Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen, Xinying; Xiao, Siyi

doi:10.3390/s21186212

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Zhu et al 27 designed an adaptive task scheduling algorithm (ADATSA) using learning automata based on a container, which improves resource utilization and QoS performance by optimizing resource imbalance, resource surplus, and task running state. Chen et al 28 established a container‐based microservice scheduling strategy (MOPPSO‐CMS) using a parallel particle swarm optimization algorithm. The optimization objectives of this strategy are network transmission cost, load balancing, optimization speed, and service reliability.…”

Section: Related Workmentioning

confidence: 99%

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

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The method of deploying microservices based on container technology is widely used in cloud environments. This method can realize the rapid deployment of microservices and improve the resource utilization of cloud datacenters. However, resource allocation and deployment of container-based microservices are key issues. With the continuous growth of computing-and storage-intensive services, it is necessary to consider the deployment of microservices of different business types. This study establishes a multi-objective optimization problem model with the similarity between containers and servers, load balance of clusters, and reliability of microservice execution as the optimization objectives. An improved artificial fish swarm algorithm is proposed for the container deployment of computing-and storage-intensive microservices. The comprehensive experimental results show that, compared with the existing deployment strategies, the matching degree between the container and server, cluster load balance value, service execution reliability, and other performance parameters are improved while shortening the running time of the algorithm. In addition, under the constraint of load balancing, the resource utilization of the computing and storage server clusters is improved.

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Section: Related Workmentioning

confidence: 99%

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

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“…In this way, an appropriate tradeoff between explorative and exploitative capabilities of PSO is established during the course of run and premature convergence problem is significantly mitigated. Chen et al [27] proposed MOPPSO-CMS algorithm, which can find a reasonable microservice container scheduling scheme in a short time. Rezaee Jordehi A ta al.…”

Section: Related Workmentioning

confidence: 99%

“…An application based on a microservice architecture can be represented as a tuple < M S_SET, M S_RELAT ION > [27], where M S_SET is the set of microservices related to the application and M S_RELAT ION is the set of utilization relationships among the microservices of the application. If a microservice completes a task and needs to use the result of other microservices, there is a utilization relationship between the two microservices.…”

Section: Problem Statement a Related Parametersmentioning

confidence: 99%

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Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

Chen

Xiao

2023

IEEE Access

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In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm -Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.

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“…The key to the success of the method is the ability to share information found by population individuals. Due to many advantages (simplicity, easy implementation, lack of coding and special operators) [ 4 ], the PSO method has been widely applied in solving various optimization problems, including control systems [ 10 ], prediction problems [ 11 ], image classification [ 12 ], energy management [ 13 ], bilevel programming problems [ 14 , 15 ], antenna design [ 16 ], scheduling problems [ 17 , 18 ], electromagnetism [ 19 , 20 ] and many others.…”

Section: Introductionmentioning

confidence: 99%

Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

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Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Cited by 9 publications

References 28 publications

A container deployment strategy for server clusters with different resource types

A container deployment strategy for server clusters with different resource types

Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

Learning Competitive Swarm Optimization

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