Studies In Automatic Management Of Storage Systems

• Main Author: Pipada, Pankaj
• Other Authors: Gopinath, K
• Language: en_US
• Published: 2015
• Subjects: Automatic Computer Storage Management; Computer Storage Systems; Machine Learning; Automatic Virtual Machine Migration; Fault-tolerant Computing; Automated Workload Identification; Automatic VM Migration; Computer Storage Optimization; Adaptive Storage Management; Workload Phase Identification; Fault Tolerance; Workload Identification; LoadIQ; Computer Science
• Online Access: http://etd.iisc.ernet.in/handle/2005/2489; http://etd.ncsi.iisc.ernet.in/abstracts/3210/G25425-Abs.pdf

Autonomic management is important in storage systems and the space of autonomics in storage systems is vast. Such autonomic management systems can employ a variety of techniques depending upon the specific problem. In this thesis, we first take an algorithmic approach towards reliability enhancement and then we use learning along with a reactive framework to facilitate storage optimization for applications. We study how the reliability of non-repairable systems can be improved through automatic reconfiguration of their XOR-coded structure. To this regard we propose to increase the fault tolerance of non-repairable systems by reorganizing the system, after a failure is detected, to a new XOR-code with a better fault tolerance. As errors can manifest during reorganization due to whole reads of multiple submodules, our framework takes them in to account and models such errors as based on access intensity (ie.BER-biterrorrate). We present and evaluate the reliability of an example storage system with and without reorganization. Motivated by the critical need for automating various aspects of data management in virtualized data centers, we study the specific problem of automatically implementing Virtual Machine (VM) migration in a dynamic environment according to some pre-set policies. This is a problem that requires automated identification of various workloads and their execution environments running inside virtual machines in a non-intrusive manner. To this end we propose AuM (for Autonomous Manager) that has the capability to learn workloads by aggregating variety of information obtained from network traces of storage protocols. We use state of the art Machine Learning tools, namely Multiple Kernel learning ,to aggregate information and show that AuM is indeed very accurate in identifying work loads, their execution environments and is also successful in following user set policies very closely for the VM migration tasks. Storage infrastructure in large-scale cloud data center environments must support applications with diverse, time-varying data access patterns while observing the quality of service. To meet service level requirements in such heterogeneous application phases, storage management needs to be phase-aware and adaptive ,i.e. ,identify specific storage access patterns of applications as they occur and customize their handling accordingly. We build LoadIQ, an online application phase detector for networked (file and block) storage systems. In a live deployment , LoadIQ analyzes traces and emits phase labels learnt online. Such labels could be used to generate alerts or to trigger phase-specific system tuning.