Optimized Task Scheduling and Resource Allocation in Cloud Computing Using PSO based Fitness Function

2013 ◽  
Vol 12 (23) ◽  
pp. 7090-7095 ◽  
Author(s):  
Zhongxue Yang ◽  
Xiaolin Qin ◽  
Wenrui Li ◽  
Yingjie Yang
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function

Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

2020 ◽  
Vol 13 (3) ◽  
pp. 326-335
Author(s):  
Punit Gupta ◽  
Ujjwal Goyal ◽  
Vaishali Verma
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Network Cost

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).

scholarly journals Enhancement in resource allocation system for cloud environment using modified grey wolf technique

2020 ◽  
Vol 20 (3) ◽  
pp. 1530
Author(s):  
Soukaina Ouhame ◽  
Youssef Hadi
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fitness Function ◽  
Advanced Technology ◽  
Grey Wolf ◽  
Cloud Environment ◽  
Abc Algorithm ◽  
Allocation System ◽  
A New Technique ◽  
Resource Allocation System

<p class="normal">Cloud computing is an advanced technology which provides services with the help of internet. These services work under the rule of pay and gain. The services consist of hardware and software used in different fields of life. Due to growth of cloud computing the number of users are increased and their demand for better services also  increased with the passage of time. Cloud computing faces different issues. One of them is resource scheduling. In this paper a new technique is used for improvement of scheduling in cloud computing. The improvement took place in GWO algorithm. Two main sections of this algorithm are modified, which are local search section and fitness function value. The above proposed technique is used to improve three main parameters of scheduling that are energy consumption, throughput and average network executation time in VM for cloud computing. The  techniques results are compared with ABC algorithm and GWO algorithm. The results show that proposed algorithm improves in the three main parameters  of scheduling for cloud computing.</p>

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

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