VMatch: A Matching Theory Based VDC Reconfiguration Strategy

Satpathy, Anurag; Sahoo, Manmath Narayan; Behera, Lucky; Swain, Chittaranjan; Mishra, Ashutosh

doi:10.1109/cloud49709.2020.00031

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The work further presents a DA-based distributed task offloading algorithm called SPATO to tackle the selfishness and rational of individual agents. In particular, PLs of agents are constructed using the analytical hierarchy process (AHP) [40] that accounts for multiple criteria of system wise objectives to obtain the rankings. The simulation results indicate that the proposed algorithms enable the network to achieve a reduced offloading delay and energy consumption as well as minimum outage compared to the random offloading plan and SMETO [37].…”

Section: B Related Workmentioning

confidence: 99%

DISCO: Distributed computation offloading framework for fog computing networks

Tran-Dang

Kim

2023

J. Commun. Netw.

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Section: B Related Workmentioning

confidence: 99%

DISCO: Distributed computation offloading framework for fog computing networks

Tran-Dang

Kim

2023

J. Commun. Netw.

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“…The work further presents a DA-based distributed task offloading algorithm called SPATO to tackle the challenge. In particular, the preference lists of agents are constructed using the analytical hierarchy process (AHP) [64] that accounts for multiple criteria of system wise objectives to obtain the rankings.…”

Section: B Many-to-one Matchingmentioning

confidence: 99%

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Tran-Dang

Kim

2022

IEEE Access

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Fog computing has been widely integrated in the IoT-based systems, creating IoT-Fog-Cloud (IFC) systems to improve the system performances and satisfy the quality of services (QoS) and quality of experience (QoE) requirements for the end users (EUs). This improvement is enabled by computational offloading schemes, which perform the task computation nearby the task generation sources (i.e., IoT devices, EUs) on behalf of remote cloud servers. To realize the benefits of offloading techniques, however, there is a need to incorporate efficient resource allocation frameworks, which can deal effectively with intrinsic properties of computing environment in the IFC systems such as resource heterogeneity of computing devices, various requirements of computation tasks, high task request rates, and so on. While the centralize optimization and non-cooperative game theory based solutions are applicable in a certain number of application scenarios, they fail to be efficient in many of cases, where the global information and control might be unavailable or cost-intensive to achieve it in the large-scale systems. The need of distributed computational offloading algorithms with low computation complexity has motivated a surge of solutions using matching theory. In the present review, we first describe the fundamental concept of this emerging tool enabling the distributed implementation in the computing environment. Then the key solution concepts and algorithmic implementations proposed in the framework of literature are highlighted and discussed. Given the powerful tool of matching theory, its full capability is still unexplored and unexploited in the literature. We thereby discover and discuss existing challenges and corresponding solutions that the matching theory can be applied to resolve them. Furthermore, new problems and open issues for application scenarios of modern IFC systems are also investigated thoroughly. INDEX TERMSIoT-Fog-Cloud systems, Matching theory, Distributed algorithm, Computational offloading. I. INTRODUCTION Practically, the Internet of Things (IoT) has become an integral element for realizing smart practical systems such as smart cities [1], smart grids [2], smart factories [3], smart logistics, and supply chain [4], [5]. The fundamental aspect of IoT-based systems is to connect all devices through the Internet protocol to exchange high volume data and pro-cess them to create smart services and applications [6], [7]. Owing to limited computation resources, network, storage, and energy, IoT devices are inadequate for executing all computational tasks, especially tasks with huge volumes and complex data structures. Cloud computing is an essential solution to this problem because it provides powerful resources to fulfill tasks efficiently [8], [9]. However, cloud

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“…We highlight important steps used in the process. A detailed discussion of AHP can be found in [17]. Considering C = {c 1 , c 2 , • • • , c |C| } to be a set of distinct criteria, we construct a pairwise comparison matrix P ∈ R |C| * |C| .…”

Section: ) Ranking Based On Ahpmentioning

confidence: 99%

“…An entry c r,v ∈ P denotes the relative importance of criterion c r against c v . We use a linear judgment scale to set the relative importance of criteria, due to its proven superiority [17] over other judgment scales. This relative importance of criteria is imposed by the stakeholder; IoT devices in our model.…”

Section: ) Ranking Based On Ahpmentioning

confidence: 99%

SPATO: A Student Project Allocation Based Task Offloading in IoT-Fog Systems

Swain¹,

Sahoo²,

Satpathy³

2021

Preprint

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The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network (WAN) latency which makes execution of delay-critical IoT applications inconceivable. To overcome this, service providers (SPs) often deploy multiple fog nodes (FNs) at the network edge that helps in executing offloaded computations from IoT devices with improved user experience. As the FNs have limited resources, matching IoT services to FNs while ensuring minimum latency and energy from an end-user's perspective and maximizing revenue and tasks meeting deadlines from a SP's standpoint is challenging. Therefore in this paper, we propose a student project allocation (SPA) based efficient task offloading strategy called SPATO that takes into account key parameters from different stakeholders. Thorough simulation analysis shows that SPATO is able to reduce the offloading energy and latency respectively by 29% and 40% and improves the revenue by 25% with 99.3% tasks executing within their deadline.

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VMatch: A Matching Theory Based VDC Reconfiguration Strategy

Cited by 7 publications

References 15 publications

DISCO: Distributed computation offloading framework for fog computing networks

DISCO: Distributed computation offloading framework for fog computing networks

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

SPATO: A Student Project Allocation Based Task Offloading in IoT-Fog Systems

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