Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

Kumar, Karthik; Lu, Yung-Hsiang

doi:10.1109/mc.2010.98

Cited by 1,365 publications

(754 citation statements)

References 5 publications

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“…This mainly due to the fact that when the MD has a few data to be ofoaded, two schemes cannot make too much di erence on the revenue performance. 7 …”

Section: Simulation Resultsmentioning

confidence: 99%

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Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

et al. 2016

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Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can o oad those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data o oading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to o oad sets the price strategies for di erent SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the e ectiveness of the proposed scheme.

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“…This mainly due to the fact that when the MD has a few data to be ofoaded, two schemes cannot make too much di erence on the revenue performance. 7 …”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this way, the problem of storage limitation can be solved as well [6]. Typically there are three types of mobile cloud architectures, namely the traditional centralized cloud [7], the recently emerged cloudlet [8] and the peer-based ad hoc mobile cloud [9].…”

Section: Introductionmentioning

confidence: 99%

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

et al. 2016

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“…Therefore, many developed applications rely on a remote services provider, which can be the same actor providing the application or a different one in charge of providing support (e.g., online leaderboard services for games). Cloud computing has become a fundamental factor in the success of many applications, as it extends the possibilities of these applications, while at the same time reducing computing costs in centralized servers [8].…”

Section: The Mobile Application Store Value Networkmentioning

confidence: 99%

Mobile application stores: success factors, existing approaches, and future developments

Cuadrado

Dueñas

2012

IEEE Commun. Mag.

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The mobile user experience has been significantly altered with the arrival of mobile broadband widespread deployments, massive improvements in available smartphones, and a shift in user habits toward a more participative, communicative role. In this context, mobile application stores have revolutionized software and content delivery. These stores focus on the applications, building around them an ecosystem of developers and consumers. The store greatly lessens the barrier between these agents, providing significant benefits to both developers and consumers. In this article we analyze this phenomenon, describing its originating factors and fundamental characteristics. We also perform a more detailed study on the two most successful application stores, identifying different approaches to implementing the model.

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“…The key novelty in EMCO is the use of crowdsensed evidence traces to characterize the influence of different contextual parameters and other factors on offloading decisions [14]. Contrary to existing solutions, which either rely on static code profiling performed on individual devices [2], [3], [4], [5], [6], [11], [15], [16], [17] or on parametrized models that consider a handful of parameters such as network latency, remaining energy, and CPU speed [9], [18], the use of crowdsensing enables EMCO to quantify and characterize the effect of a wide range of parameters and how they vary over execution contexts. EMCO models the context where offloading decisions are made is through simple dimensions that are easy to scale, and determines optimal dimensions using an analytic process that characterizes the performance of offloading based on contexts captured by the community.…”

Section: Introductionmentioning

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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Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

Cited by 1,365 publications

References 5 publications

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Mobile application stores: success factors, existing approaches, and future developments

Evidence-Aware Mobile Computational Offloading

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