This paper considers the problem of fair allocation of multiple types of resources in heterogeneous servers, along with a resource type external to those servers. Our work is motivated by the need for fair multi-resource allocation in mobile edge computing (MEC), where the users must upload their tasks over a single dedicated wireless communication link that exists outside the computing servers. We propose a fair multi-resource allocation mechanism for this environment, termed Task Share Fairness with External Resource (TSF-ER), which finds the Kalai-Smorodinsky bargaining solution satisfying important fairness properties. We show that TSF-ER is envy-free, Pareto optimal, and strategy-proof, and it satisfies the property of sharing incentive. Large-scale simulation driven by Google and Alibaba cluster trace further shows that TSF-ER significantly outperforms the existing utilitarian, Nash social welfare maximizer, and egalitarian solutions, leading to fairer resource allocation while maintaining a high level of resource utilization.

We consider the problem of fair multi-resource allocation for mobile edge computing (MEC) with multiple access points. In MEC, user tasks are uploaded over wireless communication channels to the access points, where they are then processed with multiple types of computing resources. What distinguishes fair multi-resource allocation in the MEC environment from more general cloud computing is that a user may experience different levels of wireless channel quality on different access points, so that the user's channel bandwidth demand is not fixed. Existing resource allocation studies for cloud computing generally consider Pareto Optimality (PO), Envy-Freeness (EF), Sharing Incentive (SI), and Strategy-Proofness (SP) as the most desirable fairness properties. In this work, we show these properties are no longer compatible in MEC, since there exists no resource allocation rule that can satisfy PO+EF+SP or PO+SI+SP. Hence, we propose a resource allocation rule, called Maximum Task Product (MTP), that retains PO, EF, and SI. Extensive simulation driven by Google cluster traces further shows that MTP improves resource utilization while achieving these fairness properties.

This paper considers a set of mobile users that employ cloud-based computation offloading. In order to execute jobs in the cloud, the user uploads must occur over a base station channel that 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 processed. 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 paper shows that for a wide range of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel-like method for determining this equilibrium is introduced. Results are presented that illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method. Data is also presented that show 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 performance.

In this paper we consider mobile users that employ computation offloading. In computational offloading, users can reduce energy consumption by executing jobs on a remote cloud server, rather than locally. In order to execute a job in the cloud, a mobile user must upload the job over a base station channel which is shared by all of the uploading users. The jobs are subject to hard deadline constraints, and since the channel quality may be different for each user, this may restrict the users ability to reduce energy usage. The system is modelled as a competitive game where each user is interested in minimizing its own energy use. The game is subject to the real-time constraints imposed by job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The paper shows that for known classes of parameters, a game where each user independently adjusts its offload decisions always has a pure Nash equilibrium, and a Gauss-Seidel-like method for determining this equilibrium is presented. Results are then presented which illustrate that the system always converges to a Nash equilibrium using Gauss-Seidel. Data is presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions obtained. In particular, we find that the solutions perform well compared to a lower bound on total energy performance.