Task Merging and Scheduling for Parallel Deep Learning Applications in Mobile Edge Computing

With the development of mobile edge computing and neural network Deep Learning (DL), more and more scholars are studying the combination of the two. This paper mainly studies the application of mobile edge computing and neural network DL in IoT computing collaboration and data-aware routing algorithms. Therefore, this paper proposes the deployment options of MEC technology and ETSIMEC in mobile edge computing, combining mobile edge computing with DL, designs an optimization algorithm based on Markov decision process and feature expression learning, and then analyzes and optimizes IoT computing and VANET routing algorithm. In order to have a clearer direction for the optimization algorithm, this paper also designs the edge computing model training and experiment comparison, the DL algorithm comparison experiment, and the routing algorithm simulation experiment and performance analysis. Combined with the experimental results, it is optimized and compared with traditional IoT computing and routing algorithms. Finally, it is concluded that the computing efficiency of IoT computing based on edge computing and DL designed in this paper is 21.33% higher than that of traditional IoT computing. The efficiency of the routing algorithm based on edge computing and DL designed in this paper is 9.29% higher than that of the traditional routing algorithm.

As the deep learning models keep growing larger, it becomes more difficult to carry out highly accurate inference on mobile and Internet of things (IoT) devices with acceptable latency. By using edge computing, a popular approach to reducing inference latency is to partition the deep neural network (DNN) model and off-load part of the model inference to the edge server for processing. However, the partition approach still has some limitations, such as data transmission latency and inflexible model deployment. In this research, we propose a novel two-level inference architecture for deep learning applications in mobile edge computing. Instead of partitioning a large DNN model, we use small models to do two levels of inferences on edge devices and edge servers respectively. With our architecture, the on-device first-level inference is conducted in parallel with the feature uploading to the edge server and different types of second-level models can be flexibly deployed in edge servers according to their environments. Experiments demonstrate that our architecture can decrease computing costs and inference latency in complicated edge environments.

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self‐disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single‐edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

In mobile edge computing(MEC), application partitioning can split the executions into local and edge server parts. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Edge Computing (MEC). In this paper, a new approach on task offloading scheduling for application of mobile edge computing is proposed. We divide the computing task into several subtasks. Then, we analyze the overhead of the system, and propose a fine-grained strategy for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), which can be solved by an improved NSGA-II algorithm. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

The rapid advancement of mobile edge computing (MEC) has led to a growing need for executing complex in‐vehicle applications at the endpoint. These applications are often modeled as workflows with clearly defined task dependencies. In MEC environments, computation offloading plays a crucial role in enhancing Quality of Service (QoS). Nevertheless, most existing offloading strategies primarily address independent tasks, paying little attention to the intricate dependencies among tasks. Moreover, key factors such as budgets and privacy protection are not well tackled. Hence, in this paper, we propose a computation offloading model for workflow applications in mobile edge computing, where the makespan is optimized under budget and privacy constraints. To address it, we introduce an intelligent scheduling method, which consists of task offloading and resource preparation stages, and offloads tasks from the end to the edge and clouds with awareness of task urgency, device computing capabilities, economic costs, and privacy protection. Simulation experiments are conducted based on realistic datasets. Results demonstrate that the proposed method can improve task execution efficiency and ensure data security, thereby enhancing the performance of workflow applications in mobile edge computing scenarios.

Mobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. One challenge of federated learning in mobile edge computing is data heterogeneity since the unified model of federated learning performs poorly when client data are non-independent and identically distributed. Personalized federated learning can obtain amazing models in scenarios where client data are non-independent and identically distributed. This is because the personalized model captures the features of users’ local data more accurately than the unified model. The personalized federated learning problem under two-tier (server-client) federated learning structures has been widely studied and applied. However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two learnable normalization parameters trained by clients to improve the performance of personalized models. Furthermore, in order to facilitate the collaboration among edge servers, we propose a similarity aggregation method to assign aggregation weights based on the Tanimoto coefficients between models. The experimental results show that the proposed algorithm not only increases the convergence speed of personalized models but also improves their testing accuracy.

As a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay‐aware and mobility‐aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%–20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.

QoS prediction for service recommendations in mobile edge computing

In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.

Edge computing provides a feasible technical solution to solve the problem of resource and energy supply constraints in terminal device deep learning applications. By migrating part of the deep learning application computing from the terminal to the cloud edge server, the computing pressure of the terminal device can be greatly relieved, and the energy consumption of the terminal can also be reduced to a certain extent. However, in the process of computing migration, the context switching loss of deep learning applications results in a long delay. This paper studies the problem of minimizing the completion time of deep learning applications under edge computing. We model the deep learning application as a task graph, and consider the constraints such as task switching delay, energy consumption and task dependency. Without changing the structure of the task graph, we transform it into a task scheduling-based application completion time minimization problem. In this paper, we first design a parallel scheduling algorithm based on greedy search. Considering the loss of context switching and the dependence of task data, we can allocate tasks to multiple processors for parallel processing by calculating the expected completion time of tasks, thus reducing the completion time of applications. Based on the experimental results, the greedy scheduling algorithm and comparison algorithm are analyzed and verified to optimize the completion time of different depth learning applications.

Mobile edge computing transfers computing and storage from traditional cloud servers to edge servers, presenting new challenges to quality assurance of edge services. Quality of Service (QoS) is considered as a defacto standard to evaluate similar services with different quality. Given the fact that QoS values are highly dynamic in complex edge environments, QoS monitoring is viewed as a promising technique to comprehensively and effectively understand QoS status of edge services. Due to the distributed storage of historical QoS data and the changeable edge environments, traditional QoS monitoring approaches cannot be directly applied into mobile edge computing. To address this problem, this paper proposes a novel multivariate QoS monitoring approach, called Rs-mBSRM (multivariate BayeSian Runtime Monitoring using Rough set), First, the weights of different QoS attributes are quantified and obtained according to the historical samples based on rough set theory. Second, a Bayesian classifier is constructed for each corresponding edge server during the training stage. Finally, during the monitoring stage, considering the distributed data storage, the classifier is dynamically switched and the attribute weights are also updated due to user mobility. Our experimental results on public data sets show that Rs-mBSRM is better than existing QoS monitoring approaches and is more suitable for mobile edge computing.

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro‐services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro‐services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro‐services constructed by ignoring the processing capabilities of different micro‐services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro‐services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro‐service composition. In order to improve edge resource utilization and user experience on micro‐service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro‐services composition problem in MEC as a constrained multi‐objective optimization problem. Then, a micro‐service composition optimization method M3C combining graph search and branch‐and‐bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

Time-sensitive services (TSSs) have been widely envisioned for future sixth generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the quality of service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyzing the random delay characteristics. In this paper, an MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay bound violation probability, the average delay and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate elaborate allocation of communication and computation resources to improve the QoS of TSSs.