微云环境下请求分发和容器部署成本优化

doi:10.3969/j.issn.0253-2778.2019.10.007

中国科学技术大学学报 ›› 2019, Vol. 49 ›› Issue (10): 820-827.DOI: 10.3969/j.issn.0253-2778.2019.10.007

微云环境下请求分发和容器部署成本优化

郑晓杰，李京

中国科学技术大学计算机科学与技术学院，安徽合肥 230027

收稿日期:2019-03-28 修回日期:2019-05-28 接受日期:2019-05-28 出版日期:2019-10-31 发布日期:2019-05-28
通讯作者: 李京
作者简介:郑晓杰，男，1994年生，硕士生，研究方向：移动云计算，边缘计算.E-mail:stanleye@mail.ustc.edu.cn

Cost optimization of request dispatching and container deployment in cloudlets

ZHENG Xiaojie, LI Jing

School of Computer Science and Technology,University of Science and Technology of China,hefei 230027,china

Received:2019-03-28 Revised:2019-05-28 Accepted:2019-05-28 Online:2019-10-31 Published:2019-05-28

摘要/Abstract

摘要： 随着物联网(IoT)的发展，微云(cloudlet)正在为更多低延迟、高带宽要求的应用进行服务.物联网应用请求量在时间和空间分布极其不均，如果微云仅处理周围请求，会导致一部分微云过载时，另一部分微云负载不足.此外，物联网应用的重要程度各不相同，请求量大的服务可能会抢占微云资源，使关键服务得不到执行.微云负载不均和关键服务无法执行会使云基础设施提供商成本激增.于是基于容器的微云资源分配模型，提出请求分发和容器部署成本优化问题，最优化云基础设施提供商的成本.在此基础上，提出了成本优化贪心算法（CO-Greedy），该算法能够寻找合理的请求分发和容器部署方案.实验结果表明，该算法在不同场景下表现都优于已有算法.

关键词: 微云,容器,资源分配

Abstract: With the development of the Internet of Things (IoT), cloudlet is serving more low-latency, high-bandwidth applications. The application request is dynamic in time and space. If a cloudlet only processes the surrounding requests, some cloudlets will be overloaded while others are underloaded. In addition, IoT applications vary in importance. Some unimportant services with large requests may preempt the cloudlet resources, resulting in critical services unable to be executed. Cloudlet load imbalance and critical service starvation will increase the cost of the cloud infrastructure provider. Cost optimization of request dispatching and container deployment in container-based cloudlets are investigated and a cost optimization greedy algorithm named CO-Greedy is proposed which optimizes cost by dispatching the request to the surrounding cloudlet. The experimental results show that the algorithm has better performance in all scenarios.

Key words: cloudlet, container, resource allocation

中图分类号:

TP391

郑晓杰，李京. 微云环境下请求分发和容器部署成本优化[J]. 中国科学技术大学学报, 2019, 49(10): 820-827.

ZHENG Xiaojie, LI Jing. Cost optimization of request dispatching and container deployment in cloudlets[J]. Journal of University of Science and Technology of China, 2019, 49(10): 820-827.

参考文献

［1］
SATYANARAYANAN M, BAHL P, CACERES R, et al. The case for VM-based cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009, 8(4):14-23.
[2] TRAN T X, HAJISAMI A, PANDEY P, et al. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges[J]. IEEE Communications Magazine, 2017, 55(4): 54-61.
[3] ISMAIL B I, GOORTANI E M, AB KARIM M B, et al. Evaluation of docker as edge computing platform[C]// IEEE Conference on Open Systems. Bandar Melaka, Malaysia: IEEE, 2015: 130-135.
[4] BELLAVISTA P, ZANNI A. Feasibility of fog computing deployment based on docker containerization over RaspberryPi[C]// Proceedings of the 18th International Conference on Distributed Computing and Networking. Hyderabad, India: ACM, 2017: 1-10.
[5] FARRIS I, TALEB T, FLINCK H, et al. Providing ultra-short latency to user-centric 5G applications at the mobile network edge[J]. Transactions on Emerging Telecommunications Technologies, 2018, 29(4): e3169(1-13).
[6] YANG L, CAO J N, LIANG G Q, et al. Cost aware service placement and load dispatching in mobile cloud systems[J]. IEEE Transactions on Computers, 2016, 65(5):1440-1452.
[7] WANG W, ZHAO Y L, TORNATORE M, et al. Virtual machine placement and workload assignment for mobile edge computing[C]// IEEE 6th International Conference on Cloud Networking. Prague, Czech Republic: IEEE, 2017: 1-6.
[8] BORGETTO D, MAURER M, DA-COSTA G, et al. Energy-efficient and SLA-aware management of IaaS clouds[C]// Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet. Madrid, Spanish: ACM, 2012, No.25:1-10.
[9] WU L L, GARG S K, VERSTEEG S, et al. SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments[J]. IEEE Transactions on services computing, 2014, 7(3):465-485.
[10] ZHANG F F, GE J D, LI ZJ, et al. A load-aware resource allocation and task scheduling for the emerging cloudlet system[J]. Future Generation Computer Systems, 2018, 87:438-456.
[11] YOUSEFPOUR A, PATIL A, ISHIGAKI G, et al. Fogplan: A lightweight QoS-aware dynamic fog service provisioning framework[J/E]. Internet of Things Journal /https://personal.utdallas.edu/~ashkan/papers/QDFSP.pdf, 2019.

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微云环境下请求分发和容器部署成本优化

Cost optimization of request dispatching and container deployment in cloudlets

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