A DHT-Based Process Migration Policy for Mobile Clusters

Begum, Y. Mohamadi; Mohamed, M. A. Maluk

doi:10.1109/itng.2010.157

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…In Satyanarayanan (2001), the author acquainted the term cyber foraging with increase the utilising so as to process possibilities of remote mobile devices accessible stationary computers in the neighbourhood environment. Prior, application offloading was utilised for pervasive computing (Li and Li, 2010), grid computing (Begum and Mohamed, 2010) and cluster computing (Chun et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Energy saving offloading scheme for mobile cloud computing using CloudSim

Thanapal¹,

Durai²

2018

IJAIP

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In this paper, we accentuate on offloading for sparing energy in mobile cloud computing (MCC). MCC is emerging as a noticeable research area that is looking to bring the massive advantages of the cloud to the constrained mobile devices. The impediments of poor processing capacity and constrained battery life make it troublesome for mobile devices to process complex calculation undertakings. The energy system concentrates on mobile devices battery energy that utilises computational offloading for computationally serious mobile applications on the mobile devices. Each time an application in the physical mobile devices is initialised, the power utilisation is ascertained by the structure and it decides whether to make on offloading choice or not. This paper proposes a novel energy saving computational offloading framework for the preparing of serious mobile applications in MCC. It is found that energy utilisation of the selected application reduces up to 80.94% and execution time reduces up to 97.16% by computational offloading utilising CloudSim when contrasted with the conventional methods. It enhances the quality of services for mobiles and helps in keeping up constant reactions for mobile applications.Reference to this paper should be made as follows: Thanapal, P. and Durai, M.A.S. (2018) 'Energy saving offloading scheme for mobile cloud computing using CloudSim', Int.

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Section: Related Workmentioning

confidence: 99%

Energy saving offloading scheme for mobile cloud computing using CloudSim

Thanapal¹,

Durai²

2018

IJAIP

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“…The mechanism of outsourcing computational load to remote surrogates in the close proximity is called cyber foraging [30]. Researchers [31], Grid Computing [32] and Cluster Computing [33]. In recent years, a number of cloud server based application offloading frameworks are introduced for outsourcing computational intensive components of the mobile applications partially or entirely to cloud datacenters.…”

Section: Augmenting Smartphones Through Computational Cloudsmentioning

confidence: 99%

“…The attribute of DS shows the requirement of additional support required for the development of the application. Therefore, the traditional application offloading models which require developer support [6], [35], [14], [30], [40] are represented as required, whereas the frameworks which do not require developers support [7], [13], [30], [33], [36], [38]- [43], [49], [40] are represented as Not required. The attribute of execution management shows the management policy for the deployment and management of runtime distributed application platform.…”

Section: Comparison Of Application Offloading Framework By Usingmentioning

confidence: 99%

A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing

Shiraz

Gani

Khokhar

et al. 2013

IEEE Commun. Surv. Tutorials

264

120

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Abstract-The latest developments in mobile devices technology have made smartphones as the future computing and service access devices. Users expect to run computational intensive applications on Smart Mobile Devices (SMDs) in the same way as powerful stationary computers. However in spite of all the advancements in recent years, SMDs are still low potential computing devices, which are constrained by CPU potentials, memory capacity and battery life time. Mobile Cloud Computing (MCC) is the latest practical solution for alleviating this incapacitation by extending the services and resources of computational clouds to SMDs on demand basis. In MCC, application offloading is ascertained as a software level solution for augmenting application processing capabilities of SMDs. The current offloading algorithms offload computational intensive applications to remote servers by employing different cloud models. A challenging aspect of such algorithms is the establishment of distributed application processing platform at runtime which requires additional computing resources on SMDs. This paper reviews existing Distributed Application Processing Frameworks (DAPFs) for SMDs in MCC domain. The objective is to highlight issues and challenges to existing DAPFs in developing, implementing, and executing computational intensive mobile applications within MCC domain. It proposes thematic taxonomy of current DAPFs, reviews current offloading frameworks by using thematic taxonomy and analyzes the implications and critical aspects of current offloading frameworks. Further, it investigates commonalities and deviations in such frameworks on the basis significant parameters such as offloading scope, migration granularity, partitioning approach, and migration pattern. Finally, we put forward open research issues in distributed application processing for MCC that remain to be addressed.

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“…WLAN) [92], [95], [43]; on the other hand, these can also be located at Internet/WAN latency using broadband wireless technologies (4G, 5G, LTE, etc.). Most of the existing works on resource augmentation use static service nodes [95], [96], [43], [71], in a designated area, while some use mobile service nodes [84], [52], [26]. The mobility characteristic of service nodes poses some challenges.…”

Section: Resource Augmentationmentioning

confidence: 99%

Scalable Resource Augmentation for Mobile Devices

Nir¹

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This thesis focuses on scalability of a resource augmentation environment when a large number of mobile devices and multiple service nodes are present. To deal with congestion, a scanning method was proposed to get information on users' density in an area such that the service nodes and access points could be placed at strategic points. To lower communication overhead, a centralized broker-node architecture was proposed, which manages resource monitoring on behalf of all mobile devices. In the centralized architecture, mathematical models for the task scheduling problem in the local resources case and the mobile cloud computing case were proposed to optimally minimize the total energy consumption across all mobile devices. A generalized model for the task scheduling problem was proposed. The model optimally minimized the total energy and monetary cost when evaluated in two environments for mobile cloud computing, one using a local private cloud and the other using public clouds. The models found optimal solutions for the centralized task scheduling problems, and an improvement in the total costs was observed when offloading with optimization compared to when offloading without optimization using the centralized task scheduler.

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A DHT-Based Process Migration Policy for Mobile Clusters

Cited by 9 publications

References 22 publications

Energy saving offloading scheme for mobile cloud computing using CloudSim

Energy saving offloading scheme for mobile cloud computing using CloudSim

A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing

Scalable Resource Augmentation for Mobile Devices

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