Decentralized Scheduling for Offloading of Periodic Tasks in Mobile Edge Computing

IEEE Trans. Cloud Comput.

2021

Self Cite

We consider the computation offloading problem in an edge computing system in which an operator jointly manages wireless and computing resources across devices that make their offloading decisions autonomously with the objective to minimize their own completion times. We develop a game theoretical model of the interaction between the devices and an operator that can implement one of two resource allocation policies, a cost minimizing or a time fair resource allocation policy. We express the optimal cost minimizing resource allocation policy in closed form and prove the existence of Stackelberg equilibria for both resource allocation policies. We propose two efficient decentralized algorithms that devices can use for computing equilibria of offloading decisions under the cost minimizing and the time fair resource allocation policies. We establish bounds on the price of anarchy of the games played by the devices and by doing so we show that the proposed algorithms have bounded approximation ratios. Our simulation results show that the cost minimizing resource allocation policy can achieve significantly lower completion times than the time fair allocation policy. At the same time, the convergence time of the proposed algorithms is approximately linear in the number of devices, and thus they could be effectively implemented for edge computing resource management.

Section: B Computing Equilibrium Offloading Decisionsmentioning

confidence: 99%

“…(3) In (3) we made the common assumption that the time needed to transmit the results from the cloud to the device can be neglected [15], [11], [16], [17], as for typical applications (e.g., face and object recognition), the size of the result of the computation is much smaller than D i .…”

Section: Cost Modelmentioning

confidence: 99%

Joint Management of Wireless and Computing Resources for Computation Offloading in Mobile Edge Clouds

IEEE Trans. Cloud Comput.

2021

Self Cite

“…A few recent works provided a game theoretic treatment of the mobile computation offloading problem for a single time slot [49], [50], [51], [7], [52], [53], [54], [55]. Compared to [49], we characterize the structure of the computed equilibrium, prove the bound on the price of anarchy and show an example of a better reply cycle. [50] considered a two-stage game, where first each mobile user chooses the parts of its task to offload with the objective to minimize the energy consumption and the task completion time, and then the cloud allocates computational resources to the offloaded parts with the objective to maximize its profit.…”

Section: I I R E L At E D W O R Kmentioning

confidence: 99%

Computation Offloading Scheduling for Periodic Tasks in Mobile Edge Computing

IEEE/ACM Trans. Networking

2020

Self Cite

Motivated by various delay sensitive applications, we address the problem of coordinating the offloading decisions of wireless devices that periodically generate computationally intensive tasks. We consider autonomous devices that aim at minimizing their own cost by choosing when to perform their tasks and whether or not to offload their tasks to an edge cloud through one of the multiple wireless links. We develop a game theoretical model of the problem, prove the existence of pure strategy Nash equilibria and propose a polynomial complexity algorithm for computing an equilibrium. Furthermore, we characterize the structure of the equilibria, and by providing an upper bound on the price of anarchy of the game we establish an asymptotically tight bound on the approximation ratio of the proposed algorithm. Our simulation results show that the proposed algorithm achieves significant performance gain compared to uncoordinated computation offloading at a computational complexity that is on average linear in the number of devices.

“…(3) In (3) we made the common assumption that the time needed to transmit the results from the cloud to the device can be neglected [5], [11], [15], [16], as for typical applications (e.g., face and object recognition), the size of the result of the computation is much smaller than D i .…”

Section: Cost Modelmentioning

confidence: 99%

“…We refer to the resulting strategic game Γ(P * r , P * c ) =< N , (D i ) i∈N , (C i ) i∈N > as the optimal allocation computation offloading game (OA-COG), in which the players are WDs with the objective to minimize their costs given by (16). Clearly, if the OA-COG has a pure strategy Nash equilibrium (NE) then the MEC-OG has an SPE.…”

Section: B Computing Equilibrium Offloading Decisionsmentioning

confidence: 99%

Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

2019

Self Cite

We consider the problem of allocating wireless and computing resources to a set of autonomous wireless devices in an edge computing system. Devices in the system can decide whether or not to use edge computing resources for offloading computing tasks so as to minimize their completion time, while the edge cloud operator can allocate wireless and computing resources to the devices. We model the interaction between devices and the operator as a Stackelberg game, prove the existence of Stackelberg equilibria, and propose an efficient decentralized algorithm for computing equilibria. We provide a bound on the price of anarchy of the game, which also serves as an approximation ratio bound for the proposed algorithm. Our simulation results show that the joint allocation of wireless and computing resources by the operator can halve the completion times compared to a system with static resource allocation. At the same time, the convergence time of the proposed algorithm is approximately linear in the number of devices, and thus it could be effectively implemented for edge computing resource management.