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Tag: storage

  • Has the Time Come for IT Predictive Analytics?

    Has the Time Come for IT Predictive Analytics?

    Predictive analytics technologies have become critical to compete in manufacturing (predicting machine failure), banking (predicting fraud), e-commerce (predicting buying behavior) as well as to address horizontal use cases such as cybersecurity breach prevention and sales forecasting. Using data to predict and prevent IT outages and issues is also a growing best practice—especially as advances in monitoring software have made it easier to deliver analytics in a timely manner.

    The process of rapidly collecting IT systems data, including by integrating data from various monitoring tools in use, has become rudimentary. The magic is in the algorithms, which can deliver actionable intelligence. IT predictive analytics, once known as IT operations analytics (ITOA), is still nascent in many organizations, but it’s far more streamlined than it used to be when one needed to export data sets to specialized analytics tools such as Tableau or Microsoft PowerBI. Data analysts and then data scientists were necessary to interact with these advanced tools and deliver valuable predictions—a process that could take weeks.

    Today, IT professionals can use modern IT operations monitoring and management systems for collecting and integrating the data, normalizing it and then analyzing it in real-time. That’s making predictive analytics in IT far more user-friendly, affordable and useful for all organizations, not just the largest companies with the biggest budgets.

    Advances in predictive analytics technology, aided by machine learning and AI, are creating more proactive workflows for IT operations teams. For instance:

    • Storage usage. It is not a happy moment when IT finds out that the expensive storage array is down. It’s not enough to know after the fact that storage volumes were steadily climbing out of the normal pattern. By knowing ahead of time that a trend is heading out of the acceptable range, IT can research the cause and fix it before any damage is done. With cloud storage, risks include slow connectivity if network bandwidth is insufficient or security breaches if the cloud provider’s security mechanisms are not sufficient for your use case. Predictive analytics can alert on these possibilities in time to take corrective action.
    • Cloud-to-cloud performance comparison. An important aspect of predictive analytics is the ability to make correlations and show relationships between multiple data points. IT leaders are interested in learning about an application’s performance across the entire IT environment, which may include comparing historical performance across multiple clouds to predict weaknesses in one service over the other and mitigate them proactively. This use of predictive analysis could also be valuable in helping companies choose one cloud provider over another for particular applications and workloads.
    • Container optimization. Making correlations is especially valuable when it comes to managing performance in a microservices environment. Collecting metrics for a containerized application isn’t useful by looking at each container per se, but it is useful when looking across the container environment to see if there are common metrics dangerously trending the wrong way across many or all containers.

     A Few Tenets for Predictive Analytics in IT Operations

    Regardless of the tools that you choose to create a predictive analytics practice in your organization, there are several things you must get right first. Here are six recommendations to consider:

    1. Centralize the data into one or two systems at the most. This allows for data cleansing, normalization and a single (ideally) system of record for all other users and integrations.
    2. Invest in first-class data visualization. As business units now interact with IT management systems to see departmental usage and troubleshoot minor issues, tools should be user-friendly for people who are not data analysts. Dashboards and charts can tell the story simply and explain the correlations and conclusions which the software delivers.
    3. Make it actionable. There is no use in creating predictive data if it’s not automatically sent to the right individuals and/or shared with other systems to execute actions when needed. Think about it the Amazon way: We all know that people aren’t sitting behind monitors sending millions of individualized recommendations to customers every day. Your IT operations portfolio should behave the same way: automated, intelligent, helpful and always adapting to new information.
    4. Correlate data at the speed of IT change. Before there was such a thing as virtualization and cloud infrastructure, there weren’t many devices to monitor and things didn’t change much day-to-day. You could actually “set and forget” data center infrastructure components for weeks. Now, of course, changes are happening behind the scenes every minute without our knowledge. Yet your monitoring and change management tools need to know about it. By using standard deviation for change detection monitoring—four-hour increments are typical—you can ensure that your team always has an updated understanding of infrastructure status as well as the relationships between the various systems and components.
    5. Avoid the black box. Enrich your data lake continuously with event data and allow users to see the IT operations platform working in real-time. With machine learning and artificial intelligence, there’s always a measure of doubt and a margin of error. This uncertainty can be counteracted by sending more diverse data sets so that the user can make her own conclusions. As well, when the machine makes an error in judgment, train it to avoid making the same mistake again.
    6. Update internal IT skills. You’ve heard it before. In the age of big data, IT organizations need data science expertise and skills. It’s important to acquire or develop these skills so that your team can combine machine intelligence with human insight and make better decisions. That is where AI-driven analytics holds the most promise.

    We are just getting started on this new age of predictive analytics in IT operations. Before long, organizations will move from predicting downtime and serious application issues to driving infrastructure optimization and cost savings. IT leaders will be able to answer with confidence, how much cloud infrastructure needs to be provisioned or de-provisioned over time. They will understand with better clarity common application scaling patterns, as well as overall capacity management needs. As predictive analytics takes hold, IT leaders will need to determine how to reorganize employees who previously spent hours scanning data on screens into more strategic roles.

  • Cloudian Adds Object-Based Storage Platform Monitoring Tool

    Cloudian Adds Object-Based Storage Platform Monitoring Tool

    Cloudian today added a monitoring and observability tool to its object-based portfolio of appliances for storing unstructured data in on-premises IT environments.

    Jon Toor, chief marketing officer for Cloudian, said HyperIQ provides access to analytics and health checks that enables predictive maintenance, enhances security and optimizes storage utilization. Cloudian is also pledging to extend the reach of HyperIQ in the future to other IT infrastructure via connectors.

    HyperIQ comes in the form of a virtual machine that IT teams download and install on-premises. A free HyperIQ Basic version comes with pre-configured dashboards. HyperIQ Enterprise comes with support and access to more advanced analytics features and is licensed by capacity at 0.025 cents per GB per month.

    Cloudian is also providing integration to multiple notification channels, including Slack, OpsGenie, Kafka and PagerDuty, to share alerts based on parameters IT organizations define in HyperIQ. In addition to pre-configured dashboards, IT teams can also leverage more than 100 data panels to drill into user or storage data at a more granular level.

    Toor said HyperIQ will become the foundation upon which Cloudian will also start to add AIOps capabilities to its platform. Based on machine learning algorithms, HyperIQ will enable Cloudian to collect the data required to train those algorithms, he said.

    Cloudian has been making the case for a distributed storage system that enables IT teams to globally manage appliances via a single user interface. As modern applications have become more complex, the need to access unstructured data stored within on-premises IT environments has become more pronounced.

    The challenge IT teams face is they don’t have enough staff available to manage each of those systems locally. In the wake of the economic downturn brought on by the COVID-19 pandemic, that issue has only become further exacerbated. Many IT organizations are either trying to reduce the size of their IT staff or limit the number of additional full-time employees they hire.

    At the same time, the amount of unstructured data an organization needs to store continues to grow. While much of that data can be stored on public clouds, a wide variety of global compliance regulations require much of that data to be stored in an on-premises IT environment.

    Given how distributed that data is, there is a clear need for deeper levels of analytics that ultimately should enable the management of distributed storage systems to be more automated. Many IT organizations have already significantly automated the management of storage systems within a local data center. The challenge now is to automate distributed systems residing in multiple geographic regions.

    Reliance on object-based storage systems to manage unstructured data within local data centers is still a relatively new phenomenon. Cloud storage service providers have been making use of object-based storage systems for the better part of a decade to manage massive amounts of data. Now internal IT teams are starting to follow suit. It’s not likely object-based storage systems will completely replace file-based storage systems anytime soon. However, the opportunity to automate the management of modern object-based storage systems may be far greater.

  • Dell Advances Convergence of Compute and Storage

    Dell Advances Convergence of Compute and Storage

    Dell Technologies today launched a revamped midrange storage platform that takes advantage of NVMe interfaces and solid-state drives (SSDs) to deliver 7x the performance of previous Dell EMC PowerStore platforms, in addition to allowing applications to run locally on an instance of a VMware virtual machine.

    Ben Jastrab, senior manager for storage product marketing at Dell Technologies, said the inclusion of a virtual machine in form of an AppsON capability means that rather than having to deploy applications on a server, for example, a data analytics application could be deployed directly on top of the data it needs to access on a storage system. In addition, the latest version of Dell EMC PowerStore adds support for instances of Kubernetes from VMware running on top of a virtual machine. Dell storage systems already provide support for the Container Storage Interface (CSI) needed to provide Kubernetes clusters access to external storage systems.

    Dell has also added support for Ansible and VMware vRealize Orchestrator frameworks to automate the deployment and ongoing maintenance of Dell EMC PowerStore systems. As part of that effort to automate the management of storage systems, Dell added built-in machine learning algorithms to automate labor-intensive tasks such as initial volume placement, migrations, load balancing and issue resolution. Dell EMC CloudIQ storage monitoring and analytics software makes it possible to combine machine learning and human intelligence for real-time and historical performance and capacity analysis.

    Jastrab noted that many of these innovations are made possible because the storage operating system employed within Dell EMC PowerStore has been completely revamped using microservices based on containers. That approach not only makes the operating system more flexible and resilient, but it also will afford Dell the opportunity to experiment with how it might present storage services in the future, he said.

    Automation coupled with technologies such as deduplication and compression will play a critical role in driving the total cost of storage lower in the months ahead, Jastrab said. In the wake of the economic downturn brought on by the COVID-19 pandemic, IT organizations will be under increased pressure to reduce costs at a time when both the amount and types of data that need to be analyzed and stored continue to increase, he noted, adding the Dell PowerStore system can provide access to 2.8PB of data when configured in a scale-up architecture and more than 11PB when configured using a scale-out architecture.

    IT teams will need to determine to what degree they want to rely on proprietary frameworks such as VMware vRealize to automate IT infrastructure versus an open source Ansible framework that can be applied more broadly to multiple platforms. Dell owns VMware, but Jastrab said the company is committed to providing support for multiple IT automation frameworks.

    It’s not clear to what degree applications will shift from being deployed on traditional servers to a storage system. Many next-generation applications are clearly more latency-sensitive than their predecessors, so providing more direct access to data over an NVMe interface may present IT teams with a compelling alternative. Regardless of the approach taken, the line between compute and storage continues to blur.

  • Commvault Deepens Ties With NetApp to Automate Data Protection

    Commvault Deepens Ties With NetApp to Automate Data Protection

    Commvault has extended its alliance with NetApp to make it possible to recover files nearly instantaneously from an all-flash memory array.

    Ranga Rajagopalan, vice president of product management for Commvault, said NetApp Scale-out Data Protection (SDP) from Commvault builds on its backup and recovery software to make it possible to use to NetApp hyperconverged infrastructure in combination with all-flash arrays running StorageGRID object storage software to now recover data stored on-premises or in the cloud.

    The on-premises capability is critical because it eliminates potential downtime in the event of a ransomware attack, Rajagopalan said, noting data can be recovered from an all-flash array residing on-premises using a scale-out architecture in a matter of seconds.

    At the same time, IT organizations can take advantage of an object storage system from NetApp that is compatible with the S3 application programming interface (API) to store data in the cloud, he added.

    Most IT organizations are by now aware the only way to combat ransomware is to be able to access a pristine copy of their data. The challenge they face, however, is finding a way to make that data accessible as fast as possible. It not only takes a significant amount of time to extract data from a cloud service, but it is also expensive. By keeping an organization’s most recent data available in an all-flash array locally, the time to restore data is sharply reduced, said Rajagopalan.

    Of course, organizations have been keeping copies of data on magnetic storage systems for years. However, as the cost of all-flash arrays continues to decline it’s now more affordable for organizations to consider employing much faster all-flash arrays to back up and recover their most mission-critical data, he added.

    Commvault also makes available application programming interfaces (APIs) through which the whole data protection process can be automated, he said.

    In the wake of the COVID-19 pandemic, there are two issues that many organizations are now struggling to manage. The first is the need to automate more processes at a time when many organizations will have smaller IT staffs. The number of IT personnel that can be dedicated to manually managing data protection tasks will be fewer during the economic downturn.

    At the same time, however, the number of ransomware attacks being launched continues to increase. All it takes is one mistake for an organization to find all its data encrypted. As a result, the relationship between data management and cybersecurity has become much tighter as organizations strive to ensure they always have a pristine copy of their data available.

    In the months ahead there is no doubt there will be a lot more focus on the total cost of IT operations. The challenge is, finding a way to reduce those costs must be balanced against the existing commitment to service level agreements (SLAs). The only way to meet those often-conflicting goals is to rely more on IT automation frameworks to make data always available, in a way that requires much less human intervention.

  • Humio Adds Unlimited Ingest Plan to Log Management Service

    Humio Adds Unlimited Ingest Plan to Log Management Service

    Fresh off raising an additional $20 million in funding, Humio, a provider of a platform for analyzing log data, has added an unlimited ingest plan to a cloud-based service to make it easier for organizations to retain massive amounts of log data.

    The unlimited ingest plan for the company’s cloud service extends a similar option Humio already makes available to customers that deploy its log management platform in an on-premises environment.

    Morten Gram, executive vice president for Humio, said one of the significant limitations of rival log management platforms is they charge customers based on the amount of data ingested. That approach only serves to dissuade organizations from ingesting data to limit their costs. The trouble is that organizations won’t be able to discover outlier issues in data they never see in the first place, he said, noting issue becomes even more problematic as organizations embrace best DevOps practices to deploy more applications both in the cloud and in on-premises IT environments. The Humio Bucket Storage can retain more than a petabyte of log data.

    Given the scale and scope of modern applications, Morten said organizations are now using log data to drive a wide variety of analytics tools to automate processes in real-time. Log data is also playing a critical role when it comes to applying artificial intelligence (AI) to IT management. The more log data made available, the more accurate the AI models being employed become, he noted.

    In addition, Gram said that because Humio doesn’t rely on an index engine to organize data for a search engine, IT teams can launch any type of query they like. In contrast, rival platforms are optimized for specific types of queries, he said, which also substantially reduces the amount of compute and storage required to analyze log data. That capability is why Humio can offer unlimited ingest of data and still remain profitable, as long as there are enough customers to enable Humio to operate at scale, he noted.

    Given the current need for many IT employees to work from home to help combat the COVID-19 pandemic, it’s clear more organizations will be relying on tools hosted in the cloud to manage IT environments. Many of those organizations will also be a lot more sensitive to costs given the impact the COVID-19 pandemic is having on the economy. Of course, it’s probable many organizations will postpone new application development projects until sometime after the COVID-19 pandemic subsides. However, as more organizations start to appreciate the extent to which people may adopt social distancing, many of them will soon be re-engineering business processes to adjust to a new reality. As such, it may now only be a matter of time before there are even more digital business transformation projects being launched than ever before.

    — Mike Vizard

  • Storj Labs Unfurls Decentralized Storage Service

    Storj Labs Unfurls Decentralized Storage Service

    Storj Labs has made generally available a distributed storage platform based on the excess capacity available on devices ranging from servers to individual desktops.

    Company CEO Ben Golub said the goal is to provide IT organizations with an alternative to storing data inexpensively using storage resources that have been vetted by Storj Labs.

    At the core of the Tardigrade Decentralized Cloud Storage Service provided by Storj Labs is an open source object storage system that is compatible with the S3 application programming interface (API) defined by Amazon Web Services (AWS). That object storage system distributes 80 slices of a file across a subset of thousands of nodes that each contain only a slice of the data required to reconstruct that file when needed. Only 29 of those slices are needed to reconstruct a file, so Golub said it’s relatively simple to ensure data is secure without having to manage encryption keys.

    Following an extensive beta test of the Tardigrade, the company claims to have achieved 99.95% availability, with a service level agreement of 99.9999999% achieved in terms of durability. Golub said Tardigrade makes use of erasure coding to make sure data is distributed in a highly resilient fashion.

    Storj Labs compensates owners and data centers along with individuals with access to a lot of excess storage capacity for making capacity available, Golub said. Most storage drives are not anywhere near full capacity, which Golub noted has made it relatively easy for Storj Labs to amass 19PB of storage for its Tardigrade service. Each provider of storage capacity is required to pass a series of data stewardship tests for a period of time before being allowed to become a member of the Tardigrade service.

    Storj Labs has also set up an open source partner program through which any open source project with a Tardigrade connector will receive a portion of the storage revenue generated by users of that software. That approach allows participants in projects to be partially compensated for their efforts as alternatives to cloud service providers, which routinely commercialize open source software without helping to fund its development, said Golub. Based on his previous experience as CEO of Docker Inc., he noted many open source projects die on the vine simply because the team that created it simply can’t afford to sustain it.

    Interest in Tardigrade Decentralized Cloud Storage Service for certain use cases as an alternative to pubic clouds could be high, he said, given the fact that there has been no meaningful decline in cloud storage service pricing in the last three years. Use cases that should prove to be especially appealing include data protection and image retention. Storj Labs is also making available a library of bindings for Go, Android, C, .NET/Xamarin, Node.js, Swift and Python to make it easier for developers to invoke Tardigrade storage services as code.

    It may be a while before IT organizations are completely comfortable with decentralized storage services as an alternative to public clouds. However, as the amount of data that needs to be stored continues to grow, every option available to store data may be needed soon.

    — Mike Vizard

  • Stellus Launches Next Generation NAS Platform

    Stellus Launches Next Generation NAS Platform

    Stellus Technologies launched a network-attached storage (NAS) system capable of processing unstructured data at 10 times the speed of existing systems.

    Based on a software-defined file system written on top of a key-value store, the Stellus Data Platform can be acquired either as an appliance or in the form of a virtual image that IT teams can deploy themselves on-premises, in the cloud or at the network edge.

    Ken Grohe, senior vice president and chief revenue officer for Stellus, said the operating system created for the Stellus Data Platform has been under development for the past five years thanks mainly to $70 million in funding from Samsung. As a provider of memory technologies widely used in storage systems, Samsung has a vested interest in advancing the adoption of next-generation storage systems capable of supporting a wide array of emerging applications, he said.

    NASThe appliance Stellus provides is built around 8TB hot-swappable, non-volatile memory express (NVMe) solid-state drives (SSDs). The base configuration is delivered in a 5RU2 rack that comes with 184TB of storage. The largest configuration comes in a 17RU rack to provide access to 1.475PB of storage capacity. The Stellus Data Platform can deliver throughput rates of 20 to 80+ GB/s reads and 19-70+ GB/s write throughput, according to the company.

    The Stellus Data Platform is designed specifically to address the needs of emerging applications in, for example, the medical and life sciences sector accessing massive amounts of data that existing NAS systems are either too slow or costly to address. The adoption of most of those applications are now being driven by DevOps teams rather than traditional storage administrators, Grohe said.

    Just as significantly, he said, the Stellus Data Platform is designed from the ground up to allow IT organizations to scale-up performance and capacity independently. That “Scale Through” architecture means IT teams don’t have to add additional “dead wood” to scale performance in the form of additional storage units that end up having only a small amount of data loaded on to them, said Grohe.

    In addition, the key-value store at the core of the platform enables searching and identifying unstructured data using metadata to eliminate the need for data caches and global data maps.

    Most big data analytics and artificial intelligence (AI) applications are deployed in the cloud today in part because legacy storage systems in on-premises environments can’t support them economically. The limiting factor, however, has not necessarily been the underlying technology, but rather the need to be backward-compatible with existing storage systems. As a startup, Grohe said Stellus enjoys the luxury of not being encumbered by a classic case of the innovator’s dilemma.

    It’s too early to say to what degree Stellus might supplant rivals such as Dell Technologies, NetApp and Hewlett-Packard Enterprise. Grohe said Stellus has signed up more than 500 customers to participate in its beta program. The challenge going forward will be determining to what degree Stellus will be employed to support a growing niche of emerging applications versus becoming a new foundation on which a next-generation NAS platform will be used to also support mainstream applications.

    — Mike Vizard

  • Moving Things Around: The Acquisition of Protego

    Moving Things Around: The Acquisition of Protego

    I received a note from Protego Labs’ vice president of marketing about Check Point Software acquiring Protego. We see a fair number of acquisition announcements, and usually don’t write about them–though we read them and stay abreast of industry happenings.

    I used to follow Check Point, and haven’t looked into what they’re doing in a while, so I followed through links to both organizations and looked at their products.

    I found myself becoming the wizard that some call Tim. But instead of “What is your name? What is your quest? What is your favorite color?” I was looking into “Where is your site? What are your products? What value do you add?” My beard is kept trimmed, more along the lines of a knight’s, so it makes sense I would ask different questions. As long as I’m not spouting “It’s just a flesh wound,” I figure I’m doing okay.

    First off, this merger makes sense. Protego does it for serverless and Check Point is doing their best to be where your code is running. Indeed, Protego probably needed a merger of some kind because all they do is serverless. Check Point’s Cloud Guard Dome 9, by contrast, will protect access rights and data for serverless, but doesn’t do what Protego does–keep an eye on the code. So this is a good fit.

    But it once again brings to light an interesting issue as you move things from DC to cloud A to cloud B to serverless. Where are your support functions? Networking, security, storage…These things need to be where the code is, but architectural options are more than they’ve been for decades. And the architectures are largely incompatible. Consider network functions for a physical-server based DC versus a VM DC, versus a Container DC, versus Cloud…You get the idea. The array of functionality and where it runs is different. Different in implementation and different in maintenance.

    This was a problem I (and others) pointed out when cloud started taking off: more work for ops, even if some of it is automated. Turns out ops, storage management and security all have more work, because all of those items are handled differently in the different architectures. And Protego had to focus on one to get it right, meaning they needed some kind of merger in the long run.

    So do you. Adding more and more work to ops–be it DevOps or traditional ops–is not the answer. A plan for standardizing is necessary.

    That is one of the big promises of containers, in my opinion. Not only are they more agile and easier to use in a DevOps environment, they are relatively standardized. Ops will still have more work if you’ve got apps in several of those architectures, but containers obfuscate some of the differences and allow standardized networking within the pod/node/group, for example.

    Some functionality just doesn’t fit into containers–you still need a physical network in the DC, or to configure the virtual network in a VPC to get traffic to/from your containers–but much of it does fit. Once at the pod/group, networking is virtual and the same regardless of where the containers are running. Which reduces overhead while making changes easier. The same is true for security, and increasingly for storage.

    There are great companies out there tackling these issues, and I’ll look at them on occasion. Right now, I know my former employer F5 Networks is working to solve the networking services/performance/security issues, and taking advantage of NGINX to handle some parts the core org traditionally hasn’t addressed. There are other companies, I just hear about that one more because I am still very close to some F5ers.

    It is worth planning for and certainly worth looking into more portable options, anything that lightens the load and keeps your great apps running. Keep kicking rear and spinning up apps.

    And don’t call me Tim.

    — Don Macvittie

  • Cisco Extends Ansible Support to Advance DevOps

    Cisco Extends Ansible Support to Advance DevOps

    Cisco Systems announced today that it is further embracing the open source Ansible automation framework on its storage area network (SAN) platform as part of an effort to extend best DevOps practices to IT infrastructure. The latest version of the Cisco MDS 9700 platform modules automates tasks such as VSAN configuration, device-alias and zoning configuration.

    Adarsh Viswanathan, product manager for data center storage at Cisco, said the company has embraced the Ansible framework because it’s an open framework that is now being adopted by enterprise IT organizations to manage infrastructure in a more adroit fashion. The rise of cloud-native applications being developed with DevOps processes not only result in more applications accessing a common pool of data, but also require changes to be made dynamically to the underlying storage platform.

    Cisco is adding support for 64G-line cards when they become available along with support for non-volatile memory express (NVMe) and All-Flash arrays. Cisco has also extended its SAN Analytics software to add support for the NVMe/Fibre Channel protocol along with existing support for SCSI/Fibre channel.

    Viswanathan said support for Ansible will make it simpler for IT organizations to embrace automation at scale. Cisco has previously embraced open application programming interfaces (APIs), OpenStack and Python-based scripts. Ansible takes those efforts to a higher level by make available a declarative framework that doesn’t necessarily require IT operations teams to develop programming expertise, said Viswanathan.

    IT operations teams are coming under increased pressure to provide developers with an IT experience that rivals cloud service providers. Frameworks such as Ansible make it possible for IT operations teams to make IT resources available on-demand to developers using IT infrastructure residing in an on-premises environment.

    Friction between developers and IT operations teams is rising as DevOps processes are employed more widely within enterprise IT environments. Many of those developers have alternatively turned to cloud service providers to build and deploy their applications. However, many organizations still need to deploy applications once they move into a production environment hosted in a local data center. In fact, more than 70% of enterprise applications still run in a local data center a decade after the initial rise of cloud computing. IT operations teams that manage those on-premises IT environments need to provide developers with access to resources they can easily scale up and down as required.

    Cloud-native applications based on microservices will further challenge IT operations teams as the number of application services trying to share a limited set of IT infrastructure resources only increases. In the case of Cisco, those limited resources are rack-based servers attached to SANs such as the Cisco MDS 9700.

    Of course, not every IT operations team is going to embrace DevOps processes overnight. Instead, many of them will be exposed to frameworks such as Ansible as they upgrade servers and storage over time. The one thing that is certain is developers will continue to apply pressure to make that transition sooner than later as more organizations continue to appreciate the extent to which they now depend on software to innovate.

    — Mike Vizard

  • HPE Brings Storage Deeper into DevOps Realm

    HPE Brings Storage Deeper into DevOps Realm

    Hewlett-Packard Enterprise (HPE) today moved to bring storage further into the realm of DevOps by integrating its 3PAR storage systems with VMware vRealize Orchestration software, open source Kubernetes container orchestration software and the Red Hat OpenShift platform-as-a-service (PaaS) environment built on top of Kubernetes. HPE 3PAR storage systems already support Docker containers and the DC/OS private cloud platform from Mesosphere.

    In addition, HPE is making available pre-built blueprints for Chef, Puppet and Ansible automation frameworks, as well as software development kits written in Ruby and Python that can be used to create custom integrations.

    Finally, HPE announced that HPE InfoSight, a cloud-based service that employs machine learning algorithms to predict when a drive might be about to fail or when application performance might be adversely impacted by I/O contention. HPE gained the core technology for that service when it acquired Nimble Storage last year.

    Vish Mulchand, senior director of product management and marketing for HPE Storage, said machine learning algorithms and other forms of artificial intelligence (AI) will soon be playing a much larger DevOps role. Research indicates that more than half of all support issues involve storage systems, and AI technologies will be able to identify most of those issues long before they impact applications, he says.

    Mulchand notes HPE has been adding integrations with various DevOps and container technologies to make it easier to scale applications as needed. The goal, he says, is to make storage management a more natural extension of any set of DevOps processes.

    In general, providers of storage systems have been investing heavily in automation frameworks designed to make it easier for storage administrators to manage ever increasing amounts of data. The next challenge facing IT organizations is to be able to provision storage resources in a way that doesn’t hamper the rate at which those applications might be deployed.

    It’s not clear to what degree storage administrators will be subsumed into DevOps teams. In most cases, storage administrators are looking to make available a pre-determined set of storage resources available to developers on a self-service basis. The amount of those resources is determined by the characteristics of the application, which storage administrators can gain earlier access into by participating in the DevOps process.

    Going forward, Mulchand says HPE expects competition between storage vendors increasingly will be defined by the quality of experience they provide IT organizations rather than the performance of the storage systems. Every storage vendor has embraced Flash storage systems that can address the performance requirements of just about any type of application workload, he says, so making it easier to access those storage resources and make sure they are constantly available is the next major opportunity.

    As more automation gets introduced into IT environments, there’s no doubt the role of the average storage administrator will change. Rather than manually provisioning resources, storage administrators will be able to develop policies for classes of applications that are automatically implemented whenever storage systems need to be provisioned. As storage administrators learn the characteristics of those application in a production environment, those policies will be further fine-tuned. The good news is that storage itself no longer needs to be seen as a bottleneck from either a performance or DevOps perspective.

    — Mike Vizard