Research perspective of job scheduling in cloud computing

2017 ◽  
Author(s):  
K. Sutha ◽  
G. M. Kadhar Nawaz
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Research Perspective

scholarly journals A fuzzy energy and security aware scheduling in cloud

2017 ◽  
Vol 7 (1.2) ◽  
pp. 117
Author(s):  
Sirisati Ranga Swamy ◽  
Sridhar Mandapati
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Fuzzy Inference ◽  
Turnaround Time ◽  
Time Cost ◽  
Inference System ◽  
Np Hard Problem ◽  
Fuzzy Energy ◽  
The One ◽  
The Membership Function

The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

Current Trends in Cloud Computing for Data Science Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 80-99
Author(s):  
Syed Imran Jami ◽  
Siraj Munir
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Data Science ◽  
Job Scheduling ◽  
Resource Scheduling ◽  
Data Intensive ◽  
Organizational Issues ◽  
Current Trends ◽  
Recent Trends ◽  
And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

Job Scheduling in Cloud Computing Based on DGPSO

2021 ◽  
pp. 33-45
Author(s):  
J. Arul Sindiya ◽  
R. Pushpalakshmi
Keyword(s):  
Cloud Computing ◽  
Job Scheduling

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

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