Performance improvement of cloud storage using a genetic algorithm based placement

Jindarak, Kanatorn; Uthayopas, Putchong

doi:10.1109/jcsse.2011.5930093

Cited by 7 publications

(5 citation statements)

References 6 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 objective is to balance the load of private and public clouds. Differently, Jindarak and Uthayopas [2011], and Guo and Wang [2013] proposed the GA-based approach to optimally schedule data placements in the cloud storage, which can be regarded as IaaS service placement scheduling. The optimization objective in Jindarak and Uthayopas [2011] is to improve the average data access time for users and the workload balance of the cloud systems, while the objective in Guo and Wang [2013] is to reduce the distributed cooperation costs.…”

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

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“…In paper [10,11] and [13] by considering Elliptic Curve Cryptography approach for security in data forwarding, secure user authentication and private data storage in cloud computing which provides data integrity, authentication, data confidentiality and moderate scalability factors. In paper [12] considering genetic algorithm based performance improvement of cloud storage which gives better average access speed for any storage object. In paper [14] user profiling system for cloud environment using Artificial Intelligence techniques and studies behavior of User Profiling System and proposes a new hybrid approach.…”

Section: B Elliptic Curve Encryption/decryptionmentioning

confidence: 99%

Key management infrastructure in cloud computing environment-a survey

R¹,

Sathyanarayana²

2017

TIS

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“…To solve the compute intensive optimization problems without hiring the computing infrastructure, cloud is good option. Some of optimization problems which are solved using PGAs are Resource Scheduling [46], Scheduling HPC Applications [47], Task Scheduling [48], [49], [50], Performance Improvement of Cloud Storage [51], Power Management in Cloud [52], Clustering composite SaaS components [53] etc.…”

Section: Ga Over Cloudmentioning

confidence: 99%

Untitled

2013

IJSC

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Genetic Algorithm (GA), a stochastic optimization technique, doesn't ensure optimal solution every time. Nowadays there is a need to improve the performance of each and every application so that the time required for obtaining quality solution can be minimized. This paper gives a brief overview of theoretical advances and computing trends, particularly population diversity in PGA (Parallel GA) and provides information about how various authors, researchers, scientists have parallelized GA over various parallel computing paradigms viz. Cluster, MPP (Massively Parallel Processing), GPGPU (General purpose Graphics Processing Units), Grid, Cloud, Multicore/HPC to ensure more optimal solution every time with efficacy and efficiency.

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Performance improvement of cloud storage using a genetic algorithm based placement

Cited by 7 publications

References 6 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Key management infrastructure in cloud computing environment-a survey

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