Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

© 2019 Binbin Huang et al. With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device's energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications

TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks

With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth, and dynamic networks. In this paper, we address the above challenges in MEC with TPD, that is, temporal and positional computation offloading with dynamic-dependent tasks. In particular, we investigate channel interference and intertask dependency by considering the position and moment of computation offloading simultaneously. We define a novel criterion for assessing the criticality of each task, and we identify the critical path based on a directed acyclic graph of all tasks. Furthermore, we propose an online algorithm for finding the optimal computation offloading strategy with intertask dependency and adjusting the strategy in real-time when facing dynamic tasks. Extensive simulation results show that our algorithm reduces significantly the time to complete all tasks by 30-60% in different scenarios and takes less time to adjust the offloading strategy in dynamic MEC systems

Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing

Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.Comment: 13 figure

Reconfigurable Intelligent Surface Assisted MEC Offloading in NOMA-Enabled IoT Networks

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ ϵ-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes