Seamless computing and service sharing in community networks have been gaining momentum due to the emerging technology of community network micro-clouds (CNMCs). However, running services in CNMCs can face enormous challenges such as the dynamic nature of micro-clouds, limited capacity of nodes and links, asymmetric quality of wireless links for services, deployment mod- els based on geographic singularities rather than network QoS, and etc. CNMCs have been increasingly used by network-intensive services that exchange significant amounts of data between the nodes on which they run, therefore the performance heavily relies on the available bandwidth resource in a network. This paper proposes a novel bandwidth-aware service placement algorithm which out- performs the current random placement adopted by Guifi.net. Our preliminary results show that the proposed algorithm consistently outperforms the current random placement adopted in Guifi.net by 35 regarding its bandwidth gain. More importantly, as the number of services increases, the gain tends to increase accordingly.
Edge-clouds provide a promising new approach to significantly reduce network operational costs by moving computation closer to the edge. A key challenge in such systems is to decide where and when services should be migrated in response to user mobility and demand variation. The objective is to optimize operational costs while providing rigorous performance guarantees. In this paper, we model this as a sequential decision making Markov Decision Problem (MDP). However, departing from traditional solution methods (such as dynamic programming) that require extensive statistical knowledge and are computationally prohibitive, we develop a novel alternate methodology. First, we establish an interesting decoupling property of the MDP that reduces it to two independent MDPs on disjoint state spaces. Then, using the technique of Lyapunov optimization over renewals, we design an online control algorithm for the decoupled problem that is provably cost-optimal. This algorithm does not require any statistical knowledge of the system parameters and can be implemented efficiently. We validate the performance of our algorithm using extensive trace-driven simulations. Our overall approach is general and can be applied to other MDPs that possess a similar decoupling property. We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco. Seamless computing and data access is enabled by the emerging technology of mobile micro-clouds (MMCs). Different from traditional centralized clouds, an MMC is typically connected directly to a wireless base-station and provides services to a small group of users, which allows users to have instantaneous access to cloud services. Due to the limited coverage area of base-stations and the dynamic nature of mobile users, network background traffic, etc., the question of where to place the services to cope with these dynamics arises. In this paper, we focus on dynamic service placement for MMCs. We consider the case where there is an underlying mechanism to predict the future costs of service hosting and migration, and the prediction error is assumed to be bounded. Our goal is to find the optimal service placement sequence which minimizes the average cost over a given time. To solve this problem, we first propose a method which solves for the optimal placement sequence for a specific look-ahead time-window, based on the predicted costs in this time-window. We show that this problem is equivalent to a shortest-path problem and propose an algorithm with polynomial time-complexity to find its solution. Then, we propose a method to find the optimal look-ahead window size, which minimizes an upper bound of the average cost. Finally, we evaluate the effectiveness of the proposed approach by simulations with realworld user-mobility traces.