A Holistic Auto-Scaling Algorithm for Multi-Service Applications Based on Balanced Queuing Network

被引:11
作者
Tong, Jingwan [1 ]
Wei, Mingchang [1 ]
Pan, Maolin [1 ]
Yu, Yang [1 ]
机构
[1] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou, Peoples R China
来源
2021 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, ICWS 2021 | 2021年
基金
中国国家自然科学基金;
关键词
Balanced Queuing Network; Holistic Auto-scaling; Multi-service Application; Container Cloud; SLA; ELASTICITY; AWARE;
D O I
10.1109/ICWS53863.2021.00074
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Container-supported microservice technology is widely used in cloud applications. For elastic cloud, it's vital to maintain application response time within service-level agreements (SLA) by auto-scaling technology. For applications composed of multiple services (i.e. multi-service applications), due to complex topologies, there are many factors that reduce auto-scaling algorithm performance, such as correlations among services, untimely decision, oversupply, etc. To resolve this, we propose a holistic auto-scaling algorithm (HAB) based on balanced Jackson queuing network (JQN) to reduce SLA violations rapidly with less resource cost. With the holistic auto-scaling strategy, HAB scales all services quickly and accurately. Keeping the balanced state among services, HAB saves resource cost, reduces auto-scaling decision space and simplifies algorithm parameters. The experimental results demonstrate that HAB has an average decrease of 42.31% in SLA violation rate, an average decrease of 17.88% in resource cost and an average increase of 19.39% in stability, compared with other main methods.
引用
收藏
页码:531 / 540
页数:10
相关论文
共 27 条
[1]   Elasticity in Cloud Computing: State of the Art and Research Challenges [J].
Al-Dhuraibi, Yahya ;
Paraiso, Fawaz ;
Djarallah, Nabil ;
Merle, Philippe .
IEEE TRANSACTIONS ON SERVICES COMPUTING, 2018, 11 (02) :430-447
[2]  
Dragoni N., 2017, PRESENT ULTERIOR SOF, P195
[3]   A Decentralized Autonomic Architecture for Performance Control in the Cloud [J].
Gergin, Ian ;
Simmons, Bradley ;
Litoiu, Marin .
2014 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E), 2014, :574-579
[4]   ATOM: Model-Driven Autoscaling for Microservices [J].
Gias, Alim Ul ;
Casale, Giuliano ;
Woodside, Murray .
2019 39TH IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2019), 2019, :1994-2004
[5]  
Gunther NJ, 2011, ANALYZING COMPUTER SYSTEM PERFORMANCE WITH PERL: PDQ, SECOND EDITION, P3
[6]   Enabling cost-aware and adaptive elasticity of multi-tier cloud applications [J].
Han, Rui ;
Ghanem, Moustafa M. ;
Guo, Li ;
Guo, Yike ;
Osmond, Michelle .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2014, 32 :82-98
[7]   Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms [J].
Herrera, Jose ;
Molto, German .
IEEE ACCESS, 2020, 8 :52139-52150
[8]   Efficient Cloud Auto-Scaling with SLA objective using Q-Learning [J].
Horovitz, Shay ;
Arian, Yair .
2018 IEEE 6TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD (FICLOUD 2018), 2018, :85-92
[9]  
Imdoukh M, 2019, NEURAL COMPUT APPL, P1
[10]   Unsupervised Learning of Dynamic Resource Provisioning Policies for Cloud-Hosted Multitier Web Applications [J].
Iqbal, Waheed ;
Dailey, Mathew N. ;
Carrera, David .
IEEE SYSTEMS JOURNAL, 2016, 10 (04) :1435-1446