A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks

2020 ◽  
Vol 39 (6) ◽  
pp. 8409-8417
Author(s):  
Sudan Jha ◽  
Deepak Prashar ◽  
Ahmed A. Elngar
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Ideal Solution ◽  
Swarm Optimization ◽  
Filtering Algorithm ◽  
Novel Approach ◽  
Task Accomplishment

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.

Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

2018 ◽  
Vol 17 (04) ◽  
pp. 1237-1267 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Hybrid Techniques ◽  
Heuristic Technique

Task scheduling is one of the most difficult problems which is associated with cloud computing. Due to its nature, as it belongs to nondeterministic polynomial time (NP)-hard class of problem. Various heuristic as well as meta-heuristic approaches have been used to find the optimal solution. Task scheduling basically deals with the allocation of the task to the most efficient machine for optimal utilization of the computing resources and results in better makespan. As per literature, various meta-heuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and their other hybrid techniques have been applied. Through this paper, we are presenting a novel meta-heuristic technique — genetic algorithm enabled particle swarm optimization (PSOGA), a hybrid version of PSO and GA algorithm. PSOGA uses the diversification property of PSO and intensification property of the GA. The proposed algorithm shows its supremacy over other techniques which are taken into consideration by presenting less makespan time in majority of the cases which leads up to 22.2% improvement in performance of the system and also establishes that proposed PSOGA algorithm converges faster than the others.

Task Scheduling Using an Adaptive PSO Algorithm in Cloud Computing Environment

2018 ◽  
pp. 419-426
Author(s):  
B. Sivaramakrishna ◽  
T. V. Rao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Electricity Generation ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Adaptive Pso

Now-a-days energy planners are aiming to increase the use of renewable energy sources and nuclear to meet the electricity generation. But till now coal-based power plants are the major source of electricity generation. The problem of task scheduling is one of the most important steps in taking advantage of the cloud computing environment. Various experiments show that although it is almost impossible to have an optimal solution, it seems that there is a more optimal solution using heuristic algorithms. This work compares three heuristic approaches to scheduling cloud environment tasks. These approaches are the PSO algorithm, the ACO, and the adaptive PSO algorithm for efficient task scheduling. The goal of all three of these algorithms is to generate an optimal schedule to minimize task completion time.

A PSO Algorithm Based Task Scheduling in Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Pso Algorithm ◽  
Emerging Technology ◽  
The Internet ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem ◽  
Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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.

Pso Optimization algorithm for Task Scheduling on The Cloud Computing Environment

2014 ◽  
Vol 13 (9) ◽  
pp. 4886-4897 ◽  
Author(s):  
Zahraa Tarek Abdelhamed Elmana ◽  
Magdy Zakria ◽  
Fatma A. Omara
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Computation Cost ◽  
Modified Pso ◽  
Available Resources

The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been illustrated using different time, and cost parameters and their effects in the performance measures such as utilization, speedup, and efficiency. According to the implementation results, it is found that the modified MPOS algorithm outperforms the existed PSO.

scholarly journals Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Mohammed Yousuf Uddin ◽  
Hikmat Awad Abdeljaber ◽  
Tariq Ahamed Ahanger
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Machine Scheduling ◽  
Virtual Machine Scheduling

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.

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.

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