We consider a mobile cloud computing system with multiple users, a remote cloud server, and a computing access point (CAP). The CAP serves both as the network access gateway and a computation service provider to the mobile users. It can either process the received tasks from mobile users or offload them to the cloud. We jointly optimize the offloading decisions of all users, together with the allocation of computation and communication resources, to minimize the overall cost of energy consumption, computation, and maximum delay among users. The joint optimization problem is formulated as a mixed-integer program. We show that the problem can be reformulated and transformed into a non-convex quadratically constrained quadratic program, which is NP-hard in general. We then propose an efficient solution to this problem by semidefinite relaxation and a novel randomization mapping method. Furthermore, when there is a strict delay constraint for processing each user's task, we further propose a three-step algorithm to guarantee the feasibility and local optimality of the obtained solution. Our numerical results show that the proposed solutions give nearly optimal performance under a wide range of parameter settings, and the addition of a CAP can significantly reduce the cost of multi-user task offloading compared with conventional mobile cloud computing where only the remote cloud server is available.

We consider a general multi-user mobile cloud computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

We consider a general multi-user mobile cloud computing system with a computing access point (CAP), where each mobile user has multiple independent tasks that may be processed locally, at the CAP, or at a remote cloud server. The CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users' tasks as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. This problem is NP-hard in general. We propose an efficient three-step algorithm comprising of semidefinite relaxation (SDR), alternating optimization (AO), and sequential tuning (ST). It is shown to always compute a locally optimal solution, and give nearly optimal performance under a wide range of parameter settings. Through evaluating the performance of different combinations of the three components of this SDR-AO-ST algorithm, we provide insights into their roles and contributions in the overall solution. We further compare the performance of SDR-AO-ST against a lower bound to the minimum cost, purely local processing, purely cloud processing, and hybrid local-cloud processing without using the CAP. Our numerical results demonstrate the effectiveness of the proposed algorithm in the joint management of computation and communication resources in mobile cloud computing systems with a CAP.

We consider a mobile cloud computing system consisting of multiple users, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from mobile users or offload them to the cloud. We aim to jointly optimize the offloading decisions of all users and the CAP, together with communication and processing resource allocation, to minimize the overall cost of energy, computation, and the maximum delay among all users. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We further propose an efficient solution to this problem by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations.

We consider a general multi-user mobile cloud computing system where each mobile user has multiple independent tasks. These mobile users share the communication resource while offloading tasks to the cloud. We aim to jointly optimize the offloading decisions of all users as well as the allocation of communication resource, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. An efficient approximate solution is proposed by using separable semidefinite relaxation, followed by recovery of the binary offloading decision and optimal allocation of the communication resource. For performance benchmark, we further propose a numerical lower bound of the minimum system cost. By comparison with this lower bound, our simulation results show that the proposed algorithm gives nearly optimal performance under various parameter settings.

Multirelay networks exploit spatial diversity by transmitting user's messages through multiple relay paths. Most works in the literature on cooperative or relay networks assume that all terminals are fully cooperative and neglect the effect of possibly existing malicious relay behaviors. In this work, we consider a multirelay network that consists of both cooperative and malicious relays, and aims to obtain an improved understanding on the optimal behaviors of these two groups of relays via information-theoretic mutual information games. By modeling the set of cooperative relays and the set of malicious relays as two players in a zero-sum game with the maximum achievable rate as the utility, the optimal transmission strategies of both types of relays are derived by identifying the Nash equilibrium of the proposed game. Our main contributions are twofold. First, a generalization to previous works is obtained by allowing malicious relays to either listen or attack in Phase 1 (source-relay transmission phase). This is in contrast to previous works that only allow the malicious relays to listen in Phase 1 and to attack in Phase 2 (relay-destination transmission phase). The latter is shown to be suboptimal in our problem. Second, the impact of CSI knowledge at the destination on the optimal attack strategy that can be adopted by the malicious relays is identified. In particular, for the more practical scenario where the interrelay CSI is unknown at the destination, the constant attack is shown to be optimal as opposed to the commonly considered Gaussian attack.

Cooperative relaying refers to a technique that allows the source to transmit its messages to the destination via the relaying of multiple cooperative partners and exploits the spatial diversity gains inherent in multiuser wireless systems. In this work, we examine cooperative networks with cooperative and malicious relays and determine the optimal behavior for both kinds of relays using a game-theoretic approach. We formulate the problem as a zero-sum game, determining the optimal relay strategies by identifying the Nash equilibrium of the proposed game under individual power constraints. We prove, with Rayleigh fading, the optimal strategy for malicious relays is to transmit independent Gaussian noise using full power at each relay and for cooperative relays is to independently re-encode the source's message into Gaussian signals and forward them to the destination. Inter-cooperation among relays is unnecessary. The results are verified through numerical simulations.

The information and communication technology (ICT) industry has emerged as one of the major sources of world energy consumption due to its explosive growth. As a result, energy saving in ICT industry has attracted more attention. Meanwhile, cloud computing is becoming a disruptive technology with profound implications for ICT industry. Its emergence promises the on-demand provisioning of resources as a service. In this paper, we study the energy saving issue in cloud computing. In a scenario where a data center has multiple data servers to deal with jobs, the servers are switched into sleeping mode in periods of low traffic load to reduce energy consumption while guaranteeing the quality of service in terms of job blocking probability. The problem is formulated as a Markov decision process. It is proved that the optimal policy has a double threshold structure. Numerical and simulation results show that our proposed policy can significantly reduce the energy consumption.