Augmenting computing capabilities at the edge by jointly exploiting mobile devices: A survey

Hirsch, Matías; Mateos, Cristian; Zunino, Alejandro

doi:10.1016/j.future.2018.06.005

Cited by 39 publications

(34 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, Mobile Crowd Computing (MCrC) is a subset of MClC in which nearby SDs can act as the destination “server” when offloading jobs or data from a surrounding SD . As suggested in the work of Hirsch et al and shown in Figure , such a group of nearby SDs, playing the role of resources providers, would offer computing resources at the bottom level of the Cloud‐Edge computing hierarchy but within a limited geographical area. We call such group of nearby SDs, SCE, and the scavenging of computing resources pose interesting challenges in terms of resource management.…”

Section: Related Workmentioning

confidence: 99%

“…Despite interesting progress in the exploitation of the computing potential of such groups, more investigation is needed and even more efforts toward a unified tool are necessary to accelerate resource management research in area . Since SDs are multipurpose devices, such efforts need to contemplate novel techniques for modeling nonlinear battery depletion caused by multiresource usage and sharable nature of, eg, computing resources, with owner processes and applications.…”

Section: Related Workmentioning

confidence: 99%

“…Despite interesting recent progress in the area, experimentation with real‐life settings is inherently complex, tedious, and slow, because of the following. To provide solid statistically‐significant results, job schedulers must be tested many times, for different scenarios varying jobs, nodes, and communication related variables in a controlled manner. Arranging several SDs implies encourage SD owners to participate in experiments or acquire and set up various devices. Even when vivo evaluation is viable for a small test bed, the speed at which performance samples are obtained is limited by batteries charging and discharging time.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 The considerable joint computing potential present in a group of nearby smartphones and tablets, hereinafter referred to as Smart Devices (SDs), leads many efforts in making them to cooperate in the execution of resource-intensive software. 3 In those situations where SDs share physical locality with data acquisition devices, scavenging the joint computing capabilities to execute jobs is aligned with the fog/edge computing principles, which advocate to bring computing resources closer to where computing needs are originated. The SDs' computing potential scavenging has also been proposed as a way of increasing the computing power of Grid distributed computing platforms.…”

Section: Introductionmentioning

confidence: 99%

“…Despite interesting recent progress in the area, 3 experimentation with real-life settings is inherently complex, tedious, and slow, because of the following.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

Self Cite

View full text Add to dashboard Cite

Summary Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back‐haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free‐to‐use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace‐based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace‐driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Despite interesting recent progress in the area, 3 experimentation with real-life settings is inherently complex, tedious, and slow, because of the following.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations