MPSO

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Subasish Mohapatra ◽  
Mitrabinda Ray
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.

A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing

2021 ◽  
pp. 504-526
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Mitrabinda Ray ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.

A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing

2018 ◽  
Vol 8 (1) ◽  
pp. 29-49 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Mitrabinda Ray ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 551 ◽  
Author(s):  
Mazen Farid ◽  
Rohaya Latip ◽  
Masnida Hussin ◽  
Nor Asilah Wati Abdul Hamid
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Innovative Technology ◽  
Utilization Rate ◽  
Future Research ◽  
Workflow Model ◽  
Swarm Optimization

Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

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