scholarly journals Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

2021 ◽  
Vol 11 (14) ◽  
pp. 6244
Author(s):  
Rohail Gulbaz ◽  
Abdul Basit Siddiqui ◽  
Nadeem Anjum ◽  
Abdullah Alhumaidi Alotaibi ◽  
Turke Althobaiti ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Search Space ◽  
Optimization Approach ◽  
Uniform Distributions ◽  
Batch Sizes ◽  
Core Issues

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

scholarly journals Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 45-51
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Work Load ◽  
Np Hard Problem ◽  
Multi Agent ◽  
Time And Energy ◽  
User Priority

Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.

scholarly journals An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 200
Author(s):  
Suleiman Sa’ad ◽  
Abdullah Muhammed ◽  
Mohammed Abdullahi ◽  
Azizol Abdullah ◽  
Fahrul Hakim Ayob
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Task Scheduling ◽  
Large Scale ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Search Space ◽  
Cloud Environment ◽  
Nature Inspired Algorithm ◽  
Symbiotic Organism Search

Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time.

scholarly journals Load Balancing Model using Elastic Technique in Cloud Computing

2021 ◽  
Vol 10 (4) ◽  
pp. 185-188
Author(s):  
Sovban Nisar ◽  
Deepika Arora
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Service Provider ◽  
Structural Design ◽  
Virtual Machines ◽  
Cloud Service ◽  
Virtual Machine Migration ◽  
Cloud Service Provider

A structural design in which virtual machines are implicated and connect to the cloud service provider is called cloud computing. On the behalf of the users, the virtual machines connect to the cloud service provider. The uncertainties overload the virtual machines. The genetic algorithm is implemented for the migration of virtual machine in the earlier study. The genetic algorithm is low depicts latency within the network is high at the time of virtual machine migration. The genetic algorithm is implemented for virtual machine migration in this study. The proposed algorithm is applied in MATLAB in this work. The obtained results are compared with the results of earlier algorithm. Various parameters like latency, bandwidth consumption, and space utilization are used to analyze the achieved results.

scholarly journals MTC: Minimizing Time and Cost of Cloud Task Scheduling based on Customers and Providers Needs using Genetic Algorithm

2021 ◽  
Vol 13 (2) ◽  
pp. 38-51
Author(s):  
Nasim Soltani Soulegan ◽  
◽  
Behrang Barekatain ◽  
Behzad Soleimani Neysiani
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Heterogeneous Computing ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Statistical Tests ◽  
Search Space ◽  
Considerable Effect ◽  
Cloud Services

Cloud computing is considered a pattern for distributed and heterogeneous computing derived from many resources, and requests aim to share resources. Recently, cloud computing is graded among the top best technologies globally, which must be scheduled favorably to maximize providers’ profit and improve service quality for their customers. Scheduling specifies how users’ requests are assigned to virtual machines, and it plays a vital role in the efficiency and capability of the system. Its objective is to have a throughput or complete jobs in minimum time and the highest standard. Scheduling jobs in heterogeneous distributed systems is an NP-hard polynomial indecisive problem that is not solvable in polynomial time for real-time scheduling. The time complexity of jobs is growing exponentially, and this problem has a considerable effect on the quality of cloud services and providers’ efficiencies. The optimization of scheduling-related parameters using heuristic and meta-heuristic algorithms can reduce the search space complexity and execution time. This study intends to represent a fitness function to minimize time and cost parameters. The proposed method uses a multi-purposed weighted genetic algorithm that provides six basic parameters: utility, task execution cost, response time, wait time, Makespan, and throughput to provide comprehensive optimization. The proposed approach improved response and wait times, throughput, Makespan, and utility 16, 9, 7, 8 percentages, respectively, by only a one cost unit reduction, which is dispensable. As a result, both providers and users will experience better services. The statistical tests show that the achieved improvement is valid for 94% of experiments.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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