Composite SaaS Placement and Resource Optimization in Cloud Computing Using Evolutionary Algorithms

Yusoh, Zeratul Izzah Mohd; Tang, Maolin

doi:10.1109/cloud.2012.61

Cited by 38 publications

(39 citation statements)

References 15 publications

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“…Yusoh and Maolin [27] have presented two approaches. The first approach is a Cooperative Coevolution Genetic Algorithm (CCGA) for initial placement of SaaS problems.…”

Section: Evolutionary Cloud Migration Optimization (Ecmo) Approachesmentioning

confidence: 99%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. Cloud computing has become more attractive for consumers to migrate their applications to the cloud environment. However, because of huge cloud environments, application customers and providers face the problem of how to assess and make decisions to choose appropriate service providers for migrating their applications to the cloud. Many approaches have investigated how to address this problem. In this paper we classify these approaches into non-evolutionary cloud migration optimization approaches and evolutionary cloud migration optimization approaches. Criteria including cost, QoS, elasticity and degree of migration optimization have been used to compare the approaches. Analysis of the results of comparative evaluations shows that a Multi-Objectives optimization approach provides a better solution to support decision making to migrate an application to the cloud environment based on the significant proposed criteria. The classification of the investigated approaches will help practitioners and researchers to deliver and build solid approaches.

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“…Yusoh and Maolin [27] have presented two approaches. The first approach is a Cooperative Coevolution Genetic Algorithm (CCGA) for initial placement of SaaS problems.…”

Section: Evolutionary Cloud Migration Optimization (Ecmo) Approachesmentioning

confidence: 99%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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“…In order to accelerate the evolutionary speed, Tang and Yusoh [2012] further enhanced the GA approach with a parallel cooperative coevolutionary strategy, while Yuan and Wu [2012] proposed using an adaptive simulated annealing GA to schedule the globally distributed data center resources for the SaaS placement. Yusoh and Tang [2012a] further reported that the dynamic characteristics of the cloud computing environment required dynamic resource scheduling for the SaaS service placement. They proposed a grouping GA approach to cater to the structural group of a composite SaaS and to reconfigure the placement of the SaaS's components.…”

Section: Scheduling For Service Placementmentioning

confidence: 99%

“…Their study argued that as Amazon has its storage server resources in America and Europe, while Nirvanix deploys its storage GA for SaaS placement [Yusoh and Tang 2010a]: optimally place the SaaS software/data components to different data centers Enhance GA by parallelism [Yusoh and Tang 2010b;Tang and Yusoh 2012], simulated annealing [Yuan and Wu 2012], and cooperative coevolutionary strategy [Yusoh and Tang 2012a;Yusoh and Tang 2012b] GA for PaaS placement to save energy [Agostinho et al 2011] ACO to place PaaS for load balance [Csorba et al 2010] GA for IaaS (storage) placement [Jindarak and Uthayopas 2011;Guo and Wang 2013] Scheduling for partner federation (How to federate cloud providers? )…”

Section: Scheduling For Service Placementmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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“…Researchers in [5] and [6] aim to keep cloud architecture costs to a minimum by implementing a multitenant SaaS Model. Other authors [7] concentrated on bettering execution times for SaaS providers whilst reducing resource consumption using evolutionary algorithms opposed to traditional heuristics. A heuristic is defined in [8] for the capacity planning purposes for the SaaS inspired by a utility model.…”

Section: Related Work On Optimal Deployment and Allocation Of Cloud Rmentioning

confidence: 99%

“…The values of the MMKP the instance, were produced as follows: (i) random generation of isolation values in the interval [1][2][3]; (ii) values of component consumption of CPU, RAM, disk and bandwidth (i.e., the weights) were generated in the interval [1][2][3][4][5][6][7][8][9]; (iii) individual component resource limits (i.e., knapsack capacities for CPU, RAM, disk and bandwidth) were created by halving the maximum resource consumption possible (see Equation 7). …”

Section: Instance Generationmentioning

confidence: 99%

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

Ochei¹,

Ejiofor²

2018

Adv. sci. technol. eng. syst. j.

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Composite SaaS Placement and Resource Optimization in Cloud Computing Using Evolutionary Algorithms

Cited by 38 publications

References 15 publications

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

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