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The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness.

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost : they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Low retrieval accuracy : they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix ${{\mathbf P}}$ P . Then, it uses the matrix ${{\mathbf P}}$ P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix ${{\mathbf P}}$ P to the edge server and uses it to multiply the image ${{\mathbf x}}$ x . The result ${{\mathbf P}}^T{{\mathbf x}}$ P T x , i.e. , image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix ${{\mathbf P}}$ P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.

Mobile image recognition services, which provide people with image recognition services through the cameras of mobile devices, are revolutionizing our lives. However, most existing cloud/edge-based approaches suffer from two major limitations, (i) Low recognition accuracy and high network bandwidth pressure, and (ii) Not easy to extract features based on currently available resources of mobile devices. In this paper, we propose a resource-aware feature extraction framework for mobile image recognition services. The proposed framework consists of discriminative feature extraction (DFE) and NestDFE algorithms. The DFE algorithm can generate an extractor E to extract discriminative features from the image data set on the edge server and images on mobile devices. Thus, the proposed framework can achieve higher recognition accuracy and require mobile devices to upload less feature data to the edge server. The NestDFE algorithm generates a single multi-capacity extractor that acts as a series of sub-extractors and enables mobile devices to dynamically select sub-extractors. Experimental results show that the proposed framework improves recognition accuracy by about 22.65% and reduces network traffic by about 76.09% compared with existing approaches.

In mobile edge computing, popular mobile applications, such as augmented reality, usually offload their tasks to resource-rich edge servers. The user experience can be considerably affected when many mobile users compete for the limited communication and computation resources. The key technical challenge in task offloading is to guarantee the Quality of Service (QoS) for such applications. Existing work on task offloading focus on deterministic QoS guarantee, which means that tasks have to complete before the given deadline with 100%. However, it is impractical to impose a deterministic QoS guarantee for tasks due to the high dynamics of the wireless environment when offloading to edge servers. In this paper, we focus on task offloading with statistical QoS guarantee, which can further save more energy by loosing the QoS requirement. Specially, we first propose a statistical computation model and a statistical transmission model to quantify the correlation between the statistical QoS guarantee and task offloading strategy. Then, we formulate the task offloading problem as an mixed integer non-Linear programming problem. We propose an algorithm to provide the statistical QoS guarantee for tasks using convex optimization theory and Gibbs sampling method. Experiment results show that the proposed algorithm outperforms the three baselines.