Search

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We third deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

In the traditional video streaming service paradigm, content providers typically provision the requested video content to viewers through a central content delivery network (CDN). However, remote viewers usually experience long video streaming delay due to uncertain wide area network delay, which severely affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

Federated learning (FL) has reshaped the learning paradigm by overcoming privacy concerns and siloed data issues. In FL, an aggregator schedules a set of mobile users (MUs) to collectively train a global model with their local datasets and subsequently aggregates their model updates to obtain a new global model. However, the users have many uncertainties like unstable network connections and volatile availability, which leads to the straggler problem and deteriorates the efficiency of the FL system. Besides, the issue of non-IID datasets hinders the convergence performance of the global model. To hurdle the user uncertainties, we associate a deadline with the decision in each round and partially collect MUs’ updates after the deadline, which can be achieved by considering surplus budget constraints. Moreover, we introduce fairness constraints for the non-IID issue where we ensure that all MUs have chances to be scheduled each round but the MUs with large and diverse local datasets will preferentially be selected. We propose a deadline-aware task replication for surplus client scheduling policy, called FEDDATE-CS. FEDDATE-CS is developed based on a novel contextual-combinatorial multi-armed bandit (CCMAB) learning framework with fairness guarantee. We extend the hypercube-based CCMAB framework by integrating the Lyapunov queuing technique and rigorously prove that FEDDATE-CS achieves a sublinear regret bound when learning the optimal client scheduling solution under uncertainties. Moreover, our FEDDATE-CS provides an $[\mathcal {O}(1/V),\mathcal {O}(V)]$[O(1/V),O(V)] regret-fairness tradeoff for any fairness control factor $V>0$V>0. We conduct extensive evaluations to verify the significant superiority of FEDDATE-CS over benchmarks.

With the development of 5G technology, mobile edge computing is emerging as an enabling technique to reduce the response latency of network services by deploying cloudlets at 5G base stations to form mobile edge cloud (MEC) networks. Network service providers now shift their services from remote clouds to cloudlets of MEC networks in the proximity of users. However, the permanent placement of network services into an MEC network is not economic due to limited computing and bandwidth resources imposed on its cloudlets. A smart way is to cache frequently demanded services from remote clouds to cloudlets of the MEC network. In this paper, we study the problem of service caching in an MEC network under a service market with multiple network service providers competing for both computation and bandwidth resources in terms of Virtual Machines (VMs) in the MEC network. We first propose an Integer Linear Program (ILP) solution and a randomized rounding algorithm, for the problem without VM sharing among different network service providers. We then devise a distributed and stable game-theoretical mechanism for the problem with VM sharing among network service providers, with the aim to minimize the social cost of all network service providers, through introducing a novel cost sharing model and a coalition formation game. We also analyze the performance guarantee of the proposed mechanism, Strong Price of Anarchy (SPoA). We third consider the cost- and delay-sensitive service caching problem with temporal VM sharing, and propose a mechanism with provable SPoA. We finally evaluate the performance through extensive simulations and a real world test-bed implementation. Experimental results demonstrate that the proposed algorithms outperform existing approaches by achieving at least $40\%$40% lower social cost via service caching and resource sharing among different network service providers.

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

Mobile edge computing (MEC) is envisioned as a promising solution to real-time services in Internet of Vehicles (IoV) by enabling edge caching, computing and communication. However, it is still challenging to implement multimedia streaming in MEC-based IoV due to dynamic vehicular environments and heterogeneous network resources. In this paper, we present an MEC-based architecture for adaptive-bitrate-based (ABR) multimedia streaming in IoV, where each multimedia file is segmented into multiple chunks encoded with different bitrate levels. Then, we formulate a joint resource optimization (JRO) problem by synthesizing heterogeneous edge cache and communication resource constraints, which aims at achieving both smooth play and high-quality service by optimizing chunk placement and transmission. For chunk placement, a multi-armed bandit (MAB) algorithm is proposed for online scheduling with low overhead but slow convergence. Further, a deep-Q-learning algorithm is proposed to improve cache reward and speed up convergence by using replay memory for repeatedly training. For chunk transmission, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which determines bandwidth allocation and quality level based on a benefit function incorporating quality level, available playback time, and freezing delay. Lastly, we build the simulation model and give comprehensive performance evaluation, which demonstrates the superiority of proposed algorithms.