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Datera 2017 Predictions: Four Things to Consider in 2017 - The Year of Cloud & Automation

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual series exclusive.

Contributed by Ashok Rajagopalan, Head of Product Management, Datera

Four Things to Consider in 2017 - The Year of Cloud & Automation

Over the past few years, the benefits of cloud computing, be it on public or private cloud, have become clear, yet many enterprises still struggle to operate workloads and manage their data at scale. The key to enterprises achieving simplified management at scale is automation and decoupling applications from physical infrastructure. 

A recent enterprise cloud are study by VMware discovered that a typical enterprise IT team operates at least eight individual clouds. Another peril of many of these clouds is that each one has a unique operational model, so orchestrating and managing all workloads and associated data warrants a massive set of IT resources and time, or a new operational paradigm driven by automation and abstraction. . While, a number of new workloads orchestrators such as Mesos and Kubernetes have started to deliver the abstraction for compute and networking, there aren't many such solutions delivering similar capabilities for storage. Hence, containers and their ecosystem adoption has grown primarily for stateless workloads, and are lagging for stateful workloads.

Modern clouds have become synonymous with intelligent software running on commodity x86 hardware. It is even more imperative that this software handles all the organic lifecycle process for the cloud, including software and hardware upgrades, scaling up and down, reconfiguration and patching. A truly well-defined cloud infrastructure should shield the application from impact from these processes.

Gartner predicts that cloud infrastructure will impact one trillion in IT spending making the tenants of cloud infrastructure and automation around its operations critical for success in the enterprise.  While automation primarily deals with consistent processes leveraging the abstraction of workloads from cloud infrastructure, there are some core principles required to make it effective.

Here are four things enterprises should think about as they consider implementation of an automated cloud infrastructure in particular for the data infrastructure.

1.    Policy: With an explosion of data growth, enterprises need agility and application-level control to provision and manage data through its lifecycle. Automated provisioning with application-level controls such as access and security, performance (IOPS, bandwidth and latency) and data services, coupled with scale make it foundational for automated data infrastructure. These inputs need to self-drive the data infrastructure if the IT operator has to be relieved from mundane hand crafted provisioning and management. Clearly-defined, sophisticated policies should be the first thing companies look for when considering a new solution.

2.    Performance: One of the most common criticisms of shared infrastructure  is that performance in particular latency is higher than on dedicated alternative. On-premise cloud infrastructures specifically can address these concerns. Taking advantage of low-latency data fabrics, and modern media types (such as NVMe flash, NVDIMMs), application latency can be significantly reduced, more so than dedicated infrastructure. In concert, workload isolation techniques such as QoS, multi-tenancy, and micro-segmentation gives control to the application owner. According to a Taneja research survey of IT buyers and practitioners, 40% of respondents identified the ability to better control and meet application service level commitments (SLAs) as a top priority.

3.    Security: Typically, security is associated with encryption. With the cloud, access controls and segmentation are as important to achieve security. It is imperative that these modes of security are built into the operational aspects especially with the persistent data layer in the cloud. This ensures that as workloads scale, with automated provisioning, security enforcements are intrinsic and real-time to the solution.

4.    Integrations: For cloud automation to span beyond the orchestration layer, integrations with other elements such as persistent storage layer, monitoring and event handling services, image management services, and so on, are vital. For example, in Kubernetes, the data infrastructure layer can be integrated using the FlexVolume framework to make provisioning seamless and automate to scale.

IT roles will shift dramatically over the next year as the tasks that they've traditionally managed took so much of their valuable time will be automated thus allowing IT professionals  to manage more strategic projects. By thoughtfully selecting and implementing cloud solutions, enterprise applications can smooth that transition and enjoy the benefits sooner.


About the Author 

Ashok is responsible for product direction and strategy at Datera. Prior to joining Datera, Ashok was a key part of the product team that brought Cisco UCS to market, and ran the UCS applications and cloud product teams that conceptualized FlexPod and Cisco Analytics platforms. Ashok started his career as an I/O and OS architect in HP-UX at HP. Ashok holds an MBA from UC Berkeley and MSEE from University of Arizona, Tuscon, and a number of patents in the IO and resource mgmt space.  

Ashok Rajagopalan 

Published Monday, January 09, 2017 7:02 AM by David Marshall
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