Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments

被引:17
作者
Asghari, Ali [1 ]
Sohrabi, Mohammad Karim [1 ]
机构
[1] Islamic Azad Univ, Semnan Branch, Dept Comp Engn, Semnan, Iran
关键词
Cloud computing; Resource utilization; Machine learning; Coral reefs algorithm; Load balancing; EVOLUTIONARY ALGORITHMS; SCIENTIFIC WORKFLOWS; GENETIC ALGORITHM; IAAS; MANAGEMENT; MODEL;
D O I
10.1007/s00607-021-00920-2
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Resource management is the process of task scheduling and resource provisioning to provide requirements of cloud users. Since cloud resources are often heterogeneous, task scheduling and resource provisioning are major challenges in this area. Various methods have been introduced to improve resource utilization and thus increase the efficiency of cloud computing. Existing methods can be divided into several categories, including mathematical and statistical methods, heuristic- and meta-heuristic-based methods, and machine-learning-based methods. Since the resource management problem is NP-complete, several optimization methods have been also exploited in this area. Coral reefs algorithm is an evolutionary method that has showed appropriate convergence and response time for some problems, and thus is used in this paper to combine with reinforcement learning to improve efficiency of resource management in cloud environments. The proposed method of this paper consists of two phases. The initial allocation of resources to ready-to-perform tasks is done using the coral reefs algorithm in the first phase. The tasks are considered as corals and the resources are considered reefs in this method. The second phase utilizes reinforcement learning to avoid falling into the local optima and to make optimal use of resources using a long-term approach. The proposed model of this paper, called MO-CRAML, introduces a new hybrid algorithm for improving utilization and load balancing of cloud resources using the combination of coral reefs optimization algorithm and reinforcement learning. The results of the experiments show that the proposed algorithm has better performance in cloud resource utilization and load balancing in comparison with some other important methods of the literature.
引用
收藏
页码:1545 / 1567
页数:23
相关论文
共 62 条
[1]   Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud [J].
Adhikari, Mainak ;
Nandy, Sudiirshan ;
Amgoth, Tarachand .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2019, 128 :64-77
[2]  
Agarwal M., 2018, ADV COMPUTER COMPUTA, DOI [10.1007/978-981-10-3773-3_29, DOI 10.1007/978-981-10-3773-3_29]
[3]   An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems [J].
Akbari, Mehdi ;
Rashidi, Hassan ;
Alizadeh, Sasan H. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 61 :35-46
[4]   Metaheuristic optimization frameworks: a survey and benchmarking [J].
Antonio Parejo, Jose ;
Ruiz-Cortes, Antonio ;
Lozano, Sebastian ;
Fernandez, Pablo .
SOFT COMPUTING, 2012, 16 (03) :527-561
[5]   A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents [J].
Asghari, Ali ;
Sohrabi, Mohammad Karim ;
Yaghmaee, Farzin .
COMPUTER NETWORKS, 2020, 179
[6]   Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm [J].
Asghari, Ali ;
Sohrabi, Mohammad Karim ;
Yaghmaee, Farzin .
JOURNAL OF SUPERCOMPUTING, 2021, 77 (03) :2800-2828
[7]   Online scheduling of dependent tasks of cloud's workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents [J].
Asghari, Ali ;
Sohrabi, Mohammad Karim ;
Yaghmaee, Farzin .
SOFT COMPUTING, 2020, 24 (21) :16177-16199
[8]   A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments [J].
Azgomi, Hossein ;
Sohrabi, Mohammad Karim .
APPLIED INTELLIGENCE, 2019, 49 (11) :3965-3989
[9]   CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J].
Calheiros, Rodrigo N. ;
Ranjan, Rajiv ;
Beloglazov, Anton ;
De Rose, Cesar A. F. ;
Buyya, Rajkumar .
SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (01) :23-50