Tag: scale-out infrastructure

  • 5G Security: Mitigating Threats in the Era of Hyperspeed and Hyperconnectivity

    5G Security: Mitigating Threats in the Era of Hyperspeed and Hyperconnectivity

    5G is one of the hottest industry buzzwords right now, and for good reason. Beyond its ability to deliver massive capacity, it is expected to be one of the fastest and most pervasive technologies of all time, fundamentally changing every sector of the economy.

    In recent years, we’ve lived through 3G and 4G. So what makes this next generation so revolutionary? Put simply, every emerging technology we are collectively excited about—IoT, VR/AR gaming, autonomous cars and smart cities—is enabled through 5G. This is because this next generation of the mobile network will increase the speed of service by extending high bandwidth and connectivity to the edge, a possibility that wasn’t quite there with 4G. 5G will connect everything around us via a network that is 100x faster than our current cellular connection and 10x faster than home broadband service.

    The technology is ready. From a commercial and business perspective, however, 5G implementation is still in its infancy. When it comes to capitalizing on the many use cases 5G offers, what is the holdup? With faster speeds, enormous data capacity, lower latency and more devices connecting and communicating than ever before, the security vulnerabilities are extreme. Given we are in the early stages of 5G deployments, service providers do not even know the full extent of the security concerns; they are just planning for the number of devices joining the network to grow exponentially. With the proliferation of connected personal devices, data privacy concerns have never been higher.

    To understand the potentially detrimental threats to personal safety and data privacy, we must first look at specific use cases enabled through this technology.

    The 5G Effect

    5G is more than just faster speeds—it is unlocking some of the most highly sought-after technologies of the decade. This is because low latency, or next-to-nothing lag time, allows a device asking something of the network to get a response almost instantaneously. For VR and AR gaming applications, this means a seamless user experience. And while sensory video games are good fun, these same principles can carry over to more serious applications in hospitals and other medical settings: By using a VR headset and special glove to control a robot arm, for example, a surgeon may have the ability to operate on a patient offsite.

    This extreme scenario offers two major risks: network lags and hacked equipment. An IoT-enabled hospital may have tens of thousands of “things” that could be compromised. Emergency room devices cannot afford to be starved for bandwidth if an admin printer is clogging the network. Any glitch in the network may lead to a slight delay in action, resulting in dangerous consequences. Additionally, with nefarious entries into the network through the high volume of vulnerable endpoints, the attack may have dire repercussions.

    A hospital is just one high-stakes environment. Now consider the IoT devices connected to a 5G network operating all around us—autonomous cars, energy grids, home security systems, etc.—the smallest glitch or endpoint vulnerability could lead to life-threatening situations.

    Many people do not realize 5G and IoT will have a major effect on seemingly innocuous devices such as refrigerators, thermometers and even light bulbs. While 5G will enable users to check on these types of appliances remotely through a simple app, convenience comes with consequence as these devices can also be usurped by malicious characters. It is important to recognize what 5G can enable, but also how it will all come together.

    Preparing for the Next-Gen

    Here are some tips on how enterprises can develop an effective 5G-ready security posture that encompasses the entire network:

    Invest in automation: 5G security poses its own Catch-22; because connections happen faster, attacks and breaches also happen faster and potentially with much more reach. To mitigate threats, 5G security will require actionable insights at scale. To achieve this, organizations need security systems that detect and automatically enforce security policy from endpoints to edge and every cloud in between.

    Scale up and out: To avoid a massive bottleneck, a proper 5G security posture will require upgrades to both physical and virtual infrastructures. Developing a secure architecture and solution that can keep up with the pace and scale of 5G without slowing it down is a large, but necessary, undertaking. The trick is to build in machine learning capabilities so the network can get smarter. Malware must traverse the network, so why not use all the data that the network already produces to fight it? A network that is constantly learning and updating is the only way to operate in the 5G landscape.

    Be proactive: History has shown us that whenever we expand our computing power and connectivity capabilities, we also expand the threat landscape. Not only that but as we continue to increase our dependency on communications networks and technologies to move tremendous amounts of data, there is greater potential for serious disaster should they be compromised. This is not the time to sit back and watch how things unfold, as major security risks are on the line.

    The use cases for 5G are plentiful. If we can conquer the threats, we can conquer anything the technology enables us to do—and the possibilities are endless.

    — Samantha Madrid

  • Managing Complexity in Dev/Test Infrastructure Using Machine Learning

    Managing Complexity in Dev/Test Infrastructure Using Machine Learning

    Businesses are increasingly developing software solutions that can drive innovation and digital transformation. As the number of developers increases in the organization, the ratio of developers to IT also increases over time. The net result: more dynamic and complex dev/test infrastructure requirements and slower support from IT.

    Developers and testers often cannot get their hands on infrastructure environments in a timely manner to accelerate delivery of software applications to their customers. They have to deal with lack of timely support from IT and long waits for hardware resources and for provisioning and configuring of the environments.

    How does engineering IT manage this complexity and deliver more agility to their engineering stakeholders?

    Provide Self-Service Creation of Dev/Test Sandbox Clouds

    Engineering IT teams can increase the velocity of development and testing if they enable self-service ability for dev teams to quickly provision their own lab infrastructure and application stacks at a click of a button.

    Self-service, API-driven infrastructure is a fundamental requirement for enabling developers to write code (which can be done using their favorite programming language) and make RESTful API calls to the underlying programmable infrastructure. This allows them to dynamically and automatically manage initial deployments and configurations as well as manage ongoing, automated dynamic provisioning of infrastructure, autoscaling, monitoring and alerting.

    All this automation removes the confusion and error-prone manual steps for the entire application delivery process, including development, testing, staging and production deployments. This, in turn, accelerates software delivery and increases quality.

    Additionally, if self-service is available as a SaaS-based delivery model, it is easy to add more features and workflows very quickly without having IT doing a major upgrade.

    Breaking these silos improves collaboration between teams, accelerates velocity of software development, improves infrastructure utilization, increases overall operational efficiency and reduces costs.

    Eliminating Silos in the Dev/Test Infrastructure

    Silos are common in companies where various specialist teams (storage, networking, security) form fiefdoms around their respective functional areas. Silos impede velocity because they lead to complexity of operations, lack of consistency in the environment, and lack of automation.

    Automating across silos turns into to an exercise of custom scripts and lot of “glue and duct tape,” which makes maintenance and change management complex, slow and error-prone.

    Breaking these silos improves collaboration between teams, accelerates velocity of software development, improves infrastructure utilization, increases overall operational efficiency and reduces costs.

    A hyperconverged cloud design with a software-centric, scale-out architecture tightly integrates compute, storage, networking and virtualization resources and other technologies from scratch in a commodity hardware box supported by a single vendor. Also, companies can keep costs under control by leveraging scale-out cloud designs that make it easy to start small, grow based on demand, and stay close to the right size and customer demands.

    Additionally, development teams should have the ability to quickly deploy/clone/share complex multi-tiered application stacks between various teams and between development and testing, breaking the silos within the development organization.

    Automate Operations, Monitoring and Patching

    Engineering IT teams need to have complete visibility and control of their entire stack from the infrastructure up to applications. They need intelligent software to monitor the hardware and software stack, manage large-scale clusters and automatically handle routine but time-consuming and complex operations such as failure handling, patching, security updates and software upgrades.

    Running a service with traditional sysadmin teams who execute the above activities manually becomes expensive—especially if they operate in server, storage, networking and security silos—as the dev/test environments become more dynamic and the demand for more environments and projects grows.

    However, it is possible to use an intelligent private cloud platform that leverages hyperconverged scale-out designs, machine learning software and a SaaS-based operational console to reduce complexity and increase the agility of dev/test teams.

    Cloud-based monitoring and advanced analytics dramatically reduce the need for experts in different parts of the infrastructure, scale linearly as the size of the operation increases and cut operational complexity by 90 percent.

    Manage Resource Management Using Machine Learning

    Applying machine learning to infrastructure management means intelligent software could learn about operational patterns, anticipate capacity needs, raise alerts about security anomalies, self-monitor and self-heal your environment in the face of failures, intelligently apply security patches and automatically upgrade hardware and software systems without any downtime. The next generation of infrastructure management will be driven by advances in machine learning and artificial intelligence, where the infrastructure is able to basically “drive itself” with minimal user intervention.

    This will help engineering IT teams optimize resource usage and capacity based on current and future dev/test demand and also be able to better handle the availability and performance they can deliver to their engineering teams. A lot of efficient resource management comes down to capacity planning, utilization monitoring, right-sizing of workloads, demand forecasting and detecting zombie VMs and unused resources.

    Demand forecasting and capacity planning can be viewed as ensuring there is sufficient capacity and redundancy to serve projected future demand with the required availability. Capacity planning should consider organic growth, which stems from natural service adoption and usage by dev/test teams. Having intelligent predictive analytics and machine learning can help greatly with accurate forecasting, alerting and providing lead time for acquiring additional capacity.

    Better insights into how the infrastructure is performing can also help in fine-tuning performance of end user workloads. For example, an intelligent system that is monitoring a workload for storage performance might recommend using SSDs instead of spindles to increase the IOPS and improve workload responsiveness.

    An intelligent private cloud platform that uses hyperconverged scale-out designs, machine learning software, and a SaaS-based operational console can reduce complexity and increase the agility of dev/test teams.

    About the Author / Kamesh Pemmaraju

    Kamesh Pemmaraju leads product management, product marketing, and technology partnerships at ZeroStack. Kamesh is a seasoned product management/marketing leader with a strong engineering and consulting background. In his most recent product roles at Mirantis and Dell, he focused on delivering private and hybrid cloud solutions to enterprises. He holds a Master of Science in Computer Science from Indian Institute of Science. Connect with him on LinkedIn and Twitter.