RPA: a flexible scheduling algorithm for input buffered switches

1999 ◽  
Vol 47 (12) ◽  
pp. 1921-1933 ◽  
Author(s):  
M.A. Marsan ◽  
A. Bianco ◽  
E. Leonardi ◽  
L. Milia
Keyword(s):  
Scheduling Algorithm ◽  
Flexible Scheduling

scholarly journals A Flexible Scheduling Algorithm for Storage Tanks and Its Application to Production Planning Problems.

1991 ◽  
Vol 17 (6) ◽  
pp. 1154-1160
Author(s):  
Yoshinori Kutsuwa ◽  
Hisayoshi Matsuyama ◽  
Atsuo Kodama ◽  
Morimasa Ogawa
Keyword(s):  
Production Planning ◽  
Scheduling Algorithm ◽  
Flexible Scheduling ◽  
Storage Tanks ◽  
Planning Problems

scholarly journals A flexible scheduling algorithm for the 5th-generation networks

2021 ◽  
Vol 2 (2) ◽  
pp. 101-107
Author(s):  
Lanlan Li ◽  
Wentao Shao ◽  
Xin Zhou
Keyword(s):  
Scheduling Algorithm ◽  
Flexible Scheduling

Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

2021 ◽  
pp. 165-174
Author(s):  
Ahmed A. A. Gad-Elrab ◽  
Tamer A.A. Alzohairy ◽  
Kamal R. Raslan ◽  
Farouk A. Emara
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Heuristic Methods ◽  
Flexible Scheduling ◽  
Np Completeness ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
User Tasks ◽  
Available Resources

Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.

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