scholarly journals Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
LiWei Jia ◽  
Kun Li ◽  
Xiaoming Shi
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Virtual Machines ◽  
Whale Optimization Algorithm ◽  
Distribution Model ◽  
Particle Swarm Algorithm ◽  
Whale Optimization

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nadim Rana ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi'i Muhammad Abdulhamid
Keyword(s):  
Conceptual Framework ◽  
Virtual Machine ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
Virtual Machine Scheduling

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.

Task scheduling on cloud computing based on sea lion optimization algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 99-116
Author(s):  
Raja Masadeh ◽  
Nesreen Alsharman ◽  
Ahmad Sharieh ◽  
Basel A. Mahafzah ◽  
Arafat Abdulrahman
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Sea Lion ◽  
Hunting Behavior ◽  
Content Type ◽  
Combinatorial Optimization Problems ◽  
Whale Optimization

Purpose Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions. Design/methodology/approach Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance. Findings The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

scholarly journals Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4893 ◽  
Author(s):  
Ivana Strumberger ◽  
Nebojsa Bacanin  ◽  
Milan Tuba ◽  
Eva Tuba
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
System Performance ◽  
Resource Scheduling ◽  
Whale Optimization Algorithm ◽  
Cloud System ◽  
Data Sets ◽  
Computing Paradigm ◽  
Whale Optimization

The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation–exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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