Decentralized Computation Offloading Game for Mobile Cloud Computing

By Xu Chen. IEEE Transactions on Parallel and Distributed Systems, 2014

852 citations recorded by the source corpus.

Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/69fc2fa7bffa570b127f7a55ede90f99082e2e84

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Citations in the corpus

Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, methodology): “Similar to the existing works [6], [9], [10], [13], [20], [22], [23], to enable tractable analysis, we consider a quasistatic scenario where all UEs and the wireless network remain stationary during an offloading period (usually within several seconds [10]).”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “frequency-division multiple access (OFDMA) in [15] and [16], while game-theoretic decentralized computation offloading algorithms were proposed for multi-user MEC systems in [17] and [18] for single- and multi-channel wireless environments, respectively.”
Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing (2017, methodology): “For the computation model, we consider that each UE kn has a computation task Wkn (Zkn , Dkn ), which can be computed either locally on the mobile device or remotely on the MEC server via computation offloading, as in [16].”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “In [85] and [113], the computation tasks were assumed to be either locally executed”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, methodology): “Since the size of the output is generally much smaller than the input, plus the downlink data rate is much higher than that of the uplink, we omit the delay of transferring the output in our computation, as also considered in [9], [14], [31].”
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (2018, background): “Here an edge is typically a micro-data center that is resource-rich, attached to a base station (BS) [6] or an access point [7], and available for use by nearby devices.”
Edge-computing-driven Internet of Things: A Survey (2022, background): “proposed a game-theoretic approach to achieve efficient computation offloading for edge computing and formulate the distributed computation offloading decision-making problem among end devices as a multi-user computation offloading game [37, 38].”
Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing (2017, methodology): “Similar to the work in [13], the computational energy consumption of the MEC server is omitted here, due to the fact that in this paper we consider the consumption (time and energy) from the users’ perspective, while the omitted energy is consumed by the MEC server.”
Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory (2018, background): “Similar to that in [17], [28], we define a more general security-latency metric to balance the network scale and the block verification time for”
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing (2014, methodology): “A decentralized solution, based on a gametheoretic formulation of the problem, was recently proposed in [26], [11].”
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling (2016, methodology): “Besides, we could take advantage of parallelism by processing a portion of total data at the local side and the remaining part at the cloud side concurrently [9], [12], [13], [19], [22].”
Resource Management in Fog/Edge Computing (2018, methodology): “A game theoretic approach was employed as a decentralized approach for achieving the Nash equilibrium among cooperative devices [29].”
Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, methodology): “According to [11], [12], and [22], CPU power consumption is a superlinear function of execution frequency and is given as”
Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing (2015, background): “Sim ilar to existing studies for mobile cloud computing [9], [17], [2 4], we hence account for the most critical factor (i.”
On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration (2017, neutral): “A similar orchestration problem focusing on decentralized decisions is analyzed using game theory in [41] and [171], allowing devices to be self-organized adding autonomics into the mobile cloud computing paradigm.”
Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing (2016, background): “Compared to our work, however, they did not consider the task precedence and resource scheduling in computation offloading policy.”