In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem (ERAP) as a Mixed-Integer Linear Program (MILP) and prove that ERAP is NP-hard. To solve the problem efficiently, we propose two resource allocation mechanisms. First, we develop an auction-based mechanism and prove that the proposed mechanism is individually-rational and produces envy-free allocations. We also propose an LP-based approximation mechanism that does not guarantee envy-freeness, but it provides solutions that are guaranteed to be within a given distance from the optimal solution. We evaluate the performance of the proposed mechanisms by conducting an extensive experimental analysis on ERAP instances of various sizes. We use the optimal solutions obtained by solving the MILP model using a commercial solver as benchmarks to evaluate thequality of solutions. Our analysis shows that the proposed mechanisms obtain near optimal solutions for fairly large size instances of the problem in a reasonable amount of time.

The Quality of Service (QoS) in Mobile Edge Computing (MEC) systems is significantly dependent on the application offloading and placement decisions. Due to the movement of users in MEC networks, an optimal application placement might turn into the least efficient placement in few minutes. Thus, it is crucial to take the dynamics of the system into account when designing application placement mechanisms. On the other hand, energy consumption of servers is a significant component of the cost of services in MEC systems and must also be considered in the design of the mechanisms. In this article, we model the problem of energy-aware application placement in edge computing systems as a multi-stage stochastic program. The objective is to maximize the QoS of the system while taking into account the limited energy budget of the edge servers. To solve the problem, we design a novel parallel Sample Average Approximation (SAA) algorithm. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real-world trace data.

Mobile Edge Computing (MEC) is a new paradigm that aims at decreasing the response time of running mobile applications by offloading the component of the applications on the servers located at the edge of the network instead of on the cloud servers. In this paper, we address a very important problem in the management of MEC systems, that is, the problem of finding an efficient application placement on the edge servers such that the cost of execution is minimized. We develop a multi-stage stochastic programming model for the application placement problem in edge computing systems and design a novel parallel greedy algorithm based on the Sample Average Approximation method to solve it. We evaluate the performance of the proposed algorithm by conducting extensive experimental analysis using data extracted from a real-world dataset. The experimental results show that the proposed algorithm can solve the problem efficiently.