Multi-user Computation Offloading Research Articles

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Joint Optimization of Multiuser Computation Offloading and Wireless-Caching Resource Allocation With Linearly Related Requests in Vehicular Edge Computing System

Joint Optimization of Multiuser Computation Offloading and Wireless-Caching Resource Allocation With Linearly Related Requests in Vehicular Edge Computing System

Dynamic Computation Offloading and Server Deployment for UAV-Enabled Multi-Access Edge Computing

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Energy-Aware Offloading and Power Optimization in Full-Duplex Mobile Edge Computing-Enabled Cellular IoT Networks

The incorporation of full-duplex (FD) communication into task offloading offers significant advantages for mobile edge computing (MEC). In this article, we investigate joint optimization of multiuser computation offloading, transmitting power, time slot, and computation resources in FD MEC-aided cellular Internet of Things (IoT) networks. For the sake of minimizing the energy consumption of the IoT nodes, we develop mixed-integer nonlinear programming with coupling constraints. To handle the coupling constraints, we derive the decoupling conditions and simplify the formulated problem into the decoupling and coupling cases. For the decoupling case, we convert the original problem equivalently into several independent subproblems. Then, for each subproblem, the transmitting power and computation offloading are optimized efficiently by the optimal condition of the objective function. After that, we derive the optimal time slot allocation by Karush–Kuhn–Tucker conditions. For the coupling case, we formulate the feasibility problem of the obtained results from the decoupling case as a two-level knapsack problem. Based on that, we develop an efficient algorithm to modify the solution of decoupling case so that its feasibility is guaranteed for the coupling constraints. Extensive simulations indicate that our proposed algorithm can effectively reduce the total energy consumption of the IoT nodes and fully improve the efficiency of MEC with respect to existing algorithms.

Random-Access-Based Multiuser Computation Offloading for Devices in IoT Applications

In various Internet of Things (IoT) applications, a number of devices and sensors are used to collect data sets. As devices become more capable and smarter, they can not only collect data sets but also process them locally. However, since most devices would be limited in terms of computing power and energy, they can take advantage of offloading so that their tasks can be carried out at mobile-edge computing (MEC) servers. In this article, we discuss computation offloading for devices in IoT applications. In particular, we consider users or devices with sporadic tasks, where optimizing resource allocation between offloading devices and coordinating for multiuser offloading becomes inefficient. Thus, we propose a two-stage offloading approach that is friendly to devices with sporadic tasks as it employs multichannel random access for offloading requests with low signaling overhead. The stability of the two-stage offloading approach is considered with methods to stabilize the system. We also analyze the latency outage probability as a performance index from a device perspective.

Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence-optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.

Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices

Due to the limited computing resources and energy of IoT devices, complex computing tasks are offloaded to sufficient computing servers, such as Cloud Center. However, offloading may increase the latency and congestion of the IoT network. Mobile Edge Computing (MEC) is a promising approach, which can decrease the delay and energy consumption of IoT devices significantly. In this paper, we investigate the problem of multi-user computation offloading under dynamic environment. Considering the channel interference when multiple IoT devices offload computing tasks via wireless channels at the same time, we formulate the computation offloading as an evolutionary game model. We use the replicator dynamics to analyze the evolutionary process of IoT devices and prove that multi-user computation offloading exists unique Evolutionary Stability Strategy (ESS). Finally, we design an evolutionary game algorithm based on reinforcement learning in practical application scenarios. Experiments can verify the convergence and performance of the proposed algorithm in multi-user scenarios.

A Multi-User Mobile Computation Offloading and Transmission Scheduling Mechanism for Delay-Sensitive Applications

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme , a transmission scheduling discipline , and a pricing rule . A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

Optimal Multi-User Computation Offloading Strategy for Wireless Powered Sensor Networks

Wireless sensors network (WSN) is widely used for environmental monitoring, surveillance, healthcare, and security services. One of the most critical challenges that WSN facing is the heavy computation requirements and limited energy storage. The recent development of radio frequency-based (RF) wireless power transfer (WPT) and mobile computation offloading to cloud provide a promising solution to overcome these shortcoming. In this paper, we consider a multi-user Mobile/Multi-Access edge computing (MEC) aided WSN powered by the WPT, where BSs are equipped with multiple antennas jointly service the whole sensors, the sensor node is powered by the WPT only. An optimal multi-user computation offloading strategy is proposed, where sensor nodes choose to operate in either the offloading or the local computing. In addition, perfect channel state information (CSI) and imperfect CSI are taken into account. We study the optimization of computation task offloading in MEC aided WSN under the conditions of perfect CSI and imperfect CSI. In particular, the optimal dynamic time division duplex (D-TDD) factor is investigated. Numerical results confirm the advantages of the proposed computation offloading strategy over the conventional local computation design in handing computationally heavy tasks.

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

This paper studies a multiuser mobile edge computing (MEC) system in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancelation (SIC) to efficiently decode all users’ offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex optimization problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB-based solution, and the convex relaxation-based solution achieves a suboptimal performance but with lower implementation complexity.

A quick-response framework for multi-user computation offloading in mobile cloud computing

The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.