Workload-aware live storage migration for clouds

Abstract

The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This paper presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a fully implemented system on KVM and a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.

Data transformation as a means towards dynamic data storage and polyglot persistence

“…Extract and load have been heavily researched as part of data migration and has become even more apparent with the complexity introduced by clouds and big data. 13 Data migration obstacles have been solved in several ways using high-performance networks, 13 workload-aware strategies, 14 and cost-minimizing approaches. 15 In this research domain, several solutions have also been proposed for live data migration, ie, a migration where a live application needs to be supported without downtime.…”