Task Offloading In Mobile Edge Computing Research Articles

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multi-agent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

Computing Offloading Strategy in Mobile Edge Computing Environment: A Comparison between Adopted Frameworks, Challenges, and Future Directions

With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work.

Privacy-Preserving Task Offloading Strategies in MEC

In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

Efficient Resource Allocation for Security-Aware Task Offloading in MEC System Using DVS

With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.

Workload Balancing in Mobile Edge Computing for Internet of Things: A Population Game Approach

Mobile edge computing (MEC) is an emerging paradigm that provides radio access networks with augmented resources to meet the requirements of Internet of Things (IoT) services. MEC allows IoT devices to offload delay sensitive and computation intensive tasks to edge clouds deployed at base stations (BSs). Offloading tasks to edge clouds can alleviate the computing and battery limitations of IoT devices. However, task offloading in MEC for IoT may face serious transmission latency and computation latency problems with massive number of IoT devices. Moreover, some edge clouds can be overloaded due to the spatially inhomogeneous distributions of IoT tasks. To solve these problems, we investigate the workload balancing problems to minimize the transmission latency and computation latency in task offloading process while considering the limited bandwidth resources of BSs and computation resources in edge clouds. We formulate the workload balancing problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamics and show that the game always achieves a Nash equilibrium (NE). We further propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show that our proposed workload balancing algorithms can achieve better performance than existing solutions.

Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.

NOSCM: A Novel Offloading Strategy for NOMA-Enabled Hierarchical Small Cell Mobile-Edge Computing

Mobile-edge computing (MEC) is considered as a promising technology in 5G, as it can solve the contradiction between the explosive growth of computation-intensive tasks and the limited computation power and battery life of local devices. However, in the 5G environment, most of the existing studies on task offloading in MEC have either failed to study the compatible multiple access technologies or have not considered the hierarchical relationship between small cell base station (SBS) and macro base station (MBS). Therefore, to explore the MEC offloading problem under the unique 5G architecture is of great significance at present. In light of this, we study the task offloading strategy in the nonorthogonal multiple access (NOMA)-enabled small cell MEC network. Specifically, we first describe a noval small cell MEC architecture in which MBS and SBS are both deployed with edge servers and there is a hierarchical relationship between the two. Based on this architecture, we have established the communication model and computation model, respectively. Then, we formulate the energy and delay weighted sum minimization problem, which aims at minimizing the total cost of task offloading under different requirements and takes into account the constraints of computation capabilities. To solve the problem, we develop a hybrid genetic hill climbing (HGHC) algorithm that can quickly find the optimal solution. Moreover, we perform a lot of simulation experiments to evaluate the performance of our algorithm under different parameters. The experimental results show that our algorithm can converge within about 20 iterations, which is superior to traditional heuristic algorithms.

Energy-Aware Task Offloading in the Internet of Things

With the increasing demands of big data applications in the Internet of Things (IoT), various applications have emerged to provide mobile services for IoT devices. However, due to the increasing number of IoT devices, transmission delay, energy consumption, and heavy load become new challenges for offloading tasks. Therefore, this article introduces a novel energy-aware task offloading scheme in IoT to determine the optimal offloading strategy. First, the architecture of mobile edge computing (MEC) in IoT is investigated. Second, the challenges of task offloading in MEC for IoT are introduced. Third, the framework of task offloading for MEC is proposed, and the optimal offloading strategy for computing task is achieved. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of task offloading compared with the conventional scheme.

Efficient Multitask Scheduling for Completion Time Minimization in UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) can alleviate the computing resource shortage problem of mobile user equipment (UEs). However, due to long communication distance or the obstruction of big obstacles, the direct communication link may not exist between a UE and a MEC node. It thus hinders the task offloading in MEC. Unmanned aerial vehicles (UAVs) have high degree of mobility and can carry lightweight computation and storage modules. This paper presents a UAV-assisted MEC method, in which the UAV can relay the task-input data of a UE to the MEC node and can also utilize the airborne computation and storage resource to shorten the execution time of the offloaded tasks. Considering the strict order dependency among multiple offloaded tasks, this paper optimizes the task scheduling and the UAV flight path in a joint manner. A heuristic algorithm based on particle swarm optimization (PSO) is also developed to find the optimal solution. The simulation results show that the proposed multitask scheduling method can always find the best tradeoff between the UAV’s position and the wireless channel condition. In comparison to the other three baseline scheduling methods, the proposed method can use the minimum execution time to complete all the offloaded tasks.

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.