Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

Shiraz, Muhammad; Gani, Abdullah; Shamim, Azra; Khan, Suleman; Ahmad, Raja Wasim

doi:10.1007/s10723-014-9323-6

Cited by 82 publications

(40 citation statements)

References 27 publications

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“…These framework focussing on leveraging application processing services of cloud datacentres with negligible occurrences of computationally concentrated component relocation at runtime. Therefore, the energy consumption cost and the size of data transmission is reduced in computational offloading for mobile cloud computing [6]. EECOF framework gives the results in offloading different components of the prototype application over the wireless network medium as follows, the size of data transmission is reduced by 84 % and energy consumption cost is reduced by 69.9 %.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Analysis of Novel Approaches for Energy Efficient Computational Offloading Performances in Mobile Cloud Computing

Monisha¹

2017

IJRASET

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Our interaction with the mobile devices are increasing enormously nowadays because of advantages in mobile application. This massive applications is satisfied with the help of integration between cloud and mobile computing that is called as mobile cloud computing. Another side mobile user's expectation are always progressively grownup. Due to this expectations ; In mobile devices Storage Capacity, Network Bandwidth and Processor Performances, are always upgraded day by day, therefore the smart phones and android mobiles they want better battery life or concentrating how much power consumed in latest mobile devices that is a major problem. Because of the additional applications runs in mobile device they may want to charge the mobile devices at least one time per day and also migrate their data into cloud servers. Therefore optimizing the power consumption in mobile devices that the data are migrated to the cloud. In cloud environment the user get a resources by a Cloud Service Providers. The energy consumption is reduced in mobile cloud computing by using computational offloading techniques. As a result our research paper mainly focussing on reduce the power consumption in mobile devices using different approaches of computational offloading in Mobile Cloud Computing.

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Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Analysis of Novel Approaches for Energy Efficient Computational Offloading Performances in Mobile Cloud Computing

Monisha¹

2017

IJRASET

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“…This could be possible if low-cost servers are available or the tester has sufficient computational resources. One feasible solution is to employ Cloud Computing (Shiraz et al, 2015) for achieving low-cost computational resources without compromising the quality of the obtained optimized solution.…”

Section: Multiple Server-based Tsr Optimizationmentioning

confidence: 99%

A survey on Test Suite Reduction frameworks and tools

Khan

Lee

Ahmad

et al. 2016

International Journal of Information Management

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Abstract-Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks.

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“…- [30] In this paper, author has proposed an offloading framework, that minimises the number of computation-intensive components, migrated to cloud at runtime. Thus it reduces the overhead induced by the migration of components.…”

Section: Cloud Based Techniquesmentioning

confidence: 99%

A Comparative Study of Code Offloading Techniques and Application Partitioning Methods in Mobile Cloud Computing

Kaur¹,

Kaur²

2016

IJCA

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Mobile cloud computing allows the execution of computation-intensive applications of mobile devices in computational clouds, and this process of executing in cloud by sending the application VM/Components is called application/code/component offloading. Offloading is an effective method to save the execution time and energy consumption of mobile devices. Thus it extends the battery life of mobile devices. Applications are first partitioned into offloadable and non-offloadable components, which are then transferred to remote server for execution. The objective of this paper is to explore the different techniques of offloading and application partitioning methods. These techniques are thoroughly reviewed in this paper. This paper also highlights the comparison of different techniques on the basis of their contribution, merits, demerits and also on the basis of improvement in execution time, energy consumption, communication time.

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Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

Cited by 82 publications

References 27 publications

Analysis of Novel Approaches for Energy Efficient Computational Offloading Performances in Mobile Cloud Computing

Analysis of Novel Approaches for Energy Efficient Computational Offloading Performances in Mobile Cloud Computing

A survey on Test Suite Reduction frameworks and tools

A Comparative Study of Code Offloading Techniques and Application Partitioning Methods in Mobile Cloud Computing

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