Task Scheduling in Parallel Systems using Genetic Algorithm

Singh, Rahul

doi:10.5120/18999-0470

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, conclusion and future scope is demonstrated in Section 4. In Figure 2, shows the vertex with a weight that represents task processing time and edges represent the communication and dependency time betwixt the tasks [12,3]. The DAG Graph (G) is defined as G = (V, E), locus V is set of vertex/node and E is set of edges.…”

Section: Bounded Number Of Processors (Bnp) Scheduling Algorithmsmentioning

confidence: 99%

A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

Kaur¹

2016

IJGDC

View full text Add to dashboard Cite

show abstract

Section: Bounded Number Of Processors (Bnp) Scheduling Algorithmsmentioning

confidence: 99%

A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

Kaur¹

2016

IJGDC

View full text Add to dashboard Cite

show abstract

“…It is well known that the scheduling problem is NP complete. Many interesting heuristics are proposed to solve it, we mention greedy algorithms ( Čiegis and Šilko, 2002), genetic algorithms (Sharma and Kaur, 2015), (Singh, 2014), simulated annealing and tabu search algorithms (Kirkpatrick et al, 1983), (Glover, 1989), (Glover, 1990). Such algorithms include a possibility of dynamic scheduling and allow for tasks to arrive continuously and they can consider variable in time computational resources.…”

Section: Introductionmentioning

confidence: 99%

A Three-Level Parallelisation Scheme and Application to the Nelder-Mead Algorithm

Kriauzienė

Bugajev

Čiegis

2020

Mathematical Modelling and Analysis

View full text Add to dashboard Cite

We consider a three-level parallelisation scheme. The second and third levels define a classical two-level parallelisation scheme and some load balancing algorithm is used to distribute tasks among processes. It is well-known that for many applications the efficiency of parallel algorithms of these two levels starts to drop down after some critical parallelisation degree is reached. This weakness of the twolevel template is addressed by introduction of one additional parallelisation level. s an alternative to the basic solver some new or modified algorithms are considered on this level. The idea of the proposed methodology is to increase the parallelisation degree by using possibly less efficient algorithms in comparison with the basic solver. As an example we investigate two modified Nelder-Mead methods. For the selected application, a Schro¨dinger equation is solved numerically on the second level, and on the third level the parallel Wang’s algorithm is used to solve systems of linear equations with tridiagonal matrices. A greedy workload balancing heuristic is proposed, which is oriented to the case of a large number of available processors. The complexity estimates of the computational tasks are model-based, i.e. they use empirical computational data.

show abstract

“…The honey bee algorithm is not distance dependent. Parallel ant colony algorithm is need of the hour in which multiple process can be executed at the same time [13], [14], [18].…”

Section: Resource Scheduling Policymentioning

confidence: 99%

A Review of Various Scheduling Techniques Considering Energy Efficiency in WSN

Kaur¹,

Sharma²

2017

IJCA

View full text Add to dashboard Cite

In advanced computing the Wireless Sensor Networks becomes the need of hour. The resources which are used in Wireless sensor Networks are limited in numbers. Resources are required to be allocated wisely to perform the numerous tasks in which job scheduling is always considered to be a key feature. Wireless sensor network has many sensor nodes as which are considered to be main components. Sensor node has limited energy and storage capabilities. So energy consumption in this field during scheduling is a biggest issue. This issue is carried out by many researchers and legion of algorithms are devised for achieving energy efficiency during scheduling of resources in wireless sensor networks. In this paper we have focused both the moving and stationery nodes for our study. Moving nodes are considered to be more prone to energy loss as compared to static nodes. This paper aims to study various techniques used to perform scheduling among such nodes to minimize energy consumption.

show abstract

Task Scheduling in Parallel Systems using Genetic Algorithm

Cited by 5 publications

References 12 publications

A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey

A Three-Level Parallelisation Scheme and Application to the Nelder-Mead Algorithm

A Review of Various Scheduling Techniques Considering Energy Efficiency in WSN

Contact Info

Product

Resources

About