ACCRS: autonomic based cloud computing resource scaling

被引:22
作者
Al-Sharif, Ziad A. [1 ]
Jararweh, Yaser [2 ]
Al-Dahoud, Ahmad [2 ]
Alawneh, Luay M. [1 ]
机构
[1] Jordan Univ Sci & Technol, Software Engn Dept, Irbid 22110, Jordan
[2] Jordan Univ Sci & Technol, Comp Sci Dept, Irbid 22110, Jordan
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2017年 / 20卷 / 03期
关键词
Cloud computing; Resource scaling; Autonomic computing; Quality of service; Energy efficiency; ENVIRONMENTS; DIAGNOSIS;
D O I
10.1007/s10586-016-0682-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A cloud computing model gives cloud service providers the ability to retain multiple workloads on a single physical system. However, efficient resource provisioning and possible system fault management in the cloud can be a challenge. Early fault detection can provide room to recover from potential faults before impacting QoS. Current static techniques of fault management in computing systems are not satisfactory enough to safeguard the QoS requested by cloud users. Thus, new smart techniques are needed. This paper presents the ACCRS framework for cloud computing infrastructures to advance system's utilization level, reduce cost and power consumption and fulfil SLAs. The ACCRS framework employs Autonomic Computing basic components which includes state monitoring, planning, decision making, fault predication, detection, and root cause analysis for recovery actions to improve system's reliability, availability, and utilization level by scaling resources in response to changes in the cloud system state.
引用
收藏
页码:2479 / 2488
页数:10
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