scholarly journals A Parallel Particle Swarm Optimisation for Selecting Optimal Virtual Machine on Cloud Environment

2020 ◽  
Vol 10 (18) ◽  
pp. 6538
Author(s):  
Ahmed Abdelaziz ◽  
Maria Anastasiadou ◽  
Mauro Castelli
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Healthcare Services ◽  
Turnaround Time ◽  
Particle Swarm Optimisation ◽  
Medical Applications ◽  
Implementation Time ◽  
Better Than

Cloud computing has a significant role in healthcare services, especially in medical applications. In cloud computing, the best choice of virtual machines (Virtual_Ms) has an essential role in the quality improvement of cloud computing by minimising the execution time of medical queries from stakeholders and maximising utilisation of medicinal resources. Besides, the best choice of Virtual_Ms assists the stakeholders to reduce the total execution time of medical requests through turnaround time and maximise CPU utilisation and waiting time. For that, this paper introduces an optimisation model for medical applications using two distinct intelligent algorithms: genetic algorithm (GA) and parallel particle swarm optimisation (PPSO). In addition, a set of experiments was conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The PPSO algorithm was implemented using the MATLAB tool. The results showed that the PPSO algorithm gives accurate outcomes better than the GA in terms of the execution time of medical queries and efficiency by 3.02% and 37.7%, respectively. Also, the PPSO algorithm has been implemented on the CloudSim package. The results displayed that the PPSO algorithm gives accurate outcomes better than default CloudSim in terms of final implementation time of medicinal queries by 33.3%. Finally, the proposed model outperformed the state-of-the-art methods in the literature review by a range from 13% to 67%.

A New Balanced Particle Swarm Optimisation for Load Scheduling in Cloud Computing

2018 ◽  
Vol 17 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Divya Chaudhary ◽  
Bijendra Kumar
Keyword(s):  
Cloud Computing ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Transfer Time ◽  
Load Scheduling ◽  
The Social ◽  
First Come First Serve ◽  
Basic Objective

The cloud computing is an augmentative and progressive paradigm that supports a huge amount of characteristics. It demands the optimal allocation of resources to the tasks present in the virtual machines (VMs) system using load scheduling algorithms. The basic objective of load scheduling is to avoid system overloading and thereby achieve higher throughput by maximising VM utilisation along with cost stabilisation. The first come first serve and min–min approaches allocate the load in a static manner and resources are left underutilised. The particle swarm optimisation obtains the motivation from the social behaviour of the flock of birds. It analyses various approaches for load scheduling. The paper proposes an improved balanced load scheduling approach based on particle swarm optimisation (BPSO) to minimise total transfer time and total cost stabilisation. The proposed BPSO approach is compared with the existing approaches used for load scheduling in cloudlets. The efficiency in terms of the transfer time and cost of the proposed algorithm is showcased with the help of simulation results. As evident from the results, the proposed algorithm reduces transfer time and cost than the prevalent algorithms thereby making a system with stable cost.

Receiver Initiated Deadline Aware Load Balancing Strategy (RDLBS) for Cloud Environment

2017 ◽  
Vol 8 (3) ◽  
pp. 53-73
Author(s):  
Raza Abbas Haidri ◽  
Chittaranjan Padmanabh Katti ◽  
Prem Chandra Saxena
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Load Balancing ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Computing Technology ◽  
Trade Off ◽  
The Cost ◽  
State Of Art

The emerging cloud computing technology is the attention of both commercial and academic spheres. Generally, the cost of the faster resource is more than the slower ones, therefore, there is a trade-off between deadline and cost. In this paper, the authors propose a receiver initiated deadline aware load balancing strategy (RDLBS) which tries to meet the deadline of the requests and optimizes the rate of revenue. RDLBS balances the load among the virtual machines (VMs) by migrating the request from the overloaded VMs to underloaded VMs. Turnaround time is also computed for the performance evaluation. The experiments are conducted by using CloudSim simulator and results are compared with existing state of art algorithms with similar objectives.

scholarly journals Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Redwan A. Al-dilami ◽  
Ammar T. Zahary ◽  
Adnan Z. Al-Saqqaf
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Setup Time ◽  
Setup Cost ◽  
Idle State ◽  
The Cost ◽  
Computing Infrastructure ◽  
Better Than

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

scholarly journals Measuring the Performance on Load Balancing Algorithms

2019 ◽  
pp. 41-49 ◽  
Author(s):  
Saumendu Roy ◽  
Dr. Md. Alam Hossain ◽  
Sujit Kumar Sen ◽  
Nazmul Hossain ◽  
Md. Rashid Al Asif
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Load Balancing ◽  
Virtual Machines ◽  
Huge Amount ◽  
Heavy Load ◽  
Cloud Server ◽  
Load Balancing Algorithm ◽  
Day By Day ◽  
Better Than

Load balancing is an integrated aspect of the environment in cloud computing. Cloud computing has lately outgoing technology. It has getting exoteric day by day residence widespread chance in close to posterior. Cloud computing is defined as a massively distributed computing example that is moved by an economic scale in which a repertory of abstracted virtualized energetically. The number of clients in cloud computing is increasing exponentially. The huge amount of user requests attempt to entitle the collection for numerous applications. Which alongside with heavy load not far afield off from cloud server. Whenever particular (Virtual Machine) VMs are overloaded then there are no more duties should be addressed to overloaded VM if under loaded VMs are receivable. For optimizing accomplishment and better response or reaction time the load has to be balanced between overloaded VMs (virtual machines). This Paper describes briefly about the load balancing accession and identifies which is better than others (load balancing algorithm).

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

scholarly journals Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 332-338
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Execution Time ◽  
Pso Algorithm ◽  
Turnaround Time ◽  
Bandwidth Utilization ◽  
Scheduling Optimization ◽  
Swarm Optimization ◽  
Memory Utilization

Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

scholarly journals Evaluation of cloud computing resource scheduling based on improved optimization algorithm

2020 ◽  
Author(s):  
Huafeng Yu
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Operation Time ◽  
Swarm Optimization ◽  
Ipso Algorithm

Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

scholarly journals Job Scheduling Based on Dependent and Independent Task using Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 1600-1606
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Execution Time ◽  
Mobile Cloud Computing ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Particle Swarm ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Dependent Tasks

Mobile Cloud Computing is an accumulation of both Cloud Computing and Mobile Computing. In cloud computing resources are deployed to a client on-demand basis. Mobile cloud computing is similar to cloud computing except that some devices involved in mobile cloud computing should be mobile. The demand for MCC has been increasing due to its scalability, reliability, high QOS (Quality Of Services), longer battery life, large storage capacity. Mobile cloud computing aims to take benefit of limited resources provided by a cloud provider. Task scheduling is a major concept involved in executing a task. In cloud computing job scheduling is required to execute each job without any deadlock. But the scheduling of dependent tasks is a problem in cloud systems. This problem is an NP-complete problem and can be solved using various heuristic and metaheuristic approaches. These approaches give high-quality solutions with reasonable execution time. Particle Swarm Optimization (PSO) is one of these meta-heuristic approaches that solve the problem of grid scheduling. In this paper, we address the problem encounter in dynamic scheduling. In dynamic scheduling, each task has its own deadline completion time. The task that arrived earlier in the system occupied the resources first and later arrived tasks are rejected because their execution time exceeds the deadline. In this paper, we proposed PSO with a variable job identifier that identifies independent and dependent tasks from the population. The particles are arranged with a grid dynamically and influence swarm to minimize execution time and waiting time simultaneously. The experimental studies show that the proposed approach is more efficient than other PSO based approaches as described in the literature

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