Computer Science ›› 2021, Vol. 48 ›› Issue (10): 343-350.doi: 10.11896/jsjkx.201100009

• Interdiscipline & Frontier • Previous Articles     Next Articles

Microservices User Requests Allocation Strategy Based on Improved Multi-objective Evolutionary Algorithms

ZHU Han-qing, MA Wu-bin, ZHOU Hao-hao, WU Ya-hui, HUANG Hong-bin   

  1. Key Laboratory of Information System Engineering,National University of Defense Technology,Changsha 410073,China
  • Received:2020-11-02 Revised:2021-03-01 Online:2021-10-15 Published:2021-10-18
  • About author:ZHU Han-qing,born in 1997,postgra-duate.His main research interests include microservices and scheduling theory.
    HUANG Hong-bin,born in 1975,Ph.D,professor,Ph.D supervisor.His main research interests include CPS and data analysis.
  • Supported by:
    Chinese Postdoctoral Science Foundation (2019M664033).

Abstract: How to allocate concurrent user requests to a system based on a microservices architecture to optimize objectives such as time,cost,and load balance,is one of the important issues that microservices-based application systems need to pay attention to.The existing user requests allocation strategy based on fixed rules only focuses on the solving of load balance,and it is difficult to deal with the balance between multi-objective requirements.A microservices user requests allocation model with multiple objectives of total requests processing time,load balancing rate,and total communication transmission distance is proposed to study the allocation of user requests among multiple microservices instances deployed in different resource centers.The multi-objective evolutionary algorithms with improved initial solutions generation strategy,crossover operator and mutation operator are used to solve this problem.Through many experiments on data sets of different scales,it is shown that the proposed method can better handle the balance between multiple objectives and has better solving performance,compared with the commonly used multi-objective evolutionary algorithms and traditional methods based on fixed rules.

Key words: Concurrent requests, Evolutionary algorithm, Microservices, Multi-objective optimization, Requests allocation

CLC Number: 

  • TP311.1
[1]THONE S,JOHANNE S.Microservices[J].IEEE Software,2015,32(1):116.
[2]LI Z H.Analysis of the development and impact of microservices architecture[J].China CIO News,2017(1):154-155.
[3]MA S P,FAN C Y,CHUANG Y,et al.Using Service Depen-dency Graph to Analyze and Test Microservices (Conference Paper)[J].Proceedings-International Computer Software and Applications Conference,2018,2:81-86.
[4]LEITNER P,CITO J,STOCKLI E.Modelling and ManagingDeployment Costs of Microservice-Based Cloud Applications[C]//IEEE/ACM 9TH International Conference on Utility and Cloud Computing (UCC).2016:165-174.
[5]CORNEL B,HAMZEH K,MARIOS F,et al.Delivering Elastic Containerized Cloud Applications to Enable DevOps[C] //IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).2017:65-75.
[6]REN N N.Research and Implementation of Performance Optimization Technology of Microservice-based Applications in Clouds [D].Xi'an:Xidian University,2018.
[7]MA W B,WANG R,WANG W C,et al.Micro-service composition deployment and scheduling strategy based on evolutionary multi-objective optimization[J].Systems Engineering and Electronics,2020,42(1):90-100.
[8]ZHOU X,PENG X,XIE T,et al.Benchmarking Microservice Systems for Software Engineering Research[C] //40th ACM/IEEE International Conference on Software Engineering (ICSE).2018:323-324.
[9]FU L L,ZOU S W.Research on Container Deployment of Microservices[J].Computing Technology and Automation,2019,38(4):151-155.
[10]XU C J,ZHOU X,PENG X,et al.Microservice System Oriented Runtime Deployment Optimization[J].Computer Applications and Software,2018,35(10):85-93.
[11]XIA T Y,XU J Q,JIANG M.Research of Multi-Objective Optimization Based Algorithm for Docker-Microservices Placement[J].Artificial Intelligence and Robotics Research,2017,6(2):41-55.
[12]MARIA F,ANTONIO C,RAJIV R,et al.Open Issues in Sche-duling Microservices in the Cloud[J].IEEE Cloud Computing,2016,3(5):81-88.
[13]ION-DORINEL F,FLORIN P,CRISTINA S,et al.Microser-vices Scheduling Model Over Heterogeneous Cloud-edge Environments As Support for IoT Applications[J].IEEE Internet of Things Journal,2018,5(4):2672-2681.
[14]BAO L,CHASE W,BU X X,et al.Performance modeling and workflow scheduling of microservice-based applications in clouds (Article)[J].IEEE Transactions on Parallel and Distributed Systems,2019,30(9):2101-2116.
[15]TANG Y.Design and Implementation of Job Scheduling System Based on Microservice Architecture[D].Chengdu:Southwest Jiaotong University,2019.
[16]YANG L,CAO J N,LIANG G Q,et al.Cost Aware ServicePlacement and Load Dispatching in Mobile Cloud Systems[J].IEEE Transactions on Computers,2016,65(5):1440-1452.
[17]ZHENG X J,LI J.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.
[18]XU Y.Research and implementation of related technology in web ar service platform based on micro service architecture[D].Beijing:Beijing University Of Posts And Telecommunications,2019.
[19]TAO X Y.Research on Techniques of Flow Scheduling and Request Allocation in Data Centers[D].Dalian:Dalian University of Technology,2019.
[20]ZHANG T F,MA Y,LI L,et al.Improved Genetic Algorithm for Flexible Job Shop Scheduling Problem[J].Journal of Chinese Computer Systems,2017,38(1):129-132.
[21]DEB K,JAIN H.An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach,Part I:Solving Problems With Box Constraints[J].IEEE Transactions on Evolutionary Computation,2014,18(4):577-601.
[22]WENG L G,WANG A,XIA H,et al.Improved SPEA2 based on local search[J].Application Research of Computers,2014(9):2617-2619.
[23]GAO J L,XING Q H,FAN C L,et al.Double adaptive selection strategy for MOEA/D[J].Journal of Systems Engineering and Electronics,2019,30(1):132-143.
[24]LEANDRO L M,DIRK S,YAO X.Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis[J].IEEE Transactions on Software Enginee-ring,2014,40(1):83-102.
[1] SUN Gang, WU Jiang-jiang, CHEN Hao, LI Jun, XU Shi-yuan. Hidden Preference-based Multi-objective Evolutionary Algorithm Based on Chebyshev Distance [J]. Computer Science, 2022, 49(6): 297-304.
[2] LI Hao-dong, HU Jie, FAN Qin-qin. Multimodal Multi-objective Optimization Based on Parallel Zoning Search and Its Application [J]. Computer Science, 2022, 49(5): 212-220.
[3] PENG Dong-yang, WANG Rui, HU Gu-yu, ZU Jia-chen, WANG Tian-feng. Fair Joint Optimization of QoE and Energy Efficiency in Caching Strategy for Videos [J]. Computer Science, 2022, 49(4): 312-320.
[4] GUAN Zheng, DENG Yang-lin, NIE Ren-can. Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion [J]. Computer Science, 2021, 48(9): 153-159.
[5] LI Li, LI Guang-peng, CHANG Liang, GU Tian-long. Survey of Constrained Evolutionary Algorithms and Their Applications [J]. Computer Science, 2021, 48(4): 1-13.
[6] WANG Tao, ZHANG Shu-dong, LI An, SHAO Ya-ru, ZHANG Wen-bo. Anomaly Propagation Based Fault Diagnosis for Microservices [J]. Computer Science, 2021, 48(12): 8-16.
[7] ZHOU Sheng-yi, ZENG Hong-wei. Program Complexity Analysis Method Combining Evolutionary Algorithm with Symbolic Execution [J]. Computer Science, 2021, 48(12): 107-116.
[8] WANG Ke, QU Hua, ZHAO Ji-hong. Multi-objective Optimization Method Based on Reinforcement Learning in Multi-domain SFC Deployment [J]. Computer Science, 2021, 48(12): 324-330.
[9] ZHAO Yang, NI Zhi-wei, ZHU Xu-hui, LIU Hao, RAN Jia-min. Multi-worker and Multi-task Path Planning Based on Improved Lion Evolutionary Algorithm forSpatial Crowdsourcing Platform [J]. Computer Science, 2021, 48(11A): 30-38.
[10] CUI Guo-nan, WANG Li-song, KANG Jie-xiang, GAO Zhong-jie, WANG Hui, YIN Wei. Fuzzy Clustering Validity Index Combined with Multi-objective Optimization Algorithm and Its Application [J]. Computer Science, 2021, 48(10): 197-203.
[11] HE Zhi-peng, LI Rui-lin, NIU Bei-fang. Highly Available Elastic Computing Platform for Metagenomics [J]. Computer Science, 2021, 48(1): 326-332.
[12] ZHANG Qing-qi, LIU Man-dan. Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery [J]. Computer Science, 2020, 47(8): 284-290.
[13] ZHENG You-lian, LEI De-ming, ZHENG Qiao-xian. Novel Artificial Bee Colony Algorithm for Solving Many-objective Scheduling [J]. Computer Science, 2020, 47(7): 186-191.
[14] CHEN Meng-hui, CAO Qian-feng and LAN Yan-qi. Heuristic Algorithm Based on Block Mining and Recombination for Permutation Flow-shop Scheduling Problem [J]. Computer Science, 2020, 47(6A): 108-113.
[15] ZHAO Song-hui, REN Zhi-lei, JIANG He. Multi-objective Optimization Methods for Software Upgradeability Problem [J]. Computer Science, 2020, 47(6): 16-23.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!