StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

SANFS Maestro: A SAN File System Planner

been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). Copies may be requested from IBM T. J. Watson Research Center, P

Abstract

Network (SAN) File System

Towards a middleware for configuring large-scale storage infrastructures

The rapid proliferation of cloud and service-oriented computing infrastructure is creating an ever increasing thirst for storage within data centers. Ideally management applications in cloud deployments should operate in terms of high-level goals, and not present specific implementation details to administrators. Cloud providers often employ Storage Area Networks (SANs) to gain storage scalability. SAN configurations have a vast parameter space, which makes them one of the most difficult components to configure and manage in a cloud storage offering. As a step towards a general cloud storage configuration platform, this paper introduces a SAN configuration middleware that aids management applications in their task of updating and troubleshooting heterogeneous SAN deployments. The middleware acts as a proxy between management applications and a central repository of SAN configurations. The central repository is designed to validate SAN configurations against a knowledge base of best practice rules across cloud deployments. Management applications contribute local SAN configurations to the repository, and also subscribe to proactive notifications for configurations now no longer considered safe