A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms

2019 ◽  
Vol 75 (12) ◽  
pp. 7994-8011 ◽  
Author(s):  
Hadeel Alazzam ◽  
Esraa Alhenawi ◽  
Rizik Al-Sayyed
Keyword(s):  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Harmony Search ◽  
Search Algorithms ◽  
Job Scheduling Algorithm

scholarly journals A New Fuzzy-based Job Scheduling Algorithm for Cluster Computing

2013 ◽  
Vol 77 (1) ◽  
pp. 33-36
Author(s):  
Behzad Azizpour ◽  
Mehdi Effatparvar ◽  
Mohammad Sadeq Garshasbi
Keyword(s):  
Cluster Computing ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Job Scheduling Algorithm

QoS-aware simulation job scheduling algorithm in virtualized cloud environment

2020 ◽  
Vol 11 (05) ◽  
pp. 2050048
Author(s):  
Zhen Li ◽  
Bin Chen ◽  
Xiaocheng Liu ◽  
Dandan Ning ◽  
Xiaogang Qiu
Keyword(s):  
Data Center ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
High Productivity ◽  
Complex Simulation ◽  
Size Adjustment ◽  
Computational Service ◽  
Job Scheduling Algorithm

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

Sign in / Sign up

Export Citation Format

Share Document