BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources

被引:52
作者
Gill, Sukhpal Singh [1 ]
Buyya, Rajkumar [1 ]
Chana, Inderveer [2 ]
Singh, Maninder [2 ]
Abraham, Ajith [3 ]
机构
[1] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst CLOUDS Lab, Parkville, Vic, Australia
[2] Thapar Univ, Comp Sci & Engn Dept, Patiala, Punjab, India
[3] Sci Network Innovat & Res Excellence, Machine Intelligence Res Labs MIR Labs, Auburn, WA USA
关键词
Cloud workload; Cloud computing; Resource scheduling; Quality of Service; Particle swarm optimization; Energy consumption; Resource provisioning; FRAMEWORK;
D O I
10.1007/s10922-017-9419-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud resource scheduling requires mapping of cloud resources to cloud workloads. Scheduling results can be optimized by considering Quality of Service (QoS) parameters as inherent requirements of scheduling. In existing literature, only a few resource scheduling algorithms have considered cost and execution time constraints but efficient scheduling requires better optimization of QoS parameters. The main aim of this research paper is to present an efficient strategy for execution of workloads on cloud resources. A particle swarm optimization based resource scheduling technique has been designed named as BULLET which is used to execute workloads effectively on available resources. Performance of the proposed technique has been evaluated in cloud environment. The experimental results show that the proposed technique efficiently reduces execution cost, time and energy consumption along with other QoS parameters.
引用
收藏
页码:361 / 400
页数:40
相关论文
共 23 条
[1]  
[Anonymous], HET COMP WORKSH HCW
[2]  
[Anonymous], ADV INF NETW APPL AI
[3]   CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J].
Calheiros, Rodrigo N. ;
Ranjan, Rajiv ;
Beloglazov, Anton ;
De Rose, Cesar A. F. ;
Buyya, Rajkumar .
SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (01) :23-50
[4]  
Chen Guo-chu, 2005, Information and Control, V34, P318
[5]   Efficient Diagnosis Protocol to Enhance the Reliability of a Cloud Computing Environment [J].
Chiang, Mao-Lun .
JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2012, 20 (04) :579-600
[6]  
El-kenawy E S T., 2012, INT J SOFT COMPUT EN, V2, P198
[7]   High Speed Network Impacts and Power Consumption Estimation for Cloud Data Centers [J].
Lago, Daniel ;
Madeira, Edmundo ;
Medhi, Deep .
30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II, 2015, :615-620
[8]   On Makespan, Migrations, and QoS Workloads' Execution Times in High Speed Data Centers [J].
Lago, Daniel ;
Madeira, Edmundo ;
Medhi, Deep .
IEICE TRANSACTIONS ON COMMUNICATIONS, 2015, E98B (11) :2099-2110
[9]   A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform [J].
Liu, Ke ;
Jin, Hai ;
Chen, Jinjun ;
Liu, Xiao ;
Yuan, Dong ;
Yang, Yun .
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, 2010, 24 (04) :445-456
[10]   Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud [J].
Moens, Hendrik ;
Truyen, Eddy ;
Walraven, Stefan ;
Joosen, Wouter ;
Dhoedt, Bart ;
De Turck, Filip .
JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2014, 22 (04) :517-558