Enhancing Security-Problem-Based Deep Learning in Mobile Edge Computing

To relieve the pressure of processing computation‐intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user’s activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.

With the maturity of 5G technology and the popularization of smart terminal devices, the applications running on mobile terminals are becoming more and more diversified. Most of them are complex, computationally intensive, and time-sensitive applications such as workflow and machine learning tasks. The traditional cloud computing model is far away from the mobile terminal and thus cannot meet the stringent requirements of these applications on delay and energy consumption. As a new computing model, mobile edge computing can better solve the above problems. Mobile edge computing sinks part of the computing and storage resources in the cloud to the edge of the network close to the mobile device. With computational offloading, complex applications are offloaded to nearby edge servers for execution, which leads to low delay and energy consumption. The existing researches mainly focus on independent task offloading in mobile edge computing, and thus they are not suitable for workflow tasks offloading with dependence on mobile edge computing. This paper proposes a multiple workflows offloading strategy based on deep reinforcement learning in mobile edge computing with the goal of minimizing the overall completion time of multiple workflows and the overall energy consumption of multiple user equipments. We evaluate the performance of the proposed strategy by simulation experiments based on real-world parameters. The results show that the proposed strategy performs better than other alternatives in terms of the overall completion time and the overall energy consumption.KeywordsMultiple workflows offloadingMulti-objective optimizationMulti-agent DDPG

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.

The visions of Internet of Things and 5G communications have driven the evolution of mobile computing paradigm, shifting from the centralized mobile cloud computing toward mobile edge computing (MEC). By pushing mobile computing, network control, and storage to the network edges (e.g. base stations and wireless access points), MEC is expected to enable computation‐intensive and latency‐critical applications at the resource‐limited mobile devices. MEC promises dramatic reduction in mobile energy consumption and latency, tackling the key challenges for realizing the 5G visions. The promised gains of MEC have motivated extensive research efforts in both academia and industry. A main thrust of MEC research is to seamlessly integrate the two disciplines of wireless communications and mobile computing, leading to a wide range of new designs ranging from techniques for energy‐efficient computation offloading to network architectures. This article first introduces the basic principles of computation offloading in MEC, and then provides a comprehensive survey of the state‐of‐the‐art MEC research with emphasis on energy‐efficient computation offloading and joint radio‐and‐computational resource management. Last, we introduce recent 5G standardization efforts on MEC as well as typical use scenarios.

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 study, to reduce the energy consumption for the federated learning (FL) participation of mobile devices (MDs), we design a novel joint dataset and incentive management mechanism for FL over mobile edge computing (MEC) systems. We formulate a Stackelberg game to model and analyze the behaviors of FL participants, referred to as MDs, and FL service providers, referred to as MECs. In the proposed game, each MEC is the leader, whereas the MDs are followers. As the leader, to maximize its own revenue by considering the trade-off between the cost of providing incentives and the estimated accuracy attained from an FL operation, each MEC provides full incentives to the MDs for the participation of each FL task, as well as the target accuracy level for each MD. The suggested total incentives are allocated over MDs’ proportion to the amount of dataset applied for local training, which indirectly affects the global accuracy of the FL. Based on the suggested incentives, the MDs determine the amount of dataset used for the local training of each FL task to maximize their own payoffs, which is defined as the energy consumed from FL participation and the expected incentives. We study the economic benefits of the joint dataset and incentive management mechanism by analyzing its hierarchical decision-making scheme as a multi-leader multi-follower Stackelberg game. Using backward induction, we prove the existence and uniqueness of the Nash equilibrium among MDs, and then examine the Stackelberg equilibrium by analyzing the leader game. We also discuss extensions of the proposed mechanism where the MDs are unaware of explicit information of other MD profiles, such as the weights of the revenue as a practical concern, which can be redesigned into the Stackelberg Bayesian game. Finally, we reveal that the Stackelberg equilibrium solution maximizes the utility of all MDs and the MECs.

While mobile edge computing (MEC) holds promise to enhance users' mobile experiences, building a scheduling framework to make full use of MEC capabilities is challenging. When involving quality of service (QoS) in MEC, the problem becomes even harder. In this work, we focus on QoS guaranteed scheduling in MEC with a cloudlet, which is a small cloud center deployed at the wireless access point (AP) to serve nearby mobile devices. There are multiple mobile devices (MDs) and each one is associated with a job to be offloaded to the AP and executed in the cloudlet. Each job is associated with a block of input data, an execution workload, and a QoS requirement, i.e., a time deadline that the job is expected to be completed before it. Our goal is to find an efficient schedule, which involves radio access network (RAN) allocation and job mapping on multiple heterogeneous servers, such that the number of jobs whose deadlines are satisfied is maximized. The problem is proved to be NP-hard. To solve the problem, we propose an extended marriage algorithm (EMA) by adapting the stable marriage game, for job mapping in the cloudlet. Based on this algorithm, we further implement a cooperative game based scheduling method COOPER-SCHED, which also involves RAN allocation. We perform extensive random experiments and compare it with three common heuristics in scheduling literature. The results show that COOPER-SCHED can find better schedules and shows more stability, i.e., suffering less impacts from RAN allocation, than others in MEC.

QoS prediction for service recommendations in mobile edge computing

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.

Due to the long latency of accessing services from cloud datacenters, mobile edge computing (MEC) has been proposed to provide the capability of cloud computing in close proximity to mobile users. Mobile devices can offload computation-intensive applications to MEC servers to achieve better performance and reduce power consumption. Owing to user mobility, workloads of MEC servers would vary severely to cause imbalanced loads and incur hotspot servers. Previous studies of power saving and hotspot management apply mechanisms for cloud datacenters to MEC servers, but these studies may not be suitable for MEC servers due to the limited computation and communication capacity. We are motivated to propose a scheme, complementary offloading for hotspot mitigation (COHM), for MEC. COHM consists of mechanisms for MEC servers with different states, namely busy, idle, and available. These mechanisms jointly consider the computation tasks for MEC servers with different states to mitigate hotspots. The simulation results show that COHM can decrease the number of hotspot servers without degrading the quality of service for applications.

With the popularity of mobile devices, data transmission between mobile devices and cloud datacenters not only introduces tremendous data computation and network traffic but also causes high transmission delay. Mobile edge computing (MEC) is proposed to improve Quality of Service (QoS) for mobile applications in 5G networks. However, the data consistency for mobile applications still relies on replication schemes. Frequent updates may thus result in high communication over-head and long transmission latency. In this paper, we design an adaptive replication scheme for real-time mobile applications by considering the number of failed requests and the read/write request ratios. Our scheme adaptively allocates replicas to share loads among MEC servers. It can avoid overloading MEC servers and shorten latency. The simulation results show that our scheme can reduce the response time of replication requests to increase QoS performance.

With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.

Prompted by the remarkable progress in mobile edge computing, there is an increasing need for executing complex applications on the edge server. These complex applications can be described using workflows which is a set of interdependent tasks. Existing studies focus on offloading the workflow tasks to the nearby edge servers in order to achieve high quality of service, However, the original edge server with the offloaded workflow tasks may be far away from the users due to the high mobility of users in mobile edge computing (MEC). Therefore, it is a key challenge to make good decisions on where and when the workflow tasks are migrated in the light of user’s mobility. In this paper, we propose a workflow task migration algorithm based on deep reinforcement learning with the goal of optimizing the cost of workflow migration under delay-guarantee constraints. The proposed algorithm firstly utilizes the Recurrent Neural Network (RNN) based model to predict the mobile location of users, and then applies a dynamic programming algorithm to calculate the completion time of workflow. Finally, an improved Deep Q Network (DQN) algorithm is adopted to find the optimal workflow migration strategy. In order to assess the performance of the proposed algorithm, extensive simulations are carried out for four well-known scientific workflows. The experimental results show that the proposed algorithm can meet threshold at lower costs in comparison with the state-of-the-art approaches applied to similar problems.KeywordsMobile edge computingWorkflow migrationMobilityDeep reinforcement learning

With the rapid development of the mobile Internet and the Internet of Things, there is an increasing need to execute compute-intensive tasks on mobile devices. But, mobile devices have limited resources, which makes it difficult to provide fast response times. In order to overcome such difficulties, a new computing paradigm called mobile edge computing (MEC) has been proposed to extend cloud-computing capabilities to the edge of the network. In MEC systems, mobile devices can offload compute-intensive tasks to resource-rich edge servers for execution, which effectively reduces the task processing latency. Nowadays, there are many researches on independent task offloading in MEC. However, many applications are composed of dependent tasks in real-world scenarios. Therefore, dependent task offloading has become a hot topic in MEC. In this paper, we study multiple workflows offloading in MEC. A model-free algorithm based on deep reinforcement learning is proposed to learn the optimal multiple workflows offloading strategy so as to minimize the number of workflows whose deadlines are not satisfied. Simulation results show that the proposed multiple workflows offloading strategy performs better than the state-ofthe-art approaches applied to similar problems.

5G operators will utilize mobile edge computing (MEC) to shorten transmitting latency and fulfill the requirements of increasing mobile applications for high bandwidth and ultra-low latency. A mobile online system (MOS) supports mobile online applications to trigger events in the mobile devices, refresh data, and maintain data consistency in the clouds. As users run mobile online applications, a MOS may generate massive updates for replicas in the clouds and bring more network traffic for data consistency. This also causes additional traffic overhead in the Radio Access Network (RAN) and degrades Quality of Service (QoS). This paper presents an adaptive replication scheme for MEC. Our scheme dynamically allocates the number of replicas based on the read/write frequency in each MEC. The simulation results show that our scheme can efficiently reduce the average response time and improve QoS.