FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds

计算机科学 容错 云计算 分布式计算 供应 CloudSim公司 调度(生产过程) 虚拟化 备份 虚拟机 工作量 算法 计算机网络 操作系统 工程类 运营管理
作者
Ji Wang,Weidong Bao,Xiaomin Zhu,Laurence T. Yang,Yang Xiang
出处
期刊:IEEE Transactions on Computers [Institute of Electrical and Electronics Engineers]
卷期号:64 (9): 2545-2558 被引量:100
标识
DOI:10.1109/tc.2014.2366751
摘要

As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users. Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature. Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role. However, few researches on the fault-tolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently. To address this issue, this paper presents a fault-tolerant mechanism which extends the primary-backup model to incorporate the features of clouds. Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization. On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel fault-tolerant elastic scheduling algorithms for real-time tasks in clouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds. Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, i.e., Non-M igration-FESTAL (NMFESTAL), Non-Overlapping-FESTAL (NOFESTAL), and Elastic First Fit (EFF). The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
田様应助空间采纳,获得10
刚刚
李善聪完成签到,获得积分10
刚刚
隐形曼青应助空间采纳,获得10
刚刚
彭于晏应助空间采纳,获得10
刚刚
李健的小迷弟应助空间采纳,获得10
刚刚
斯文败类应助空间采纳,获得10
刚刚
科目三应助空间采纳,获得10
1秒前
夏成蹊完成签到,获得积分10
1秒前
1秒前
oo发布了新的文献求助30
1秒前
zuanyhou发布了新的文献求助10
1秒前
碧蓝的往事完成签到 ,获得积分10
2秒前
2秒前
背后如彤完成签到 ,获得积分10
2秒前
3秒前
橘子面包完成签到 ,获得积分10
3秒前
Connor完成签到,获得积分10
3秒前
cxmei完成签到,获得积分10
3秒前
3秒前
失眠成败发布了新的文献求助30
4秒前
4秒前
WANG同学发布了新的文献求助20
4秒前
4秒前
4秒前
4秒前
寻空完成签到,获得积分10
5秒前
科研通AI6.4应助千羽采纳,获得10
5秒前
百浪多息完成签到,获得积分10
5秒前
小小鱼发布了新的文献求助10
6秒前
lei完成签到,获得积分10
7秒前
哼哼哈嘿发布了新的文献求助10
8秒前
Cola完成签到,获得积分0
8秒前
甜美阁完成签到,获得积分10
8秒前
nn发布了新的文献求助10
8秒前
suzzky发布了新的文献求助10
8秒前
我哈哈哈发布了新的文献求助10
9秒前
youngster00发布了新的文献求助10
9秒前
10秒前
干净的南烟完成签到,获得积分10
10秒前
唐唐完成签到,获得积分10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Management and the Arts 310
Teaching Social and Emotional Learning in Physical Education 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7634820
求助须知:如何正确求助?哪些是违规求助? 9208909
关于积分的说明 19750140
捐赠科研通 7202865
什么是DOI,文献DOI怎么找? 3275133
关于科研通互助平台的介绍 2436999
邀请新用户注册赠送积分活动 2272066