Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

Abstract

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.

TARGO: Transition and Reallocation Based Green Optimization for Cloud VMs

“…Power Aware Meta Scheduler [16] is another attempt implemented from the perspective of VM scheduler. By predicting Cloud data center utilization, the scheduler manages lifecycle of the running VMs through dynamic power saving stage switches.…”

“…In this context, much GCC research has been done addressing on virtualization and VM management optimizations. Current approaches to achieve better energy and (computational) resource utilization efficiency can be generally classified into two categories: 1) server/workload consolidation solutions involving VM scheduling [1][14] [16], migration [15][18] [26], and sizing/resizing management [9][22] [24]; 2) hardware integrated solutions involving dynamic voltage and frequency scaling (DVFS) [25] and hardware cooling control mechanism [12]. As a matter of fact, these approaches can only be implemented on Cloud service provider (CSP) side (inside the Cloud).…”