How ‘AIOps’ is optimizing cloud computing up and down the stack
Artificial intelligence workloads are consuming ever greater shares of information technology infrastructure resources. AI is also taking up residence as an embedded component for managing, monitoring, scaling, securing and controlling IT infrastructure.
Increasingly, this emerging IT management paradigm goes by the name “ AIOps .” This buzzword refers to two aspects of AI’s relationship to cloud infrastructures and operations. On the one hand, it refers to AI as a growing workload that infrastructure and operations are being optimized to support.
On the other, it refers to AI’s use as a tool to make infrastructure and operations more continuously self-healing, self-managing, self-securing, self-repairing and self-optimizing. In this regard, AI’s growing role in IT infrastructure management stems from its ability to automate and accelerate many tasks more scalably, predictably, rapidly and efficiently than manual methods alone. Without AI’s ability to perform continuous log analysis, anomaly detection, predictive maintenance, root cause diagnostics, closed-loop issue remediation and other critical functions, managing complex multiclouds may become infeasible or cost-prohibitive for many organizations.
Acknowledging these challenges as well as the potential productivity benefits from embedding AI in infrastructure, more enterprise IT professionals are exploring the growing range of AIOps platforms and tooling on the market. Many vendors have introduced sophisticated offering that that embed machine learning and other AI tools for intelligent, adaptive, 24×7 operation.
Wikibon sees the AIOps market segmenting into two broad solution categories:
AI-workload-optimized computing platforms
AI-augmented infrastructure optimization tools
AI-workload-optimized computing platforms
AI-optimized application infrastructure is one of today’s hottest trends in the IT business.
More vendors are introducing IT platforms that accelerate and automate AI workloads through pre-built combinations of storage, compute, and interconnect resources.
At a hardware level, AI-ready storage/compute integration is becoming a core requirement for many enterprise customers. As I discussed here a few months ago, Pure Storage Inc., a well-established provider of all-flash storage platforms, now offers AIRI , an integrated hardware/software platform for distributed training and other compute- and storage-intensive AI workloads. The product is purpose-built for a wide range of AI pipeline workloads, ranging from upfront data ingest and preparation all the way through modeling, training and operationalization.
AIRI integrates into just over a half-rack Pure Storage’s FlashBlade storage technology with four Nvidia Corp.’s DGX-1 supercomputers that run the latest Tesla V100 graphics processing units. AIRI’s storage and compute are interconnected through Arista 100GbE switches that incorporate Nvidia’s GPUDirect RDMA technology , thereby providing a direct path for high-speed, high-volume data exchange on distributed training and other AI workloads. The solution also incorporates Pure Storage’s AIRI Scaling Toolkit as well as Nvidia’s GPU Cloud deep learning software stack , a container-based environment for TensorFlow and other AI modeling frameworks.
As another proofpoint, consider how Dell EMC has configured AI into its hyperconverged infrastructure products to scale and accelerate AI workloads on multiclouds, as well as within premises-based “true private clouds” that approximate the public cloud experience. As I discussed here recently, Dell EMC recently made several important product announcements , rolling out preconfigured infrastructure products that accelerate and scale AI workloads as follows:
AI-optimized hardware that manages complex workloads in hyperconverged infrastructure: The company recently unveiled a new version of its Dell EMC VxRail hyperconverged infrastructure appliance product.
