基于利益相关视角的多维QoS云资源调度方法

doi:10.11959/j.issn.1000-436x.2019113

Abstract

Abstract:

A multidimensional cloud computing architecture is designed and a multidimensional cloud resource scheduling model is constructed based on the stakeholder perspective of cloud users and cloud service providers to meet the high QoS requirements of cloud users (such as task execution time and task completion time) with low computing costs (such as energy consumption,economic costs and system availability).For the second-level cloud resource scheduling,an MQoS cloud resource scheduling algorithm based on multiple Greedy algorithm is proposed.The experimental results show that under the four cloud computing application scenarios with no aftereffects,the MQoS cloud resource scheduling algorithm has an overall increase of 206.42%~228.99% and 34.26%~56.93 in terms of multidimensional QoS degree compared with FIFO and M2EC algorithms.It has an average overall reduction of 0.48~0.49 and 0.20~0.27 in terms of cloud data center load balance difference.

Key words: cloud computing, resource scheduling, multi-objective optimization, stakeholder perspective

CLC Number:

TP393

SU Mingfeng,WANG Guojun,LI Renfa. Multidimensional QoS cloud computing resource scheduling method based on stakeholder perspective[J]. Journal on Communications, 2019, 40(6): 102-115.

Figures/Tables 11

References 30

[1]	TOPCUOGLU H , HARIRI S , WU M Y . Performance-effective and low-complexity task scheduling for heterogeneous computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2002,13(3): 260-274.
[2]	MACE J , BODIK P , MUSUVATHI M ,et al. 2DFQ:two-dimensional fair queuing for multi-tenant cloud services[C]// The ACM Special Interest Group on Data Communication. ACM, 2016: 144-159.
[3]	GRANDL R , CHOWDHURY M , AKELLA A ,et al. Altruistic scheduling in multi-resource clusters[C]// The USENIX Symposium on Operating Systems Design and Implementation. USENIX, 2016: 65-80.
[4]	WANG Z , HAYAT M , GHANI N ,et al. Optimizing cloud-service performance:efficient resource provisioning via optimal workload allocation[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(6): 1689-1702.
[5]	DALVANDI A , GURUSAMY M , CHUA K . Application scheduling,placement,and routing for power efficiency in cloud data centers[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(4): 947-960.
[6]	陈黄科, 祝江汉, 朱晓敏 ,等. 云计算中资源延迟感知的实时任务调度方法[J]. 计算机研究与发展, 2017,54(2): 446-456.
	CHEN H K , ZHU J H , ZHU X M ,et al. Resource-delay-aware scheduling for real-time tasks in clouds[J]. Journal of Computer Research and Development, 2017,54(2): 446-456.
[7]	ZHANG H , CHEN L , YI B ,et al. CODA:toward automatically identifying and scheduling coflows in the dark[C]// The Conference of the ACM Special Interest Group on Data Communication. ACM, 2016: 160-173.
[8]	GHADERI J , . Randomized algorithms for scheduling vms in the cloud[C]// The Annual IEEE International Conference on Computer Communications. IEEE, 2016: 1-9.
[9]	KULKARNI S G , ZHANG W , HWANG J ,et al. NFVnice:dynamic backpressure and scheduling for NFV service chains[C]// The Conference of the ACM Special Interest Group on Data Communication. ACM, 2017: 71-84.
[10]	GONG Y , HE B , LI D . Network performance aware optimizations on IaaS clouds[J]. IEEE Transactions on Computers, 2017,66(4): 672-687.
[11]	刘维杰, 王丽娜, 王丹磊 ,等. 面向云计算平台的虚拟机同驻方法[J]. 通信学报, 2018,39(11): 116-128.
	LIU W J , WANG L N , WANG D L ,et al. Virtual machine co-residency method on cloud computing platform[J]. Journal on Communications, 2018,39(11): 116-128.
[12]	CHOWDHURY M , STOICA I . Efficient coflow scheduling without prior knowledge[C]// The Conference of the ACM Special Interest Group on Data Communication. ACM, 2015: 393-406.
[13]	DALVANDI A , GURUSAMY M , CHUA K C . Application scheduling,placement,and routing for power efficiency in cloud data centers[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(4): 947-960.
[14]	李智勇, 陈少淼, 杨波 ,等. 异构云环境多目标 memetic 优化任务调度方法[J]. 计算机学报, 2016,39(2): 377-390.
	LI Z Y , CHEN S M , YANG B ,et al. Multi-objective memetic algorithm for task scheduling on heterogeneous cloud[J]. Chinese Journal of Computers, 2016,39(2): 377-390.
[15]	沈尧, 秦小麟, 鲍芝峰 . 一种云环境中数据流的高效多目标调度方法[J]. 软件学报, 2017,28(3): 579-597.
	SHEN Y , QIN X L , BAO Z F . Effective multi-objective scheduling strategy of dataflow in cloud[J]. Journal of Software, 2017,28(3): 579-597.
[16]	徐久强, 郭雪静, 王进法 ,等. CPS 资源服务模型和资源调度研究[J]. 计算机学报, 2018,41(10): 2330-2343.
	XU J Q , GUO X J , WANG J F ,et al. Research on CPS resource service model and resource scheduling[J]. Chinese Journal of Computers, 2018,41(10): 2330-2343.
[17]	REN X , ANANTHANARAYANAN G , WIERMAN A ,et al. Hopper:decentralized speculation-aware cluster scheduling at scale[C]// The ACM Special Interest Group on Data Communication. ACM, 2015: 379-392.
[18]	CUI L , HAO Z , PENG Y ,et al. Piccolo:a fast and efficient rollback system for virtual machine clusters[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(8): 2328-2341.
[19]	HOMSI S , LIU S , CHAPARRO-BAQUERO G A ,et al. Workload consolidation for cloud data centers with guaranteed qos using request reneging[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(7): 2103-2116.
[20]	CHENG D Z H , ZHOU X B , LAMA P ,et al. Energy efficiency aware task assignment with DVFS in heterogeneous hadoop clusters[J]. IEEE Transactions on Parallel and Distributed Systems, 2018,29(1): 70-82.
[21]	HU Z , LI B , LUO J ,et al. Time-and cost-efficient task scheduling across geo-distributed data centers[J]. IEEE Transactions on Parallel and Distributed Systems, 2018,29(3): 705-718.
[22]	DU Y , VECIANA G D . Scheduling for cloud-based computing systems to support soft real-time applications[C]// The Conference of the ACM Special Interest Group on Data Communication. ACM, 2016: 1-9.
[23]	CUI L , CZIVA R , TSO F P ,et al. Synergistic policy and virtual machine consolidation in cloud data centers[C]// The Annual IEEE International Conference on Computer Communications. IEEE, 2016: 1-9.
[24]	YANG S , WIEDER P , YAHYAPOUR R ,et al. Reliable virtual machine placement and routing in clouds[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(10): 2965-2978.
[25]	ADAM O , LEE Y C , ZOMAYA A Y . Stochastic resource provisioning for containerized multi-tier Web services in clouds[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(7): 2060-2073.
[26]	RAMPERSAUD S , GROSU D . Sharing-aware online virtual machine packing in heterogeneous resource clouds[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(7): 2046-2059.
[27]	孙大为, 常桂然, 陈东 ,等. 云计算环境中绿色服务级目标的分析、量化、建模及评价[J]. 计算机学报, 2013,36(7): 1509-1525.
	SUN D W , CHANG G R , CHEN D ,et al. Profiling,quantifying,modeling and evaluating green service level objectives in cloud computing enviroments[J]. Chinese Journal of Computers, 2013,36(7): 1509-1525.
[28]	ADDYA S K , TURUK A K , SAHOO B ,et al. A game theoretic approach to estimate fair cost of VM Placement in cloud data center[J]. IEEE System Journal, 2018,12(4): 3509-3518.
[29]	ALHARBI F , TIAN Y C , TANG M L ,et al. An ant colony system for energy-efficient dynamic virtual machine placement in data centers[J]. Expert System witch Applications, 2019(120): 228-239.
[30]	LEBRE A , PASTOR J , SIMONET A ,et al. Putting the next 500 VMplacement algorithms to the acid test:the infrastructure provider viewpoint[J]. IEEE Transactions on Parallel and Distributed Systems, 2019,30(1): 204-217.

Metrics

Recommended 0

No Suggested Reading articles found!

符号	含义
CU	云用户
CT	云任务
PM	物理机
VM	虚拟机
CSP	云服务提供商
CDC	云数据中心
CET	云任务执行时间
CFT	云任务最终完成时间
CEC	云系统能耗
CA	云系统可用性
CE	云系统经济
CET_ijk	云任务j在云数据中心k虚拟机i的执行时间
CFT_ijk	云任务j在云数据中心k虚拟机i的最终完成时间
CEC _kl	云数据中心k的物理机l的云系统能耗
CA _km	云数据中心k的虚拟机m的云系统可用性
CE _kl	云数据中心k的物理机l的云系统经济
Qs	多维QoS度
LBD	云数据中心负载均衡差

CDC 序号	节点数/个	CPU/PE	RAM/GB	BW/(Gbit·s^-1)
DC00~DC02	200	2~4	8~32	1~10
DC03~DC05	250	4~6	8~32	1~10
DC06~DC08	300	4~6	8~64	1~20
DC09~DC11	350	4~8	8~64	1~20
DC12~DC14	400	4~8	16~128	1~40
DC15~DC17	500	4~16	16~128	1~40

VM类型	虚拟CPU/颗	RAM/GB	Storage/GB	BW/(Gbit·s^-1)
通用型	2	8	500	1
计算型	8	16	500	2
内存型	4	32	500	4
存储型	2	8	2 000	10

应用场景	CU数/个	资源差	任务差
HR-HT	1×10⁴~9×10⁴	100,1]	1,500]
HR-LT	1×10⁴~9×10⁴	10,1]	1,500]
LR-HT	1×10⁴~9×10⁴	100,1]	1,10]
LR-LT	1×10⁴~9×10⁴	10,1]	1,10]

参数	参数值	描述
δ	0.2,0.8]	基于能耗和动态能耗的调节参数
T	60	时间段
W	0.2,0.2,0.2,0.2,0.2]	五维性能指标的权重向量

Multidimensional QoS cloud computing resource scheduling method based on stakeholder perspective

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 30

Related Articles 15

Metrics

Recommended 0

[1]	Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG. Scheduling framework based on reinforcement learning in online-offline colocated cloud environment [J]. Journal on Communications, 2023, 44(6): 90-102.
[2]	Shuxu ZHAO, Ping WEI, Xiaolong WANG. Optimal coalition structure generation strategy in multi-task concurrent edge computing environment [J]. Journal on Communications, 2023, 44(2): 172-184.
[3]	Wenlong KOU, Yuyang ZHANG, Fenghua LI, Xiaogang CAO, Jiamin LI, Zhu WANG, Kui GENG. On-demand and efficient scheduling scheme for cryptographic service resource [J]. Journal on Communications, 2022, 43(6): 108-118.
[4]	Sifeng ZHU, Jianghao CAI, Zhengyi CHAI, Enlin SUN. Multi-objective optimal offloading decision for cloud-edge collaborative computing scenario in Internet of vehicles [J]. Journal on Communications, 2022, 43(6): 223-234.
[5]	Huaqun WANG, Zhe LIU, Debiao HE, Jiguo LI. Identity-based provable data possession scheme for multi-source IoT terminal data in public cloud [J]. Journal on Communications, 2021, 42(7): 52-60.
[6]	Yuanzhi HE, Cong PENG, Jihong YU, Yun LIU. Resource scheduling algorithm of satellite communication system for future multi-beam dense networking [J]. Journal on Communications, 2021, 42(4): 109-118.
[7]	Jianhong ZHANG, Menglong WU, Jing WANG, Pei LIU, Zhengtao JIANG, Changgen PENG. Secure and verifiable multi-keyword searchable encryption scheme in cloud [J]. Journal on Communications, 2021, 42(4): 139-149.
[8]	Ruiqi LI, Chunfu JIA, Yafei WANG. Multi-key homomorphic proxy re-encryption scheme based on NTRU and its application [J]. Journal on Communications, 2021, 42(3): 11-22.
[9]	Jiawei ZHANG, Jianfeng MA, Zhuo MA, Teng LI. Time-based and privacy protection revocable and traceable data sharing scheme in cloud computing [J]. Journal on Communications, 2021, 42(10): 81-94.
[10]	Wenjuan WANG, Xuehui DU, Dibin SHAN. Construction method of attack scenario in cloud environment based on dynamic probabilistic attack graph [J]. Journal on Communications, 2021, 42(1): 1-17.
[11]	Tianyi ZHU,Fenghua LI,Wei JIN,Yunchuan GUO,Liang FANG,Lin CHENG. Cross-domain access control policy mapping mechanism for balancing interoperability and autonomy [J]. Journal on Communications, 2020, 41(9): 29-48.
[12]	Youliang TIAN,Qin LUO. Verifiable multi-keyword search scheme based on improved Merkle-Tree authentication method [J]. Journal on Communications, 2020, 41(9): 118-129.
[13]	Na WANG,Kun ZHENG,Junsong FU,Jian LI. Method of ciphertext retrieval in mobile edge computing based on block segmentation [J]. Journal on Communications, 2020, 41(7): 95-102.
[14]	Pei ZHANG,Shuaijun LIU,Zhiguo MA,Xiaohui WANG,Junde SONG. Improved satellite resource allocation algorithm based on DRL and MOP [J]. Journal on Communications, 2020, 41(6): 51-60.
[15]	Lindong ZHAO,Wenqin ZHUANG,Jianxin CHEN,Liang ZHOU. Hierarchical task offloading in heterogeneous cellular network:modeling and optimization [J]. Journal on Communications, 2020, 41(4): 34-44.