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Tag: AIOps

  • ServiceNow Partners With IBM on AIOps

    ServiceNow Partners With IBM on AIOps

    ServiceNow and IBM this week announced that the Watson artificial intelligence for IT operations (AIOps) platform from IBM will be integrated with the IT service management (ITSM) platform from ServiceNow.

    Pablo Stern, senior vice president for IT workflow products for ServiceNow, said once that capability becomes available later this year on the Now platform, IT teams will become on average 20% more productive. This is good timing, as most teams currently are struggling to adapt to the increased demand for IT support from employees working from home and in the office in the wake of COVID-19 pandemic.

    Mike Gilfix, vice president of cloud and integration and chief product officer for Cloud Paks at IBM, said IT teams will also be able to proactively identify anomalies in IT environments 60% faster.

    IT teams interact with IBM Watson AIOps via a chatbox interface such as Slack. The platform will surface recommendations based on ServiceNow data and enable IT teams to query IBM Watson AIOps to provide a faster mean time to resolution for IT incidents, said Gilfix. That capability is provided via a configuration management database from ServiceNow that tracks the operational state of all business services in real-time.

    IBM Watson AIOps, which can be deployed in the cloud or on-premises, also can surface additional context the next time similar issues arise, in addition to being able to record actions and insights for auditing purposes. IT teams can also feed data from other systems into IBM Watson AIOps to automate a wider range of processes.

    AIOps platforms won’t be replacing IT professionals anytime soon. However, IT teams that are not able to access AIOps platforms will find themselves working harder than their peers. As IT environments become more complex, the number of potential issues that can adversely impact end user experience are moving beyond the capabilities of an IT team’s ability to track manually.

    AIOps platforms, meanwhile, continue to advance in sophistication. Given the structured nature of IT environments, IBM Watson AIOps is now able to have a significant impact on productivity with 24 hours of being deployed, Gilfix said. As part of this launch, IBM is expanding its global ServiceNow services offering to include additional capabilities that provide advisory, implementation and managed services on the Now Platform. IBM also has formed an AIOps Elite Team that will help prepare organizations to transition to AIOps free of charge.

    IT may take a while still for IT teams to adjust culturally to AI, but there are many rote tasks that many IT professionals would like to see eliminated. Over time those tasks conspire to increase the level of drudgery IT professionals experience as they perform the same repetitive tasks. In theory, at least, AI should reduce overall IT staff fatigue in a way that could reduce turnover while at the same time freeing up resources to tackle more complex IT challenges.

    It may take some time for IT teams to trust the recommendations surfaced by an AIOps platform, but the more those platforms learn about the IT environment, the more accurate those suggestions become.

  • Datadog Allies with BigPanda on AIOps

    Datadog Allies with BigPanda on AIOps

    BigPanda Inc. and Datadog today announced they will integrate their respective platforms to make it easier to form artificial intelligence for automating IT operations (AIOps) to consume data generated by the Datadog IT monitoring service. The two companies are pledging to invest in a go-to-market partnership to create a comprehensive platform for monitoring, analytics and incident management.

    As part of that effort, Datadog is committing to making available an “integration tile” through which BigPanda will also be able to feed root cause analytics based on machine learning algorithms into the Datadog console. That capability will be enabled by a service-to-service topology map created by Datadog that will be used to drive event enrichment, correlation, impact analysis and prioritization.

    Mohan Kompella, vice president of product marketing for BigPanda, said Datadog affords his company a unique opportunity to better train AI models using a massive amount of IT and security metrics that Datadog aggregates daily. That data can then be combined with data ingested from other IT service management systems, which will enable IT operations to automate processes using better-trained AI models, added Kompella. Thanks to modern application programming interfaces (APIs), Kompella noted it’s become a lot easier for platforms such as BigPanda to consume a wide range of data.

    The more data an AI model is exposed to the faster it learns about the IT environment an organization is seeking to automate. Each IT environment is unique, so it usually takes some time for AI models to be optimally tuned. The more data that’s made available, the faster the AI platform is able to make accurate recommendations. It’s then up to each IT team to decide to what degree they want the AIOps platform to automate a task based on those recommendations versus implementing themselves. The more accurate the recommendations become over time, the more confidence IT teams gain in the AIOps platform.

    Of course, IT environments tend to change rapidly and evolve in the DevOps era. The alliance with Datadog will provide BigPanda with a primary source of data from tools that are already widely employed by DevOps teams.

    Ultimately, the goal is to reduce the mean time to resolution for IT incidents, in addition to eliminating as much as possible the need to invite developers and IT operations teams to a “war room” meeting to discover the root cause of an issue. Many IT teams these days find themselves spending weeks trying to ascertain the root cause of an issue that, once discovered, only takes a few minutes to fix. AIOps platforms afford IT teams the opportunity to eliminate a lot of friction that currently exists in those complex IT environments that have scaled beyond the ability of humans to manage without some AI assistance.

    In the wake of the economic downturn brought on by the COVID-19 pandemic, interest in AIOps is clearly on the rise. IT environments continue to grow in size as the enterprise IT becomes more extended. It won’t be long before most IT teams expect to be able to leverage AI to manage highly distributed computing environments. It may, of course, be a while before IT teams fully trust those platforms; however, it’s also clear IT as a whole is reaching a point where there is no alternative.

  • AIOps and the Myth of ‘Move Fast and Break Things’

    AIOps and the Myth of ‘Move Fast and Break Things’

    Observability through AIOps enables developers to find problems faster by cutting through the noise

    Years and years ago, a little-known entrepreneur named Mark Zuckerberg uttered the phrase, “Move fast and break things,” to his development team. Since then, dev teams have stuck to this “move fast and break things” mantra, where fast development far outweighs dealing with a few bugs down the road. Under this mindset, finding and fixing problems is essentially the last step in the development process.

    The Problem With Breaking Things

    This way of working has served dev teams well over the years as consumers continue to demand speedier innovation. But as end user expectations around deliverability speed rise, so do expectations around service reliability. Customers now want better products faster. Not only does the “break things” way of development lead to extra work for the dev teams down the road, but it also degrades customer trust over time as issues, from a buggy user experience to major security concerns, pop up once the product is already shipped.

    Even Zuckerberg realized this years later, admitting, “What we realized over time is that it wasn’t helping us to move faster because we had to slow down to fix these bugs and it wasn’t improving our speed.” He realized this so much so that the Facebook team officially changed their mantra to “move fast with stable infra” in 2014. Sure, Zuckerberg shouldn’t be our role model after everything that has happened, but if he can realize the need for stable infrastructure, so should we.

    So, how do dev teams meet growing end user expectations while cleaning up the now hurried development process for more stable systems?

    Observability With AIOps and the Development Process

    This is where observability strengthened by AIOps (artificial intelligence for IT operations) comes into play. Observability—or simply making data readily available from a system that you want to monitor—enhanced through AIOps allows dev teams to automate their monitoring. This automation can include applying AI/ML algorithms to all data; eliminating noise; detecting anomalies; correlating relevant metric anomalies, traces, changes and log events triggered by incidents; using contextual data to surface incidents; and identifying probable root causes of those incidents. This gives the teams a more complete picture of what’s happening in their systems, where the bugs are and how to resolve issues quicker, ultimately speeding the development processes overall.

    Through observability with AIOps, dev teams can actually build systems faster by finding problems earlier, cutting out the time-consuming process of retroactively fixing mountains of problems after the product has already shipped. On top of that, finding these problems earlier ultimately leads to more reliable services—and reliable services mean happier customers. When the product actually works the first time around and there’s a system in place to find and fix evolving issues, there’s less legwork to make customers happy. It’s a win-win.

    Observability with AIOps also means freeing developer time and effort to focus more on innovation and less on operations. And who doesn’t want to work on higher impact projects more often instead of spinning their wheels making sure the work they’ve already done is actually functioning correctly?

    We Need to Stop Stalling

    It’s easy to get stuck in your ways, but that is the only reason dev teams haven’t improved their processes to streamline workflows and build better products. Simply put, we have to stop stalling on making this change because, finally, moving fast doesn’t have to mean breaking things.

    We can continue to move fast while building products that are better quality and keep customers happier if we change our mindset around the development process. As developers, it’s our responsibility to evolve with the best strategies for our team, customers, and business—and we now have a way to do so by embracing observability with AIOps.

  • Granulate Applies AI to Linux Server Optimization

    Granulate Applies AI to Linux Server Optimization

    Granulate today announced that a platform that leverages machine learning algorithms to optimize Linux server environments running on-premises or in the cloud is now generally available.

    The Granulate platform employs agents, dubbed gAgents, that install machine learning algorithms to continuously optimize deployments across a server environment, said company CEO Asaf Ezra. The company also launched gCenter, a portal through which IT teams can manage the deployments of those agents.

    The Granulate agents automatically learn the specific resource usage patterns and data flow of any applications. By analyzing CPU scheduling order, oversubscribed locks, memory, network and disk access patterns, the agent also identifies over-subscribed resources, bottlenecks and prioritization opportunities. It then tailors scheduling and prioritization decisions regarding CPU, locks, caches and network access at the operating system level to improve the application performance.

    Ezra said the agents created by Granulate will then automatically optimize the server environment based on parameters defined by IT teams using gCenter. That capability not only enables IT teams to reduce costs by 60% but it also results in a 40% improvement in response times and a 5X improvement in overall throughput without any changes to application code being required, he said.

    According to the company, more than 40,000 instances of gAgent have already been deployed by IT teams at PicsArt, Perion, AppsFlyer and Coralogix.

    The gAgents are designed to install easily using a single command line on any Linux server. Once installed, Ezra said, it’s not long before IT teams discover they can significantly reduce their cluster size and downsize their machines.

    In the wake of the economic downturn brought on by the COVID-19 pandemic, IT teams are under more pressure than ever to reduce costs. IT environments, however, have often become too complex to optimize without relying on some form of artificial intelligence to automate IT operations (AIOps).

    While many IT teams may have initially been dubious of the effectiveness of AI, it’s become increasingly apparent that optimizing IT environments without relying on machine learning algorithms to some degree isn’t feasible.

    The challenge, of course, is making sure that whatever form of AI employed is making the right optimization decisions. As such, most IT teams tend to launch small AIOps projects initially until they can verify that the decisions being made are valid. Once a level of trust is established, many of the tedious tasks that IT teams that once were performed manually are either eliminated or, at the very least, reduced sharply.

    It’s not clear exactly what impact AIOps in all its forms will have on IT jobs. In theory, smaller IT teams should be able to manage larger IT environments. However, rather than eliminating positions, most organizations appear to be increasing the volume of applications they can deploy as the overall IT environment becomes more automated.

    Like it or not, IT is becoming more automated. The issue now is determining precisely where the interface between IT administrators and machines that can manage themselves now lies.

  • 5 Myths About Software Development

    5 Myths About Software Development

    2020 has broken many rules about software development. Some perceived trends, however, remain myths

    Some trends come and go, some trends stick. I remember when Docker launched and it was barely mature enough to run unit tests, and now containers are a common way for modern orgs to deploy applications. We’ve seen the rise of microservices but also the return to the monolith. We’ve seen “observability” take off in popularity, while serverless remains a buzzword with limited (but fast-growing!) adoption. Machine learning and artificial intelligence are still click-worthy headlines, but how much actual ML is being used within our applications?

    The True State of Software Development

    Let’s look at five common myths about the current state of software development and evaluate why they’re not completely true:

    Digital Transformation Initiatives Will Be Hindered by the Pandemic

    We live in an online world where every business is already a software business (or rapidly becoming one). The impact of the pandemic further highlighted ISG’s research which stated that “2020 will still see the rapid scaling of digital initiatives across industries.” While some industries have been hit so hard that digital projects had no choice but to stop (e.g. the airline industry), in many circumstances this will be the catalyst to fully embrace digital transformation.

    The Waterfall Method Still Works

    Organizations that haven’t adopted more agile and iterative approaches to building software are operating at a significant disadvantage. I know there are many big companies that still believe in doing all of the strategic planning upfront (I know this because I talk to these companies), but the truth is that it leaves very little room for flexibility and improvement in real-time. In today’s world, the customers and the users are kings—it is no longer possible to build great products without their input. If you take anything away from this article, it’s that the waterfall methodology needs to be abandoned once and for all.

    Everyone Loves Kubernetes

    K8s is not a one-size-fits-all solution. The fact of the matter is that a large chunk of the enterprise still hasn’t fully embraced the cloud, let alone started containerizing their applications. So while we have to give credit where it’s due and acknowledge that K8s is the clear leader for orchestrating containers, we also have to acknowledge that the hype doesn’t quite match the adoption. It’s still a relatively young technology.

    According to a report from Datadog last year, 45% of their customers that run containers use Kubernetes. That number is growing and is certainly impressive, but when you realize that only a chunk of their customers run containers in the first place and they are a modern monitoring solution where we’d expect to see a higher percentage than a random sampling, it’s clear that K8s is far from mainstream.

    Everyone Is Doing Tracing

    The rise of the digital age has put monitoring and observability in the forefront. As we rely more and more on online services for internal and external interactions, we better make sure they are working properly. But at the same time, regardless of industry headlines and conference talks focusing on tracing, the truth is that the industry at large has yet to adopt it.

    For most organizations, there’s so much room for improvement in getting logging and metrics right, that there’s no need to opt for tracing (yet). After all, Google built most of its global platform relying on metrics. Unlike SREs, developers often find tracing alien and very cumbersome to deploy and wield. Instead, more and more companies are opting to empower their engineers to see what’s going on in remote environments through software understandability.

    AI Will Solve Our IT Problems

    We’ve been hearing about the promises of AI in IT for a long time now. One of the terms still actively promoted that I am very skeptical of is “AIOps.” What does that even mean, really? According to a recent report from OverOps, only a very small percentage of companies are even considering AIOps in their plans for the future.

    That said, I do believe in making better, data-driven decisions. So the more data we can collect, contextualize and understand, the better. We see how this helps companies identify anomalies, alert on issues and even project out into the future. But none of this, really, is artificial intelligence.

    Conclusion

    There’s often a gap between hype and actual adoption. That’s the role of good marketing. And ultimately, hype helps drive adoption. But as technical practitioners, we need to stay rooted in reality in software development and beyond. So while I am convinced agile has proven to be the best way to build and deploy software, and that most organizations would benefit from letting go of old-fashioned waterfall processes; on the other end, I haven’t been fully convinced of the benefits of tracing and AIOps for most companies.

  • OpsRamp Adds Synthetic Monitoring to AIOps Platform

    OpsRamp has extended the capabilities of its namesake AIOps platform to include synthetic monitoring capabilities alongside existing tools for monitoring end user experiences.

    Bhanu Singh, senior vice president of engineering and DevOps for OpsRamp, said the Summer 2020 Release of the software-as-a-service (SaaS) platform adds synthetic monitoring that not only identifies bottlenecks across multi-step transactions but also correlates transaction performance such as page responsiveness and page timeouts with business metrics such as conversion rates and completion rates.

    By combining synthetic and end user monitoring capabilities, OpsRamp is effectively reducing the total cost of monitoring at a time when many organizations are under increased pressure to reduce costs, noted Singh.

    The OpsRamp Summer 2020 Release also improves monitoring with transaction latency breakdown for each step that contributes to user experience issues.

    It also adds visualizations for event correlations enabled by machine learning algorithms, support for HTTP response header logging for web performance insights and the ability to customizable transaction steps to provide more context.

    The OpsRamp Summer 2020 Release also adds 22 monitoring integrations for Amazon Web Services (AWS), including AppStream, Cognito, EventBridge, GuardDuty, Lex, OpsWorks and SageMaker.

    OpsRamp has added alert integrations with AppDynamics, Microsoft Systems Center Operations Manager, Sumo Logic and Sysdig. In addition, OpsRamp can now consume Sysdig events.

    Finally, OpsRamp is making it possible to access alert sequencing explainers that describe how the machine learning algorithms work. Alert sequence policy status shows model training progress at each stage of the event management life cycle.

    Singh said this latter capability is critical if IT teams are to gain confidence in AIOps platforms such as OpsRamp. In the wake of the downturn brought on by the COVID-19 pandemic, the need for AIOps has never been more pronounced. IT organizations are now under more pressure than ever to automate IT operations as part of a larger effort to reduce costs.

    The challenge they face is IT environments have arguably never been more complex. In many IT environments, the level of complexity is no longer feasible to manage IT processes manually. Automating IT processes is challenging because in most cases the IT environments have been customized extensively. AIOps presents an opportunity to employ machine learning algorithms to learn how each application environment functions and then automate it based on best practices.

    At this juncture, it’s more of a question of the degree to which IT organizations will be relying on AIOps. It may be a while before IT teams gain confidence in AIOps tools. However, with each passing day, machine learning algorithms become a little bit smarter. They may never have the intelligence of a human IT administrator; however, they never forget what they learn, require time off or quit to take another job.

    On the downside, of course, is that should machine learning algorithms make a mistake, the potential damage to an IT environment could be significant given the level of scale machine learning algorithms operate on. As such, IT organizations will always be well-advised to make sure the appropriate level of human supervision is always available.

  • AIOps Greasing the Wheel of Digital Security amid COVID-19

    AIOps Greasing the Wheel of Digital Security amid COVID-19

    Amid the most challenging era for business infrastructures and transformations, AIOps (AI for IT Operations) can help industries operating in the digital environment to be vigilant when it comes to network operations or the digital security measures.

    AIOps has the potential to significantly align well with the cybersecurity needs of a company. A cybersecurity department in every organization needs to be active with respect to the latest and innovative security-related solutions. Threat detection and data security are crucial for every business entity.

    The corporate networking infrastructure for communication purposes is always under the threat of unauthorized third-parties that try to access confidential communications. Whether they are wireless, wired or connected to internet of things (IoT) devices. AIOps, through its intelligent algorithms, detects the communication requests and their source to accept them and restrict the fraudulent entities from becoming part of the legitimate network. Visibility and speed are two important aspects of platform security. If the source of the incoming request is known, it would be easy to verify it.

    The following are some of AIOps applications:

    Regulatory Framework

    General Data Protection Regulation (GDPR) corresponds to the protection of online customer data online. Businesses are required to ensure enough security measures to protect the personal and financial information of customers stored in an online account from identity theft and data breaches. AIOps can be used to secure the data from unauthorized third-party access in order to comply with the regulatory requirements.

    Real-time Data Processing

    In this era, communications, connection, data transmission and storage activities are performed online. Real-time data collection and processing in AIOps help streamline the operations and ensure the security of platforms in a centralized database. Technologies are shaping future investments. Enterprise management with respect to the performance attributes such as data analysis and monitoring for controlled access are in high demand. Advanced automation techniques have become possible with the innovative AIOps that help undergo operations in efficiently, eliminating the time-consuming and frustrating manual tasks. Machine learning and artificial intelligence algorithms collectively help reduce the overall response time, provide optimal user experience and protect network systems.

    Applications of AIOps amid the COVID-19 outbreak pave the roads for leading challenges. The rapid digital transformations during the pandemic are giving birth to various challenges with respect to security, user experience and the downfall of industries. Businesses are revamping operational infrastructure and fraudsters are actively exploiting vulnerabilities in the digital environment. The density with which the fraud is increasing each passing day is high. AIOps, under these circumstances, help businesses take into consideration vigilant security measures. The innovative solutions employ AI algorithms for the security of online platforms.

    Information Security and AIOps

    Information security highlights three major factors: confidentiality, integrity and availability. Confidentiality takes into account the need for protecting confidential customer information. Integrity keeps the form of data intact and availability ensures that the data is present at the moment when it is required. AIOps help ensure this CIA triad of information security through its high potential applications online security.

    High-scale data breaches, identity theft, account takeover frauds, malicious executables injection and similar cyberattacks are due to the uncontrolled access over user accounts and data. The list of fraud calls for stringent actions that AIOps provides in the form of digital security. The AI-powered identity verification systems help verifies the identity of customers online before onboarding them. Biometric sign-in every time while accessing the accounts help mitigate the risk fo fraud and bad actors that become the reason for malevolent activities. The spoofing detection and prevention techniques use AIOps that identify the spoofing elements in user-uploaded identity proofs and discard their communication request, which in return curb the likelihood of cyberattacks.

  • VMware’s Blue Medora Buy Advances AIOps

    VMware’s Blue Medora Buy Advances AIOps

    VMware has announced it will acquire Blue Medora, a provider of a service for integrating data collected by IT monitoring tools, to expand its efforts to advance the adoption of artificial intelligence to automate IT operations (AIOps).

    Roy Ritthaler, vice president of cloud management for VMware, said Blue Medora provides VMware with a software-as-a-service (SaaS) platform that is employed to inform VMware vRealize, an IT automation suite that is now being used by more than 400 customers.

    The data Blue Medora collects will also be employed within Project Magna, a research and development initiative that applies machine learning algorithms and other forms of artificial intelligence (AI) to automate IT operations, he said. Project Magna is based on a reinforcement learning engine that automates the tuning of multiple sub-components of the overall IT environment, resulting in what VMware calls “self-driving data centers.”

    Ritthaler said the goal is not to replace IT professionals as much as it is to eliminate rote tasks while making it easier to manage increasingly complex IT environments at scale. It then will become feasible for a smaller number of IT professionals to manage hybrid cloud computing environments, he noted.

    Blue Medora’s True Visibility Suite provides VMware with visibility not just into IT infrastructure but also all the way up the application stack.

    Ritthaler said it’s only a matter of time before most management frameworks are accessed as service, given all the data that needs to be collected and then analyzed to surface meaningful insights. It’s not clear how roles with IT organizations will evolve once those insights are surfaced regularly. Most enterprise IT organizations today rely on multiple specialists to optimize all the components that make up the IT environment. In theory, AIOps will make it possible for IT generalists to manage an IT environment without having to rely on so many specialists.

    AIOps undoubtedly will also have a profound impact on DevOps processes. As more IT operations become automated, the amount of time and effort spent managing IT infrastructure should decline.

    Interest in IT automation is on the rise in the wake of the COVID-19 pandemic. With many IT professionals working from home as much as possible, organizations are looking to automate rote IT processes to make them easier to manage remotely. In many cases, achieving that goal will require IT teams to rely more on tools that are accessed as a cloud service.

    Regardless of the path forward, it’s apparent the management of IT is about to be utterly transformed.

    It’s not that IT organizations will suddenly have a greater appreciation of IT automation. However, the pandemic has added more urgency to an IT transition that was already underway prior to the pandemic. The issue now is not so much whether to embrace IT automation as it is determining what can be automated for a consistent outcome across IT environments that frequently are made up of a diverse yet not especially well-integrated range of platforms.

  • Aruba Injects AIOps Into Network Management

    Aruba Injects AIOps Into Network Management

    Aruba, a Hewlett Packard Enterprise (HPE) company, this week launched a platform that leverages artificial intelligence (AI) to identify and remediate networking issues before they have an impact on end users.

    Patrick LaPorte, senior director of cloud and software solutions marketing, said the Aruba Edge Services Platform (ESP) applies algorithms to data collected for customer networks and aggregated by Aruba in the cloud. That approach enables the company to apply AIOps to automate the management of both software-defined wide area networks (SD-WANs) and Wi-Fi access points.

    Aruba has been making a case for converging the management of SD-WANs and Wi-Fi access points in remote offices for a couple of years now. With the downturn in the economy brought on by the COVID-19 pandemic, the need to reduce the total cost of networking is even more pressing, LaPorte said.

    The Aruba ESP platform further reduces the total cost of networking by augmenting network administrators with AIOps capabilities that will enable a smaller team to centrally manage via the cloud a much larger number of network services being delivered to remote offices, he said.

    At the same time, Aruba enables IT organizations to implement zero-trust network security by providing built-in support for role-based access, dynamic segmentation of network services and identity-based intrusion detection to authenticate, authorize and control every user and device connected to the network, said LaPorte.

    The Aruba approach to AIOps revolves around Aruba Central, a cloud service based on Kubernetes the company relies on to manage networking equipment that runs ArubaOS services. Aruba claims to have more than 65,000 customers using Aruba Central to manage network services.

    Aruba Central has been enhanced to include AI Search, a data discovery service based on a natural language processing (NLP) engine that enables IT teams to employ English-language queries to extract user and device information from the data lake created by Aruba ESP. The company has also added user-centric analytics to better identify client, application and network performance issues.

    Aruba has also added a faster Aruba CX 6200 Switch Series that includes built-in analytics and automation capabilities.

    Finally, Aruba has launched Developer Hub, which provides access to its application programming interfaces (APIs) and documentation to applications that leverage the Aruba ESP platform.

    The way network services are being managed and delivered to remote offices is dramatically changing as applications become more distributed. At the same time, the entire definition of what constitutes a remote office in the age of COVID-19 is being transformed as more employees work from home. Aruba is addressing that challenge by making it easier to set up network tunnels between home offices and remote offices managed by Aruba Central, said LaPorte.

    As networking continues to evolve there is no doubt IT organizations will be relying more on automation and AI to deliver network services as scale. Less clear is the degree to which IT organizations will be willing to standardize on networking equipment from a single vendor to achieve that goal. In theory, IT organizations can reduce their total cost by standardizing on a single vendor. In practice, however, many IT environments are comprised of a dizzying array of networking equipment from multiple vendors.

    Regardless of what path forward is chosen, it’s already apparent the current networking status quo will not hold for much longer.

  • Should I Stay or Should I Go? A Cloudy Decision

    Should I Stay or Should I Go? A Cloudy Decision

    If you’ve been operating in the cloud for some time now, chances are your business has changed since you first made that move and particularly during the current climate. Has your cloud usage grown considerably — and your OpEx costs? Is that just the cost of doing business in the cloud?

    It doesn’t have to be. Here’s how you can rationalize your infrastructure and determine if there are cloud expenses you can reclaim and even if it makes sense to move some of your cloud deployments into colocation.

    The rush to the public cloud has now slowed as organizations realized that it is not a one-size-fits-all solution. The main issue is the lack of deep visibility into the performance of applications provided by the host. Our own research has recently revealed that 32% of public cloud resources are currently under-utilized, and without proper direction and guidance, this will remain the case. What is needed is real-time data and intelligent recommendations to lower costs and assure performance.

    In order to optimize cloud resources, a third-party AIOps based resource is needed. This will provide an independent and granular view of how applications are using capacity and if it is right-sized. In addition, it will monitor the performance of the applications in real-time and provide metrics and analytics to eliminate bottlenecks. The allocated capacity can also be monitored to ensure an accurate match to workload requirements via real-time performance data.

    Although the major hosts provide cost optimization tools, these are not very accurate. Analysis of billing and how it matches capacity over time — as well as in real-time — is what is needed for the cloud to remain a vital part in IT infrastructure. Armed with this information you can plan capacity purchases and discover wasted spend. By using a single platform for cloud management, you can monitor your infrastructure, plan capacity and eliminate performance risks. Performance bottlenecks can be predicted before they affect clients and SLAs with multi-conditional alerting powered by advanced anomaly detection.

    Cloud solutions are not only publicly provided by the likes of AWS and Azure. Colocation is also a strong option where your applications are managed on your behalf by a system integrator. This is increasingly becoming a stronger option for more business-critical applications. But to determine which is best for you, you need to start with the facts. 

    The cloud promises IT organizations unprecedented value in the form of business agility, faster innovation, superior scalability and, most importantly, cost savings. For many organizations, it is at the core of their IT digital transformation strategy. It is a disruptive force that requires application workload behavior knowledge, careful planning and collaboration from well-informed, trusted advisors.

    As a first step, enterprises frequently target a subset of their less critical on-premises applications for migration to the public cloud. Typically, organizations will take one of two paths to the cloud:

    1. Going Cloud Native. Rewrite your application to use resources offered by a cloud provider.
    2. Lift and Shift. Very minimal or zero code changes to the application. Largely, just replicate the application in the cloud.

    The faster time-to-production choice is to lift and shift the targeted applications to a cloud service provider’s Infrastructure as a Service (IaaS). In the lift and shift option, the advantage is a reduction in the cost incurred in the physical infrastructure such as hardware, floor space, cooling, security, etc., and the management of that infrastructure. Savings will differ depending on your unique computing resource needs, workload refactoring and business models.

    Even in its simplest form, IaaS migrations must be carefully planned requiring answers to some fundamental questions:

    1. Will my application perform as expected in a public cloud? (Application Fitness)
    2. How much will it cost to run my applications in a public cloud? (OpEx)
    3. Which cloud service provider is the best choice for my applications? (Cost and Fit)

    IT managers need answers to these questions before the actual migration is performed. As most internal IT organizations don’t have deep cloud expertise, the question is: Who can you trust to provide you with the answers and help you make better business decisions?

    As technology and the cloud stands to play an ever-increasing role throughout organizations, ensuring that you’re adopting the right type of infrastructure specifically for your business has never been more vital for continued success. Choosing a service that provides the answers to your key questions, before the actual migration takes place and prepares you with vital insights into your applications and workloads targeted for cloud migration, has to be an important part of the decision-making process.

    As organizations continue to battle the COVID-19 storm, understanding the product that will overhaul your IT infrastructure, before you fully buy into it, is going to provide the confidence and assurance you need to make that decision a little less cloudy.