Ant system for service deployment in private and public clouds

Csorba, Máté J.; Meling, Hein; Heegaard, Poul E.

doi:10.1145/1809018.1809024

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…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%

“…The optimization objective is to improve energy savings and load balancing in large cloud data centers. For such PaaS service placement scheduling for deploying the VMs, Csorba et al [2010] proposed an ACO approach for deploying the VM images to the server clusters that reside in different parts of the network. The objective is to balance the load of private and public clouds.…”

Section: Scheduling For Service Placementmentioning

confidence: 99%

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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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Section: Scheduling For Service Placementmentioning

confidence: 99%

Section: Scheduling For Service Placementmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

View full text Add to dashboard Cite

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“…Csorba et al propose a distributed algorithm to deploy replicas of VM images onto PMs that reside in different parts of the network [32]. The objective is to construct balanced and dependable deployment configurations that are resilient.…”

Section: Level 3: Service Provisionmentioning

confidence: 99%

“…Aforementioned SVNE approaches [30][31][32][33][34] lack an availability model. When the infrastructure is homogeneous, it might suffice to say that each VN or VNE need a predefined number of replicas.…”

Section: Level 3: Service Provisionmentioning

confidence: 99%

Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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Abstract. The chapter summarizes activities of COST IC1304 ACROSS European Project corresponding to traffic management for Cloud Federation (CF). In particular, we provide a survey of CF architectures and standardization activities. We present comprehensive multilevel model for traffic management in CF that consists of five levels: Level 5 -Strategies for building CF, Level 4 -Network for CF, Level 3 -Service specification and provision, Level 2 -Service composition and orchestration, and Level 1 -Task service in cloud resources. For each level we propose specific methods and algorithms. The effectiveness of these solutions were verified by simulation and analytical methods. Finally, we also describe specialized simulator for testing CF solution in IoT environment.

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“…The results of the proposed approach provide cost savings which are three times better compared with "Best Resource Selection". Meanwhile, Csorba et al [26] have proposed a Colony Optimization (CO) approach for deployment of virtual machines (VMs) images onto physical machines. The proposed approach improves the scalability of systems.…”

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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Ant system for service deployment in private and public clouds

Cited by 25 publications

References 26 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Traffic Management for Cloud Federation

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

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