Towards Computational Offloading in Mobile Device Clouds

Mtibaa, Abderrahmen; Harras, Khaled A.; Fahim, Afnan

doi:10.1109/cloudcom.2013.50

Cited by 89 publications

(46 citation statements)

References 11 publications

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“…In [3], a clinical approach towards design of a device cloud is proposed. In [4] an addition to this approach is proposed that support this design with an approach where the computational loads and its effect on individual devices is measured. Likewise, [14] presents a mobile intercloud architecture that takes assistance from virtualized mobile terminals in a user specified application environment.…”

Section: Related Workmentioning

confidence: 99%

Reinforcing Edge Computing with Multipath TCP Enabled Mobile Device Clouds

Venkatraman

Kroep

Joshi

et al. 2019

2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)

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In recent years, enormous growth has been witnessed in the computational and storage capabilities of mobile devices. However, much of this computational and storage capabilities are not always fully used. On the other hand, popularity of mobile edge computing which aims to replace the traditional centralized powerful cloud with multiple edge servers is rapidly growing. In particular, applications having strict latency requirements can be best served by the mobile edge clouds due to a reduced round-trip delay. In this paper we propose a Multi-Path TCP (MPTCP) enabled mobile device cloud (MDC) as a replacement to the existing TCP based or D2D device cloud techniques, as it effectively makes use of the available bandwidth by providing much higher throughput as well as ensures robust wireless connectivity. We investigate the congestion in mobile-device cloud formation resulting mainly due to the message passing for service providing nodes at the time of discovery, service continuity and formation of cloud composition. We propose a user space agent called congestion handler that enable offloading of packets from one sub-flow to the other under link quality constraints. Further, we discuss the benefits of this design and perform preliminary analysis of the system.

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Section: Related Workmentioning

confidence: 99%

Reinforcing Edge Computing with Multipath TCP Enabled Mobile Device Clouds

Venkatraman

Kroep

Joshi

et al. 2019

2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)

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“…Mobile cloud architecture, application model, time-constrained or real time Fig. 1: Conceptual overview of the Aura model task offloading scheduler and algorithm are proposed in [18], [19], [20], [21], [22]. Our work is complementary to the research on mobile computation offloading and can leverage the offloading or scheduling scheme on the mobile devices to determine optimal strategies for offloading tasks to IoT clouds.…”

Section: Related Workmentioning

confidence: 99%

Aura: An IoT Based Cloud Infrastructure for Localized Mobile Computation Outsourcing

Hasan

Hossain

Khan

2015

2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering

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Many applications require localized computation in order to ensure better performance, security, and lower costs. In recent years, the emergence of Internet-of-Things (IoT) devices has caused a paradigm shift in computing and communication.IoT devices are making our physical environment and infrastructures smarter, bringing pervasive computing to the mainstream. With billions of such devices slated to be deployed in the next five years, we have the opportunity to utilize these devices in converting our physical environment into interactive, smart, and intelligent computing infrastructures. In this paper, we present Aura -a highly localized IoT based cloud computing model. Aura allows clients to create ad hoc clouds using the IoT and other computing devices in the nearby physical environment, while providing the flexibility of cloud computing. Aura provides localized computation capability from untapped computing resources. Computations done on Aura are highly flexible, giving clients full control to start, stop, migrate, and restart computations in nearby devices as the clients move between different physical locations. To demonstrate the feasibility of Aura, we have ported a lightweight version of MapReduce to run on IoT devices, and evaluated its performance.

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“…access to location) thus they cannot be remotely deployed. Mtibaa et al [2013] propose the idea of mobile device cloud, where the union of mobile devices located in proximity may be used to perform computationally intensive tasks. The authors built a framework and performed a set of experiments to show the feasibility to use a mobile device cloud to perform offloading.…”

Section: Cloudlet-base Approachesmentioning

confidence: 99%