, allowing the use of more data or allowing the process to be completed in less time.

    The systems that interact directly with either customers, employees or clients are edge computing systems, pulling in and processing data within relatively tight time windows. Edge systems may have expiry windows where data is deleted after a given time window where it is deemed to be stale.

    The data collected and processed at the edge may be moved to a system of record or core transactional store and kept for longer periods providing historical context. These data sets may grow to petabytes in size while still needing to support response times measured in milliseconds. Meeting these requirements requires hyperscale capability, where the performance of data access does not degrade as the repository of information grows.

    Here’s a real-life example. We must match the information collected regarding devices, identity and patterns that establish context against our database of user profiles. We want to run more sophisticated recommendation engines based on these patterns to ensure we are putting the closest thing to what the customer wants in front of them. We also want to make pricing decisions in real-time, to gain the largest basket at the best margins. We may have established a model that tells us customers with this profile are likely to make additional purchases after this transaction, so our analysis and optimization boundaries go beyond just this transaction. We will want to log this so we can track this behavior to validate the model. We have these processes going on, some in parallel, others in sequence:

    • Establish an identity profile of the customer.
    • Match that profile against a database of profiles that models likely desires and behaviors.
    • Based on that match, determine the products and/or services to recommend.
    • Select the content and media types based on the user profile.
    • Determine pricing based on the user profile and the transaction profile.

    The last element, real-time pricing, is something we want to modify to take into account the margin and profitability of the customer over time. We also know that if this impacts the overall window of the transaction the customer has a higher likelihood of abandoning the transaction and we can lose out on not just this transaction, but a whole set subsequent to this one.

    Doing Better for Both the Customer and the Business

    By building models based on hyperscale capability, 10 milliseconds are saved here, another 20 milliseconds there. This creates the time bandwidth in the overall process to add the notion of optimizing pricing around not just this transaction, but the value of this transaction profile and the relative margins of the various components in this sale and other subsequent sales that are likely to happen. We have created time to do this additional processing and do better–better for our customers and better for our business. And we know that as we add more data points to our profiles and scale up to include more data overall, we won’t experience performance degradation. These time benefits will remain. This is the real-time innovation dividend.

    — Lenley Hensarling

  • Go Big: Scalability in the Age of DevOps

    Go Big: Scalability in the Age of DevOps

    Achieving hyperscale is a work of technical art for any business. As companies grow and customers begin to rely more heavily on your service, ensuring reliability during peak business times is mission-critical. An enormous amount of work goes on behind the scenes to ensure scalability, and oftentimes it becomes necessary to redesign or rethink processes and systems to improve them as they face increased pressure.

    When customer demand reaches its apex, it’s the ultimate test for any organization’s systems and processes; therefore, it’s important to prepare a comprehensive strategy that focuses on scalability, reliability and hypergrowth in a targeted, calculated and data driven methodology.

    The first step on your path to hyperscale is forecasting and gathering data analytics around business and growth patterns for a full year to make smarter forecasts. Set aside time each day throughout the year to go over the allocation of your data and analytics, measuring them for accuracy to see how they stack up to your current predictions for that specific day. This may seem taxing and cumbersome when engineers have other priorities to work on, but devoting time to this in the early months of the year will help you be that much more accurate when it comes to predicting the needs of your customers and how you should go about scaling to meet those needs.

    Especially during the holiday season, it’s important to have a minute-by-minute projection of how much traffic you expect and need. That’s where forecasting and an advanced data science model come into play. A common mistake made when it comes to scalability is not getting granular enough about the real pressure points for your systems. Having accurate projections and forecasts in place that feed into your data science model means you can better test your systems and production limits while gaining greater insight into meeting your scaling points. If you find fragilities ahead of time through this, you can assign your engineering team to fix them well in advance.

    Another way to truly bolster your scalability efforts is by introducing an operability development model. This entails constantly deploying some controlled chaos into your systems to see how they really stack up and function under pressure, with the goal of up-leveling the capacity of both your operations and system growth. The operability development model allows you to really test the grit of your systems by squeezing the most out of them, prepares you to respond quickly and methodically should the worst case scenario occur and sets you down the path toward continued performance growth and scale.

    From here, you’ll want to institute a preventative maintenance model. Think of it like getting an oil change for your car. Instead of waiting until the very moment you need your software to perform exceptionally, start taking a look at the little things that could or are causing problems ahead of time and fix them on a monthly or quarterly basis. A preventative maintenance model helps you inspect, adapt and solve these little nuisances before they become serious problems, granting you the cleanest operating environment possible.

    Scalability is king, but availability and reliability are arguably just as critical. Improving availability means reducing latency and getting as close as you can to where your customers utilize your services. This can be achieved by deploying locations around the world to make connections much faster and services more independent. To maintain reliability, you should have processes in place that can quickly respond and offer support to customers 24/7 to maximize consistency and performance.

    Another strategy to consider—if it makes sense for your business or if you’re evaluating cloud providers as a growth option—is to double down on your efforts by re-architecting your platform and systems as cloud native from the ground up to support greater levels of scalability and cost efficiency. By going cloud-native, you can achieve independently scalable services so that you don’t have to scale all of your services together. This means your engineers and developers don’t have to spend way too much time figuring out where issues and fragilities occur.

    A platform architected as cloud-native allows you to leverage microservices and containerization, creating an environment with more reliable, self-healing, high-tenancy and auto-scaling features that take the most advantage of your hardware and services. An added benefit of a cloud native platform is that you can use your data to find the most opportune times to both scale in advance and descale based on performance and customer expectations. The efficiencies that come from re-architecting as cloud native enable engineers to spend more time on automating software and the delivery pipeline, which is a huge boon for scalability.

    At the end of the day, a well-thought-out preparation strategy for mass scalability makes the whole process much more exciting and obtainable. It also greatly reduces lead-up anxiety by getting a head start on planning so that you have more time to sit back and enjoy the fruits of your labor. Lastly, a yearlong scalability strategy enables you to experiment and try new things without the fear of failure. Testing new approaches ahead of time to see what does or doesn’t work will only help you learn and make your scalability efforts that much more effective for the future as your company continues down the path of hypergrowth and hyperscale.

    — Jamie Tischart

  • Radisys Introduces DCEngine – Hyperscale Data Center Infrastructure Solution

    SAN JOSE, Calif.–(Business Wire)–Radisys® Corporation (NASDAQ:RSYS), the services acceleration company, today launched DCEngine™, the newest addition to Radisys’ portfolio of solutions designed to help service providers solve the complex challenges of next-generation networks. Based on principles evangelized by the Open Compute Project (OCP), together with enhancements designed specifically to meet the unique needs of communication service providers, DCEngine provides a disruptive cloud platform that goes well beyond software-defined networking to create a truly open, software-defined services delivery infrastructure that fully embraces open source hardware and software technologies and offers DevOps agility to service providers.

    Radisys has already received initial orders and began shipping DCEngine into one of the world’s largest mobile operators late in 2015 as this operator works to evolve its infrastructure to a DevOps-enabled data center environment. Additional meaningful deployments are anticipated as this operator continues to build out its infrastructure, with other trials with service providers and cable operators around the globe expected to accelerate over the course of this year.

    “Radisys’ 25-plus years of communication software DNA, coupled with our leadership in open telecommunication platforms, positions us as the ideal partner for communication service providers looking to evolve from existing central office architecture to the cloud,” said Brian Bronson, President and Chief Executive Officer at Radisys. “DCEngine’s seamless scalability and efficient lifecycle management, coupled with our proven track record of understanding and addressing the needs of the service provider, positions Radisys to be a leader in enabling the evolution of next-generation operator infrastructure.”

    “As Verizon introduces open, flexible technology that paves the way for central office transformation, we look to companies like Radisys to assist us in that journey,” said Damascene Joachimpillai, architect, cloud hardware, network and security, Verizon Labs. “Network modernization will rely on solutions such as DCEngine that meet service provider needs with open source hardware and software technologies.”

    DCEngine offers:

    • A pre-installed, ready-to-deploy frame inspired by OCP as an open architecture for Radisys and third-party partners. This open systems approach helps to propagate the DCEngine innovations through the open source community and allows operators to avoid vendor lock-in typical of alternative rack solutions.
    • Unparalleled performance and scalability through a collection of easily deployed and serviced compute, storage and networking sleds. DCEngine leads the market in terms of density in a single rack with up to 2.4 petabytes of storage and up to 152 Intel® Xeon® class processors in a standard 42 RU frame.
    • A flexible software and management framework enabling the most demanding service provider workloads. This framework is based on leading open source projects including the Open Network Operating System (ONOS) and Central Office Re-architected as a Datacenter (CORD) initiatives being led by On.LAB.
    • A design that meets next-generation central office and service provider data center specifications, including NEBS temperature, EMI and seismic requirements where required.
    • Professional services and cradle-to-grave lifecycle management of DCEngine hardware and fully validated and supported software stacks, including design, installation, configuration, deployment and ongoing maintenance and validation.

    The convergence of IT and service provider domains, coupled with new economic pressures and service challenges driven by 5G, IoT and OTT are forcing communication service providers to rethink their network and cloud computing architectures. DCEngine is designed to help service providers address their unique cloud requirements and deliver on a new era of rapid service delivery and flexibility. DCEngine’s low cost infrastructure provides a platform for unified workloads and the elimination of purpose built-hardware traditionally deployed for specific workloads and applications. DCEngine enables services to be deployed much more quickly by decoupling those services from the underlying infrastructure and offering an adaptive, software programmable platform that reduces costs, streamlines operations and increases efficiency in the service provider domain.

    “Radisys has developed an open, vendor agnostic solution for central office transformation,” stated Michael Howard, Senior Research Director and Advisor, Carrier Networks, IHS. “Its DCEngine has a disaggregated and flexible data center architecture and Radisys set its platform apart by designing DCEngine with the concept of 3rd-party open source hardware and software, while meeting telco NEBS and environmental requirements where necessary. We believe that operators will want to closely examine the DCEngine approach for Central Office transformation projects.”

    Meet with Radisys’ DCEngine Experts at ONS 2016

    In addition to participating at this week’s OCP U.S. Summit, Radisys will be exhibiting and speaking at the Open Networking Summit (ONS) 2016 in Santa Clara, California, March 14-17. To learn more or to schedule a meeting with Radisys’ experts, visitwww.radisys.com/ons-open-networking-summit-2016.

    About Radisys

    Radisys helps communications and content providers, and their strategic partners, create new revenue streams and drive cost out of their services delivery infrastructure. Radisys’ hyperscale software-defined infrastructure, service aware traffic distribution platforms, real-time media processing engines and wireless access technologies enable its customers to maximize, virtualize and monetize their networks. For more information about Radisys, please visit www.radisys.com.

    Radisys® is a registered trademark of Radisys. All other trademarks are the property of their respective owners.

    Radisys
    Ray Adensamer, +1-604-918-6318
    Director of Marketing
    ray.adensamer@radisys.com