Dynamic service deployment strategy in resource constrained mobile edge computing

doi:10.11772/j.issn.1001-9081.2021061615

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1662-1667.DOI: 10.11772/j.issn.1001-9081.2021061615

Special Issue: 2021年全国开放式分布与并行计算学术年会(DPCS 2021)论文

• National Open Distributed and Parallel Computing Conference 2021 (DPCS 2021） • Previous Articles Next Articles

Dynamic service deployment strategy in resource constrained mobile edge computing

Jingling YUAN¹^,², Huihua MAO¹^,², Nana WANG³, Yao XIANG²()

^1.Sanya Science and Education Innovation Park，Wuhan University of Technology，Sanya Hainan 572019，China
^2.School of Computer and Artificial Intelligence，Wuhan University of Technology，Wuhan Hubei 430070，China
^3.School of Civil Engineering and Architecture，Wuhan University of Technology，Wuhan Hubei 430070，China

Received:2021-09-13 Revised:2021-11-19 Accepted:2021-11-24 Online:2022-01-10 Published:2022-06-10
Contact: Yao XIANG
About author:YUAN Jingling， born in 1975， Ph. D.， professor. Her research interests include machine learning， distributed parallel processing， intelligent analysis.
MAO Huihua， born in 1996， M. S. candidate. His research interests include edge intelligence， edge computing.
WANG Nana， born in 1978， Ph. D. Her research interests include intelligent method， safety monitoring.
XIANG Yao， born in 1989， Ph. D. His research interests include mobile edge computing， intelligent computing.
Supported by:
National Natural Science Foundation of China(61303029);Major Science and Technology Project of Sanya Yazhou Bay Science and Technology City Administration(SKJC-KJ-2019KY02)

移动边缘计算中资源受限的动态服务部署策略

袁景凌¹^,², 毛慧华¹^,², 王娜娜³, 向尧²()

^1.武汉理工大学三亚科教创新园, 海南三亚 572019
^2.武汉理工大学计算机与人工智能学院, 武汉 430070
^3.武汉理工大学土木工程与建筑学院, 武汉 430070

通讯作者: 向尧
作者简介:袁景凌（1975—），女，湖北武汉人，教授，博士，CCF会员，主要研究方向：机器学习、分布式并行处理、智能分析
毛慧华（1996—），男，江西宜春人，硕士研究生，CCF会员，主要研究方向：边缘智能、边缘计算
王娜娜（1978—），女，山东青岛人，博士，主要研究方向：智能方法、安全监测
向尧（1989—），男，湖北宜昌人，博士，CCF会员，主要研究方向：边缘计算、智能计算。
基金资助:
国家自然科学基金资助项目(61303029);三亚崖州湾科技城管理局重大科技项目(SKJC?KJ?2019KY02)

Abstract

Abstract:

The emergence of Mobile Edge Computing （MEC） enables mobile users to easily access services deployed on edge servers with low latency. However， there are various challenges in MEC， especially service deployment issues. The number and resources of edge servers are usually limited and only a limited number of services can be deployed on the edge servers； in addition， the mobility of users changes the popularities of different services in different regions. In this context， deploying suitable services for dynamic service requests becomes a critical problem. To address this problem， by deploying appropriate services by awareness of the dynamic user requirements to minimize interaction delay， the service deployment problem was formulated as a global optimization problem， and a cluster-based resource aggregation algorithm was proposed， which initially deployed suitable services under the resource constraints such as computing and bandwidth. Moreover， considering the influence of dynamic user requests on service popularity and edge server load， a dynamic adjustment algorithm was developed to update the existing services to ensure that the Quality of Service （QoS） always met user expectations. The performance of this deployment strategy was verified through a series of simulation experiments. Simulation results show that compared with the existing benchmark algorithms， the proposed strategy can reduce service interaction delay and achieve a more stable load balance.

Key words: service deployment, Mobile Edge Computing (MEC), resource constraint, dynamic requirement awareness, service popularity

摘要：

移动边缘计算（MEC）的出现使移动用户能够以低延迟访问部署在边缘服务器上的服务。然而，MEC仍然存在各种挑战，尤其是服务部署问题。边缘服务器的数量和资源通常是有限的，只能部署数量有限的服务；此外，用户的移动性改变了不同服务在不同地区的流行度。在这种情况下，为动态请求部署合适的服务就成为一个关键问题。针对该问题，通过了解动态用户请求来部署适当的服务以最小化交互延迟，将服务部署问题表述为一个全局优化问题，并提出了一种基于集群划分的资源聚合算法，从而在计算、带宽等资源约束下初步部署合适的服务。此外，考虑动态用户请求对服务流行度及边缘服务器负载的影响，开发了动态调整算法来更新现有服务，以确保服务质量（QoS）始终满足用户期望。通过一系列仿真实验验证了所提出策略的性能。仿真结果表明，与现有基准算法相比，所提出的策略可以降低服务交互延迟并实现更稳定的负载均衡。

关键词: 服务部署, 移动边缘计算, 资源约束, 动态需求感知, 服务流行度

CLC Number:

TP393

Jingling YUAN, Huihua MAO, Nana WANG, Yao XIANG. Dynamic service deployment strategy in resource constrained mobile edge computing[J]. Journal of Computer Applications, 2022, 42(6): 1662-1667.

袁景凌, 毛慧华, 王娜娜, 向尧. 移动边缘计算中资源受限的动态服务部署策略[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1662-1667.

Figures/Tables 9

Fig. 1 An example of service deployment on edge servers

Tab. 1 Experiments parameter setting

变量	值
$c i$	均匀分布［3 000， 8 000］
$b i$	均匀分布［1 000， 3 000］
$s i$	均匀分布［1 000， 5 000］
$s c k$	均匀分布［1， 20］
$s b k$	均匀分布［1， 15］
$s s k$	均匀分布［1， 50］
$I D l o c a l$	500 ms
$β$	0.2
$θ$	0.6
$σ 1$	0.5
$σ 2$	0.2
$d i s t a n c e m a x$	200 m
$T$	1 s
$t$	1 000 s

Tab. 1 Experiments parameter setting

变量	值
$c i$	均匀分布［3 000， 8 000］
$b i$	均匀分布［1 000， 3 000］
$s i$	均匀分布［1 000， 5 000］
$s c k$	均匀分布［1， 20］
$s b k$	均匀分布［1， 15］
$s s k$	均匀分布［1， 50］
$I D l o c a l$	500 ms
$β$	0.2
$θ$	0.6
$σ 1$	0.5
$σ 2$	0.2
$d i s t a n c e m a x$	200 m
$T$	1 s
$t$	1 000 s

Tab. 2 Variable setting

参数组次	集群数量Q	服务数量	边缘服务器数量
1	5~15	1 000	100
2	10	500~1500	100
3	10	1 000	50~150

Fig. 2 Influence of cluster number on average interaction delay

Fig. 3 Influence of cluster number on load balance

Fig. 4 Influence of service number on average interaction delay

Fig. 5 Influence of service number on load balance

Fig. 6 Influence of edge server number on average interaction delay

Fig. 7 Influence of edge server number on load balance

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Dynamic service deployment strategy in resource constrained mobile edge computing

移动边缘计算中资源受限的动态服务部署策略

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