CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

Beenish Gul,Osman Khalid,Imran Ali Khan,Raheel Nawaz,Saad Mustafa,Darren Dancey,Syed Sajid Hussain

doi:10.1109/access.2020.2985234

Abstract

Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server’s CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server’s capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).

Highlights

Cloud computing is a shared computing paradigm that aims to provide number of services including computing, web hosting, and storage under a single platform which are otherwise offered by different service providers [1]–[3]
SPACE AND TIME COMPLEXITY The space complexity of proposed techniques namely: MAXIMUM CAPACITY AND POWER TECHNIQUE (MaxCap) and REMAINING CAPACITY AND POWER (RemCap) is computed as: O(2p + q), where, p denotes the number of virtual machine (VM) that are to be placed on servers, q is used to represent the number of servers
WORK In this paper, for efficient resource management, we added the capacity of CPU and Random Access Memory (RAM) into domain along with energy consumption to improve the server selection for VM placement

Summary

INTRODUCTION

Cloud computing is a shared computing paradigm that aims to provide number of services including computing, web hosting, and storage under a single platform which are otherwise offered by different service providers [1]–[3]. The workload consolidation can be performed using various energy efficient resource management (RM) techniques [5]–[10]. B. Gul et al.: CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds. Various RM techniques have been proposed by the researchers to reduce energy consumption by ensuring efficient utilization of resources [18]. The research community started considering RAM-based energy consumption for efficient energy management in DCs. In this paper, we present two energy-aware techniques for VM consolidation. To summarize, following are our main contributions: 1) This paper presents detailed analysis of the selected energy-efficient resource management techniques using cloud environments. 2) Two new energy-efficient SLA-aware resource management techniques namely MaxCap and RemCap are proposed to optimize energy and handle SLA violations by balancing the network load.

LITERATURE REVIEW

SPACE AND TIME COMPLEXITY The space complexity of proposed techniques namely

EXPERIMENTAL EVALUATIONS

PERFORMANCE EVALUATION

Findings

CONCLUSION AND FUTURE WORK