scholarly journals Task – Heterogeneous Resource Mapping Algorithm for Grid Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 10906-10909
Keyword(s):  
Scheduling Algorithm ◽  
Vital Role ◽  
Task Mapping ◽  
Grid Environment ◽  
Mapping Algorithm ◽  
Complete Problem ◽  
Heuristic Scheduling ◽  
Task Scheduling Algorithm ◽  
Np Complete

In grid, scheduling algorithms play vital role of mapping a set of tasks to the available heterogeneous resources. Extant literatures have shown that the task mapping problem is an NP-Complete problem. Heuristic scheduling algorithm aims to obtain the minimum overall execution time of the set of tasks. In this paper, we address the problem of scheduling a set of n tasks with a set of m resources, such that the makespan is minimized. The proposed task scheduling algorithm (RTS) is based on the well-known optimization algorithm, called Hungarian algorithm. (RTS) algorithm considers an equal number of tasks and resources, and maps the tasks to the resources and makes an effective scheduling decision. We simulate the (RTS) algorithm and compare it with the Min-min heuristic scheduling algorithm. The performance evaluation shows that (RTS) produces minimized makespan and better resource utilization in comparison to existing Min-min.

scholarly journals Limit Value Task Scheduling (LVTS): an Efficient Task Scheduling Algorithm for Distributed Computing Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 10457-10462
Keyword(s):  
Resource Utilization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing Environment ◽  
Complete Problem ◽  
Limit Value ◽  
Computational Environment ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Diverse Groups

The grid computational environment suits to meet the computational demands of large, diverse groups of tasks. Assigning tasks to heterogeneous wide spread resources seems complex and is termed as an NP-Complete problem. A new task scheduling algorithm, called Limit Value Task Scheduling Algorithm (LVTS) is presented to efficiently identify the appropriate resources, which is responsible for the scheduling process. The proposed algorithm (LVTS) schedules the tasks to the appropriate resources by calculating the limit value of the tasks and the ceil value of the tasks which represents the completion time of the last tasks scheduled in the resource with highest processing capacity. The efficiency of the (LVTS) measured based on makespan and resource utilization. Experimental results indicates LVTS algorithm sounds good than the Min-min on both makespan and resource utilization.

Towards Efficient Bounds on Completion Time and Resource Provisioning for Scheduling Workflows on Heterogeneous Processing Systems

2017 ◽  
Vol 9 (3) ◽  
pp. 60-82 ◽  
Author(s):  
D. Sirisha ◽  
G. Vijayakumari
Keyword(s):  
Completion Time ◽  
High Performance ◽  
Turnaround Time ◽  
Resource Provisioning ◽  
Optimal Solutions ◽  
Complete Problem ◽  
Heterogeneous Processing ◽  
Heuristic Scheduling ◽  
Np Complete ◽  
Workflow Tasks

Compute intensive applications featured as workflows necessitate Heterogeneous Processing Systems (HPS) for attaining high performance to minimize the turnaround time. Efficient scheduling of the workflow tasks is paramount to attain higher potentials of HPS and is a challenging NP-Complete problem. In the present work, Branch and Bound (BnB) strategy is applied to optimally schedule the workflow tasks. The proposed bounds are tighter, simpler and less complex than the existing bounds and the upper bound is closer to the exact solution. Moreover, the bounds on the resource provisioning are devised to execute the workflows in the minimum possible time and optimally utilize the resources. The performance of the proposed BnB strategy is evaluated on a suite of benchmark workflows. The experimental results reveal that the proposed BnB strategy improved the optimal solutions compared to the existing heuristic scheduling algorithms for more than 20 percent of the cases and generated better schedules over 7 percent for 82.6 percent of the cases.

Task Scheduling Algorithm in Grid Environment Based on Duplication and Insertion

2013 ◽  
Vol 8 (10) ◽  
Author(s):  
Lijun Cao ◽  
Xiyin Liu ◽  
Torkel Hans-Georg Hans-Georg ◽  
Zhongping Zhang
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Grid Environment ◽  
Task Scheduling Algorithm

scholarly journals Potential Finish Time and Min-mean Algorithm for allocating Meta-Tasks on distributed Computational Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 10580-10586
Keyword(s):  
Task Scheduling ◽  
Resource Sharing ◽  
Scheduling Algorithm ◽  
Average Completion Time ◽  
Finish Time ◽  
Task Scheduling Algorithm ◽  
Engineering Problems ◽  
Np Complete ◽  
Selection Of ◽  
Scientific Engineering

Grid is widely distributed and promising technology that enables the integrated and heterogeneous resource sharing for solving computationally challenging scientific engineering problems. In a distributed grid environment, allocating the tasks to the available computing resources proves complex and it is an NP-Complete problem as resources are geo-graphically distributed. In this paper presents a new task scheduling algorithm, called Potential Finish Time Min-mean Task Scheduling Algorithm (PFTSA), to enhance the selection of the suitable resources, which is responsible for the scheduling process. The proposed algorithm (PFTSA) schedules the tasks to the suitable resources by considering the potential finish time of the tasks, average execution time and waiting time of the tasks and average completion time of the resources. The proposed algorithm (PFTSA) results in minimum makespan as well as improved resource utilization. The experimental results indicate the PFTSA is a promising algorithm than the existing Min-min algorithm.

QoS-Constrained Resource Scheduling Base on Market Economy Models in Grid Computing

2013 ◽  
Vol 443 ◽  
pp. 599-602
Author(s):  
Lei Chen
Keyword(s):  
Market Economy ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Resource Scheduling ◽  
Data Access ◽  
Grid Environment ◽  
Task Scheduling Algorithm ◽  
User Tasks

The Grid task scheduling algorithm is proposed that takes the service quality of resource scheduling, time and cost together into consideration, so that it can better meet user tasks Quality of Service (QoS) requirements and make the complex grid environment open. On the basis of the price model drove by supply and demand in economy, we design the Grid task scheduling algorithm in the market economy model. The experiment results indicate the effectiveness of proposed algorithm in terms of usersQoS guarantee. It reduce data access latency and decrease bandwidth consumption.

scholarly journals Desktop Grid Environment for Evolution Strategies using Grid Task Scheduling Algorithm RR

2008 ◽  
Vol 21 (9) ◽  
pp. 300-309
Author(s):  
Yoshiyuki MATSUMURA ◽  
Noriyuki FUJIMOTO ◽  
Yoshikazu MURAYAMA ◽  
Masaki MATSUDA ◽  
Masashi OISO
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Evolution Strategies ◽  
Grid Environment ◽  
Desktop Grid ◽  
Task Scheduling Algorithm

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2019 ◽  
pp. 115-127
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2021 ◽  
pp. 1303-1316
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

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