Energy efficient offloading for competing users on a shared communication channel

Abstract

In this thesis we consider a set of mobile users that employ cloud-based computation offloading. In computation offloading, user energy consumption can be decreased by uploading and executing jobs on a remote server, rather than processing the jobs locally. In order to execute jobs in the cloud however, the user uploads must occur over a base station channel which is shared by all of the uploading users. Since the job completion times are subject to hard deadline constraints, this restricts the feasible set of jobs that can be remotely processed, and may constrain the users ability to reduce energy usage. The system is modelled as a competitive game in which each user is interested in minimizing its own energy consumption. The game is subject to the real-time constraints imposed by the job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The thesis shows that for a variety of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel method for determining this equilibrium is introduced. Results are presented which illustrate that the system always converges to a Nash equilibrium using the GaussSeidel method. Data is also presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions.We find that the solutions perform well compared to a lower bound on total energy

Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks

“…A few recent works provided a game theoretic treatment of computation offloading in a game theoretical setting [24], [25], [7], [26], [27], [28]. [24] considers a two-stage problem, where first each mobile user decides what share of its task to offload so as to minimize its energy consumption and to meet its delay deadline, and then the cloud allocates computational resources to the offloaded tasks.…”

An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing

“…In this section, the performance of the proposed mechanism is evaluated through numerical simulations designed by using the MATLAB. e compared algorithms are the competitionbased algorithm [30] and the user-satisfaction-based offloading algorithm [31]. eir features are described as follows:…”

“…In addition, the parameters of the other three mobile devices include CPU processing parameters, such as χ i , α i , and β i . ese parameters are adopted as in [30]. In the simulation, the type of the mobile device in the mobile-edge cloud computing scenario is randomly selected among the abovementioned three types, and each mobile device has only one task waiting to be executed.…”

Decentralized Scheduling for Offloading of Periodic Tasks in Mobile Edge Computing

“…A few recent works provided a game theoretic treatment of the mobile computation offloading problem for a single time slot [31], [32], [5], [18], [33], [34], [7]. [31] considers a two-stage game, where first each mobile user chooses the parts of its task to offload, and then the cloud allocates computational resources to the offloaded parts.…”

“…[32] considered a three-tier cloud architecture, and provided a distributed algorithm for the computing a mixed strategy equilibrium. [33] considered tasks that arrive simultaneously and a single wireless link, and showed the existence of equilibria when all mobile users have the same delay budget. [5] showed that assuming a single wireless link and link rates determined by the Shannon capacity of an interference channel, the resulting game is a potential game.…”