Advancing the state of mobile cloud computing

Bahl, Paramvir; Han, Richard; Li, Li Erran; Satyanarayanan, Mahadev

doi:10.1145/2307849.2307856

Cited by 154 publications

(72 citation statements)

References 16 publications

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“…Moreover, the scalability claimed by ThinkAir does not support multi-tenancy, but the system creates multiple virtual machines based on Android-x86 within the same server for parallelization. Other frameworks for computational offloading include [16], [22], [23], [24], [25]. COSMOS [6] provides offloading as a service at method level using Android-x86.…”

Section: Related Workmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

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Section: Related Workmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…Technological innovations are occurring at an accelerated rate [2] increasingly. Mobile devices are much more capable in terms of processing speed & storage.…”

Section: Fig 1 Cloud Computingmentioning

confidence: 99%

Implementing Multi Criteria Analysis of Advanced Mobile Cloud Computing: A Review

Kumar¹

2018

IJRASET

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“…Some of the security issues include data isolation, browser security, malware, flooding attack, privacy, data integrity, and protection of DATA [3]. …”

Section: A MCC Popularitymentioning

confidence: 99%