A Fine-Grained and Dynamic MapReduce Task Scheduling Scheme for the Heterogeneous Cloud Environment

2015 ◽  
Author(s):  
Yingchi Mao ◽  
Haishi Zhong ◽  
Longbao Wang
Keyword(s):  
Task Scheduling ◽  
Cloud Environment ◽  
Fine Grained ◽  
Scheduling Scheme ◽  
Heterogeneous Cloud

scholarly journals DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR HETEROGENEOUS CLOUD ENVIRONMENT

2019 ◽  
Vol 01 (01) ◽  
pp. 51-62 ◽  
Author(s):  
Bhalaji N
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Cloud Environment ◽  
Wide Range ◽  
Heterogeneous Cloud ◽  
The Way

Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.

Local Pollination-Based Moth Search Algorithm for Task-Scheduling Heterogeneous Cloud Environment

2020 ◽  
Author(s):  
M Gokuldhev ◽  
G Singaravel
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Flower Pollination Algorithm ◽  
Cloud Environment ◽  
Resource Needs ◽  
Implementation Time ◽  
Heterogeneous Cloud

Abstract Nowadays, Cloud computing is a new computing model in the field of information technology and research. Generally, the cloud environment aims in providing the resource that depends upon the user’s necessity. The major problem caused by cloud computing is task scheduling. Nevertheless, the previous scheduling methods concentrate only on the resource needs, memory, implementation time and cost. In this paper, we introduced an optimal task-scheduling algorithm of the local pollination-based moth search algorithm (LPMSA), which is the hybridization of moth search algorithm (MSA) and flower pollination algorithm (FPA). The proposed LPMSA chooses an optimal solution for proper task scheduling in the cloud. Moreover, the exploitation capacity of MSA is improved by using the local search of the FPA algorithm. In this work, we use 2-fold simulation processes that are implemented under the platform of JAVA. The proposed LPMSA for task-scheduling performance is evaluated using low and high heterogeneous machines with uniform and non-uniform parameters. The experimental analysis demonstrates that the proposed LPMSA approach is well suitable for cloud task scheduling thereby reducing the makespan and energy consumption during proper task scheduling.

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