scholarly journals A priority based on min-min algorithm for reducing make span task scheduling in cloud computing

2018 ◽  
Vol 7 (4) ◽  
pp. 2039
Author(s):  
Abdulrahman Mohammed Hussein Obaid ◽  
Santosh Kumar Pani ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Span Task ◽  
Third Party ◽  
Second Phase ◽  
Two Phase ◽  
A Priority ◽  
Priority Algorithm

This paper proposes a two-phase technique for task scheduling which works on third-party broker. The priority algorithm is executed by selecting the task that has the highest priority. However, if more than one task has the same priority; it goes to second phase to execute the traditional Min-Min algorithm. Experiments are conducted by considering random tasks in order to compare the performance of the pro-posed algorithm with the Min-Min algorithm. The recorded experimental outcomes indicate that the proposed technique is given 10% better results as compared to the traditional Min-Min algorithm.

Task scheduling by a priority queuing system to a robotized assembly

1991 ◽  
Vol 24 (14) ◽  
pp. 252-254
Author(s):  
Yang Yang ◽  
M. Staroswiecki
Keyword(s):  
Task Scheduling ◽  
Queuing System ◽  
A Priority

A novel multiclass priority algorithm for task scheduling in cloud computing

2021 ◽  
Author(s):  
Hicham Ben Alla ◽  
Said Ben Alla ◽  
Abdellah Ezzati ◽  
Abdellah Touhafi
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Priority Algorithm

A Priority Base Task Scheduling on Virtual Machines using Workflowsim

2016 ◽  
Vol 15 (2) ◽  
pp. 39-45
Author(s):  
Pradeep Singh Rawat ◽  
Priti Dimri ◽  
Varun Barthwal
Keyword(s):  
Task Scheduling ◽  
Virtual Machines ◽  
A Priority

Study and Simulation of CPU Priority Scheduling Algorithm on Multi-Core Processor Platform

2013 ◽  
Vol 694-697 ◽  
pp. 2540-2544
Author(s):  
Zi Guo Fan ◽  
Rong Liang Wang ◽  
Peng Hao Yang
Keyword(s):  
Operating Systems ◽  
Load Balance ◽  
Scheduling Algorithm ◽  
Priority Scheduling ◽  
Response Performance ◽  
The Mean ◽  
A Priority ◽  
Multi Core Processor ◽  
Priority Algorithm

Multi-core processor technology is getting more and more common for both business and private use. However, the operating systems and applications are not keeping the same pace with multi-core hardware. In the mean while, to get better performance, more factors need to be considered under multi-core platform, e.g. load balance, cache, task relationship, etc. This paper focuses on making full use of multi-core processor through scheduling the tasks to proper core with CPU priority Algorithm which calculates a priority of each core when scheduling a task. With CPU Priority, it is easier to take interesting factors into account and combine several factors together. We did our work based on a scheduler simulator implemented with Python and we observed that, with CPU priority scheduling algorithm, it does suggest a flexible way to schedule the CPU assignment and is able to gain some satisfactory improvement on the response performance according to our simulation.

Priority-Based Task Scheduling in the Cloud Systems Using a Memetic Algorithm

2016 ◽  
Vol 25 (10) ◽  
pp. 1650119 ◽  
Author(s):  
Bahman Keshanchi ◽  
Nima Jafari Navimipour
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Memetic Algorithm ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Cloud Environment ◽  
Distributed Computing Systems ◽  
Complete Problems ◽  
Np Complete ◽  
A Priority

Task scheduling is one of the major issues to achieve high performance in distributed systems such as Grid, Peer-to-Peer and cloud environment. Generally, there are two phases in heuristics-based task scheduling algorithms in heterogeneous distributed computing systems (HeDCSs). These phases are task prioritization and processor assigning respectively. Heuristic-based task scheduling algorithms may use different policies to assign priority to subtasks which produce different makespans in a heterogeneous computing system. Thus, a suitable scheduling algorithm is one that can efficiently assign a priority to tasks in order to minimize makespan. Recently, memetic algorithms (MAs) have been used as evolutionary or population-based global search approaches with local search heuristic to optimize NP-complete problems. Recent studies on MAs have discovered their success on a wide variety of real-world problems. Since the task scheduling problem is an NP-complete, in this paper, a new task scheduling algorithm on cloud environment using multiple priority queues and a memetic algorithm (MPQMA) is proposed. The proposed method uses a genetic algorithm (GA) along with hill climbing to assign a priority to each subtask while using a heuristic-based earliest finish time (EFT) approach to search for a solution for the task-to-processor mapping. The basic idea of our approach is using the advantage of MA to increase the convergence speed of the solutions. We implemented the algorithm on Azure Cloud Service by C# language where the experimental results for the set of randomly generated graphs revealed that the proposed MPQMA algorithm outperformed the existing three task scheduling algorithms in terms of makespan with fast convergence to the optimized solution.

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