Security Driven Scheduling Model for Computational Grid Using NSGA-II

Kashyap, Rekha; Vidyarthi, Deo Prakash

doi:10.1007/s10723-013-9251-x

Cited by 19 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the comparative study reveals that the proposed model has an edge over other contemporary models viz. Min-Min, Max-Min, HEFT and EAMM for energy optimization [17,23,24,25,33].…”

Section: Related Workmentioning

confidence: 99%

“…Sometime, there is a need to consider more than one parameter while scheduling a job on grid. It is possible to work on multi-objective [24] optimization and to handle more than one QoS parameter simultaneously to solve the grid scheduling problem. Also there are many other appealing meta-heuristic techniques that can be explored with various QoS parameters and will be taken as future work.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

See 1 more Smart Citation

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

PrasadSahu¹,

Singh²,

Prakash³

2015

IJCA

View full text Add to dashboard Cite

Computational Grid (CG) is a wide network of computational resources that provides a distributed platform for high end compute intensive applications. The resources in the computational grid are usually heterogeneous and being a highly heterogeneous system, Computational Grid poses a number of constraints. It is difficult to allocate and schedule the applications properly to achieve the benefit of the grid resources from the applications point of view, as the resources are heterogeneous and dynamic in nature. There are no common scheduling strategies that fulfill all the needs with respect to both, user and the system. The available scheduling implementations consider specific characteristics of the available resources and the application. The complexity of application, user requirements and system heterogeneity prevents any scheduling procedure in achieving its best performance. The aim of a grid scheduling algorithm is to find an appropriate set of resources and maintain its userdemanded Quality of Service (QoS) requirements. Scheduling in CG is an NP-hard problem which requires an efficient solution. The problem, considered in this work, is task scheduling in Computational Grid (CG). Task scheduling in CG is a complex problem as many QoS parameters and system constraints are involved. This paper deliberates over the problem and various tools used in order to solve this problem.

show abstract

“…Also, the comparative study reveals that the proposed model has an edge over other contemporary models viz. Min-Min, Max-Min, HEFT and EAMM for energy optimization [17,23,24,25,33].…”

Section: Related Workmentioning

confidence: 99%

Section: Conclusion and Future Scopementioning

confidence: 99%

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

PrasadSahu¹,

Singh²,

Prakash³

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…It is introduced in 2002 to overcome some shortcomings of NSGA [4]. Since then, it has proved its efficiency in many MO branches [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram

Moharam¹,

Elhosseini²,

Ali³

2015

SIC

View full text Add to dashboard Cite

This paper introduces a design for multi-objective PID controller using non-dominated sorting genetic algorithm (NSGA-II). When selecting the objectives to be optimized, it is taken into account to cover some important characteristics of the system like performance, robustness and control signals' smoothness. The decision making is done using Level diagram tool. Three tanks liquid level system control is discussed as a case study. The results show that this tool improves the process of decision making (DM). Also, comparisons with Ziegler and Nichols (Z-N) and different optimization methods are presented.

show abstract

“…In general, it is unusual to assume a single criterion for the decision making in a grid system which is governed by various related factors. The scheduling problem in the grid system may have several conflicting objectives, e.g., makespan, reliability, energy consumption, cost, security, etc., which need to be optimized simultaneously [1][2][3][4].The outcome values of different objectives, viz. execution time of any job, the energy consumption by the job and the reliability offered by the system to the job may vary from one allocation to another [5].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

Self Cite

View full text Add to dashboard Cite

parameters by effectively managing the available grid resources. In general, it is unusual to assume a single criterion for the decision making in a grid system which is governed by various related factors. The scheduling problem in the grid system may have several conflicting objectives, e.g., makespan, reliability, energy consumption, cost, security, etc., which need to be optimized simultaneously [1][2][3][4].The outcome values of different objectives, viz. execution time of any job, the energy consumption by the job and the reliability offered by the system to the job may vary from one allocation to another [5]. The distributed nature of grid resources and the dynamic nature of the grid workload make the makespan of the job unpredictable. For makespan minimization, it is warranted to effectively deal with the load balancing as well [6]. Further, the heterogeneous hardware components of the grid are more prone to failure and therefore reliability of such systems decreases with the increase in the number of geographically distributed resources [7]. The heterogeneity of the system demands varying energy consumption based on their hardware profile [8]. It necessitates choosing those nodes/machines that are able to execute the job quickly, reliably and with least energy consumption.Since these objectives are conflicting in nature, any approach adopted to optimize one objective may not result in the overall optimized solution with respect to other objectives. All the conflicting objectives need to be addressed simultaneously and the best way for this is to have the trade-off solutions with all the objectives [9, 10]. A good solution corresponding to all the objectives is desired. This solution may not offer the best of all the objectives, but certainly better when seen as a trade-off among the objectives. This approach is referred as multiobjective optimization technique [5]. As such, job scheduling in the grid is an NP-hard problem and traditional Abstract Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Nondominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multiobjective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.

show abstract

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Cited by 19 publications

References 29 publications

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram

An energy-efficient reliable grid scheduling model using NSGA-II

Contact Info

Product

Resources

About