A Fuzzy Enabled Genetic Algorithm for Task Scheduling Problem in Cloud Computing

2020 ◽  
Vol 10 (3) ◽  
pp. 334-344
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fundamental Problem ◽  
Data Sets ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Roulette Wheel Selection ◽  
Np Hard Problem

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.

Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment

2020 ◽  
Vol 13 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Pradeep Singh Rawat ◽  
Priti Dimri ◽  
Punit Gupta
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Low Cost ◽  
Big Bang ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Big Crunch ◽  
Resource Cost

: 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 and Round Robin improve the performance but these are not cost efficient at the same time. : Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model. : The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost).

scholarly journals HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

2018 ◽  
Vol 17 (2) ◽  
pp. 7236-7246 ◽  
Author(s):  
Rasha Ali Al-Arasi ◽  
Anwar Saif
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Application Software ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm ◽  
Np Hard Problem ◽  
Software And Hardware

Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.

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