Analyzing computation offloading energy-efficiency measurements

Fekete, Krisztián; Csorba, Kristóf; Forstner, Bertalan; Vajk, Tamás; Fehér, Marcell; Albert, István

doi:10.1109/iccw.2013.6649248

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…A method of only offloading tasks when the energy consumption via WIFI transfer is efficient, is presented in 12 . Whilst another model for determining whether offloading this computation from mobile devices to cloud are presented in 13 . Sometimes, code is divided up with some executed locally and others executed remotely.…”

Section: Energy Consumption and MCCmentioning

confidence: 99%

User Profiling for Energy Optimisation in Mobile Cloud Computing

Benkhelifa¹,

Welsh²,

Tawalbeh³

et al. 2015

Procedia Computer Science

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Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality.Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. This paper reviews current work in energy consumption of mobile cloud computing and then proposes a system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with an external cloud for optimum energy consumption. Such a system is particularly useful for cloudlets which contain constrained resources so may need to choose between a number of clients. Whilst mobile computing enables a variety of feature rich functionality for users in a non-fixed location, cloud computing is revolutionising the way in which computing resources are being provisioned, used and optimised for both service providers and end users. These two fields are being combined in order to provide greater functionality for mobile devices in a number of different ways. Augmentation of mobile resources from the cloud has been shown as one way in which the energy consumption and power of mobile devices may be considerably enhanced. However, due to the resource constrained nature of the devices, in particular their power source and communication interfaces, there is often a fine line where offloading of these resources is economical.

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Section: Energy Consumption and MCCmentioning

confidence: 99%

User Profiling for Energy Optimisation in Mobile Cloud Computing

Benkhelifa¹,

Welsh²,

Tawalbeh³

et al. 2015

Procedia Computer Science

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show abstract

“…A method of only offloading tasks, when the energy consumption via WIFI transfer is efficient, is presented in Zhang et al, 24 whilst another model for determining whether offloading, this computation from mobile devices to cloud are presented in Fekete et al 25 Sometimes, code is divided up with some executed locally and others executed remotely. A method of only offloading tasks, when the energy consumption via WIFI transfer is efficient, is presented in Zhang et al, 24 whilst another model for determining whether offloading, this computation from mobile devices to cloud are presented in Fekete et al 25 Sometimes, code is divided up with some executed locally and others executed remotely.…”

Section: Energy Consumption and Social Mobile Cloudmentioning

confidence: 99%

Automated negotiated user profiling across distributed social mobile clouds for resource optimisation

Benkhelifa

Welsh

Tawalbeh

et al. 2018

Concurrency and Computation

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With mobile computing being the number one user paradigm of choice, and cloud computing becoming the chosen supporting infrastructure, the aggregation of these two areas is inevitable.Yet due to their novelty, they suffer from both inherited and new issues. Social computing theory can apply well to this fusion, such as pooling together in a social model to create ad hoc mobile clouds or by providing a greater functionality for mobile devices through resource augmentation from an external cloud. However, due to the highly contested resources within this setting, these resources must be negotiated within a social context. This paper argues that the application of social models to mobile cloud computing can allow mobile devices to employ cooperative strategies for resource sharing to allow aspects such as energy and costs to be minimised. Social computing is the means of using computing resources to augment human intelligence, mobiles can provide these enhancements to social intelligences but in a peer-to-peer manner. This paper proposes the design of a novel system which employs aggregated user and application resource profiling, in order to determine the most optimal place to process data, locally or on a remote cloud. Negotiation with the local cloud will then find a balance between optimum energy and resource utilisation.

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“…However, battery life and energy consumption are still the main limiting factors. Work in [7] has characterized the energy profile of the different network interfaces based on data transfer patterns. This must be taken into consideration when designing a mobile cloud system and data traffic must in general be reduced as much as possible when nodes are operating on batteries or over a paid cellular connection.…”

Section: Related Workmentioning

confidence: 99%

On the feasibility of a network coded mobile storage cloud

Sipos

Fitzek

Lucani

2015

2015 IEEE International Conference on Communications (ICC)

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Conventional cloud storage services offer relatively good reliability and performance in a cost-effective manner.However, they are typically structured in a centralized and highly controlled fashion. In more dynamic storage scenarios, these centralized approaches are unfeasible and developing de centralized storage approaches becomes critical. The novelty of this paper is the introduction of the highly dynamic distributed mobile cloud, which uses free resources on user devices to move storage to the edges of the network. At the core of our approach, lies the use of random linear network coding to provide an effective and flexible erasure correcting code. This paper identifies and answers key questions regarding the feasibility of such a system. We show that the mobile cloud has sufficient network resources to adapt to changes in node numbers and also study the redundancy level needed to maintain data availability. We have found that as little as 75% redundancy is enough to offer 99.28% availability for the examined period and essentially 100% availability is achieved when using 50% redundancy along with high-availability nodes. We have leveraged traces from a popular P2P mobile application to simulate the processes governing user behavior to show feasibility of mobile storage clouds in real scenarios.

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Analyzing computation offloading energy-efficiency measurements

Cited by 9 publications

References 7 publications

User Profiling for Energy Optimisation in Mobile Cloud Computing

User Profiling for Energy Optimisation in Mobile Cloud Computing

Automated negotiated user profiling across distributed social mobile clouds for resource optimisation

On the feasibility of a network coded mobile storage cloud

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