Small-World Optimization Applied to Job Scheduling on Grid Environments from a Multi-Objective Perspective

2012 ◽  
pp. 42-51 ◽  
Author(s):  
María Arsuaga-Ríos ◽  
Francisco Prieto-Castrillo ◽  
Miguel A. Vega-Rodríguez
Keyword(s):  
Job Scheduling ◽  
Small World ◽  
Multi Objective ◽  
Grid Environments

Multi-Objective Artificial Bee Colony for scheduling in Grid environments

2011 ◽  
Author(s):  
Maria Arsuaga-Rios ◽  
Miguel A. Vega-Rodriguez ◽  
Francisco Prieto-Castrillo
Keyword(s):  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bee Colony ◽  
Grid Environments

Resource Provisioning in the Cloud

2014 ◽  
pp. 589-612
Author(s):  
Ming Mao ◽  
Marty Humphrey
Keyword(s):  
Job Scheduling ◽  
Data Transfer ◽  
Resource Provisioning ◽  
Research Trends ◽  
Future Research ◽  
Application Performance ◽  
Grid Environments ◽  
Solution Methods ◽  
The One ◽  
Cloud Users

It is a challenge to provision and allocate resources in the Cloud so as to meet both the performance and cost goals of Cloud users. For a Cloud consumer, the ability to acquire and release resources dynamically and trivially in the Cloud, while being a powerful and useful aspect, complicates the resource provisioning and allocation task in the Cloud. While on the one hand, resource under-provisioning may hurt application performance and deteriorate service quality; on the other hand, resource over-provisioning could cost users more and offset Cloud advantages. Although resource management and job scheduling have been studied extensively in the Grid environments and the Cloud shares many common features with the Grid, the mapping from user objectives to resource provisioning and allocation in the Cloud has many challenges due to the seemingly unlimited resource pools, virtualization, and isolation features provided by the Cloud. This chapter focuses on surveying the research trends in resource provisioning in the Cloud based on several factors such as the type of the workload, the VM heterogeneity, data transfer requirements, solution methods, and optimization goals and constraints, and attempts to provide guidelines for future research.

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

scholarly journals A Parallel Multi-Objective Cooperative Coevolutionary Algorithm for Optimising Small-World Properties in VANETs

2014 ◽  
Vol 17 (1) ◽  
Author(s):  
Grégoire Danoy ◽  
Julien Schleich ◽  
Pascal Bouvry ◽  
Bernabé Dorronsoro
Keyword(s):  
Evolutionary Algorithms ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Small World ◽  
City Centre ◽  
Nsga Ii ◽  
Solution Vector ◽  
Multi Objective ◽  
Hoc Networks ◽  
Fully Connected

Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms’ architecture in that the population is split into subpopulations, each of them optimising only a sub-vector of the global solution vector. All subpopulations cooperate by broadcasting their local partial solutions such that each subpopulation can evalu- ate complete solutions. Cooperative coevolution has recently been used in evolutionary multi-objective optimisation, but few works have exploited its parallelisation capabil- ities or tackled real-world problems. This article proposes to apply for the first time a state-of-the-art parallel asynchronous cooperative coevolutionary variant of the non- dominated sorting genetic algorithm II (NSGA-II), named CCNSGA-II, on the injection network problem in vehicular ad hoc networks (VANETs). This multi-objective optimi- sation problem, consists in finding the minimal set of nodes with backend connectivity, referred to as injection points, to constitute a fully connected overlay that will optimise the small-world properties of the resulting network. Recently, the well-known NSGA- II algorithm was used to tackle this problem on realistic instances in the city-centre of Luxembourg. In this work we analyse the performance of the CCNSGA-II when using different numbers of subpopulations, and compare them to the original NSGA-II in terms of both quality of the obtained Pareto front approximations and execution time speedup.

Multi-objective evolutionary algorithm for grid job scheduling based on adaptive neighborhood

2013 ◽  
Vol 32 (3) ◽  
pp. 599-602
Author(s):  
Ming YANG ◽  
Sheng-jun XUE ◽  
Liang CHEN ◽  
Yong-sheng LIU
Keyword(s):  
Evolutionary Algorithm ◽  
Job Scheduling ◽  
Multi Objective ◽  
Adaptive Neighborhood ◽  
Grid Job Scheduling

Resource Provisioning in the Cloud

2016 ◽  
pp. 2159-2181
Author(s):  
Ming Mao ◽  
Marty Humphrey
Keyword(s):  
Job Scheduling ◽  
Data Transfer ◽  
Resource Provisioning ◽  
Research Trends ◽  
Future Research ◽  
Application Performance ◽  
Grid Environments ◽  
Solution Methods ◽  
The One ◽  
Cloud Users

It is a challenge to provision and allocate resources in the Cloud so as to meet both the performance and cost goals of Cloud users. For a Cloud consumer, the ability to acquire and release resources dynamically and trivially in the Cloud, while being a powerful and useful aspect, complicates the resource provisioning and allocation task in the Cloud. While on the one hand, resource under-provisioning may hurt application performance and deteriorate service quality; on the other hand, resource over-provisioning could cost users more and offset Cloud advantages. Although resource management and job scheduling have been studied extensively in the Grid environments and the Cloud shares many common features with the Grid, the mapping from user objectives to resource provisioning and allocation in the Cloud has many challenges due to the seemingly unlimited resource pools, virtualization, and isolation features provided by the Cloud. This chapter focuses on surveying the research trends in resource provisioning in the Cloud based on several factors such as the type of the workload, the VM heterogeneity, data transfer requirements, solution methods, and optimization goals and constraints, and attempts to provide guidelines for future research.

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