Multi-user Mobile Edge Computing System Research Articles

Deep reinforcement learning-based low-latency task offloading for mobile-edge computing networks

Wireless Mobile Edge Computing (MEC) is an emerging solution to facilitate users’ computing. However, there are still some drawbacks, such as slow learning speed and high task latency. To overcome these difficulties, a deep reinforcement learning (DRL)-based task offloading (DRTO) framework is proposed, taking into account the time-varying nature of wireless channels and the unpredictability of task arrivals in multi-user MEC systems. Many deep neural networks (DNNs) are also used as scalable solutions to quickly determine the best offloading strategy by combining the perceptual capabilities of deep learning with the decision making capabilities of reinforcement learning (RL). Additionally, the concept of a minimum upper bound on the target Q-value is utilized to address the value overestimation problem that occurs during offload policy updates. This approach aims to minimize the latency of offload decisions by considering different channel environments, variable latency constraints among many users, and limited computational resources. Simulation results show that the algorithm reduces computation time and latency while achieving near-optimal performance compared to traditional Deep Deterministic Policy Gradient (DDPG) and Q-learning optimization methods. In addition, DRTO achieves more than 99.6% computational speedup compared to current almost ideal benchmarking techniques. Thus, the DRTO computational offload approach offers great potential for reducing overall system latency and improving the user experience.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Sequential Offloading for Distributed DNN Computation in Multiuser MEC Systems

This paper studies a sequential task offloading problem for a multiuser mobile edge computing (MEC) system. While most of the existing works consider static one-shot offloading optimization with fixed wireless channel conditions and fixed computational tasks, we consider a dynamic optimization approach, which embraces wireless channel fluctuations and random deep neural network (DNN) task arrivals over an infinite horizon. Specifically, we introduce a local CPU workload queue (WD-QSI) and an MEC server workload queue (MEC-QSI) to model the dynamic workload of DNN tasks at each WD and the MEC server, respectively. The transmit power and the partitioning of the local DNN task at each WD are dynamically determined based on the instantaneous channel conditions (to capture the transmission opportunities) and the instantaneous WD-QSI and MEC-QSI (to capture the dynamic urgency of the tasks) to minimize the average latency of the DNN tasks. The joint optimization can be formulated as an ergodic Markov decision process (MDP), in which the optimality condition is characterized by a centralized Bellman equation. However, the brute force solution of the MDP is not viable due to the curse of dimensionality as well as the requirement for knowledge of the global state information. To overcome these issues, we first decompose the MDP into multiple lower dimensional sub-MDPs, each of which can be associated with a WD or the MEC server. Next, we further develop a parametric online Q-learning algorithm, so that each sub-MDP is solved locally at its associated WD or the MEC server. The proposed solution is completely decentralized in the sense that the transmit power for sequential offloading and the DNN task partitioning can be determined based on the local channel state information (CSI) and the local WD-QSI at the WD only. Additionally, no prior knowledge of the distribution of the DNN task arrivals or the channel statistics will be needed for the MEC server. The proposed solution can achieve the superb performance over various state-of-the-art baselines.

Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture

Mobile Edge Computing (MEC) is a promising computing paradigm in the context of 5G networks, as it enables the migration of workloads from User Equipments (UEs) to nearby MEC servers, thereby providing additional computing resources to UEs. In this paper, we propose a joint optimization approach to offloading decisions and resource allocation in a multi-user and multi-server MEC system, which operates in a time-varying environment. Our objective is to minimize the average task latency and discard rate under the constraints of latency and limited computing resources of the server. While traditional optimization methods have been used to solve computational offloading problems in static environments, these methods are not suitable for time-varying systems. Deep reinforcement learning can be an effective method for solving optimization problems in time-varying environments, as it can be used to adjust strategies in real-time in response to changes in the environment. However, the increased number of UEs in the system leads to a combinatorial increase in the number of possible actions, making it difficult for the algorithm to learn. To address this issue, we propose a multi-branch network based Deep Q Network (DQN) algorithm called Branch Deep Q Network (BDQN), which modifies the action generation network into a multi-branch network structure, each branch generates one-dimensional action. This modification makes the number of network outputs increase linearly. Numerical results show that the BDQN algorithm outperforms other baseline algorithms in terms of average task latency and discard rate.

STAR-RIS-Aided Mobile Edge Computing: Computation Rate Maximization With Binary Amplitude Coefficients

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Energy-Efficient Optimization of Multi-User NOMA-Assisted Cooperative THz-SIMO MEC Systems

The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge computing (MEC). THz communications offer abundant bandwidth for high-data-rate short-distance applications and NOMA-enabled schemes are promising schemes to realize the target spectral efficiencies and low latency requirements in future networks, while MEC would allow distributed processing and data offloading for the emerging applications in these networks. In this paper, an energy-efficient scheme of multi-user NOMA-assisted cooperative THz single-input multiple-output (SIMO) MEC systems is proposed to allow the uplink transmission of offloaded data from the far cell-edge users to the more computing resources in the base station (BS) through the cell-center users. To reinforce the performance of the proposed scheme, two optimization problems are formulated and solved, namely, the first problem minimizes the total users’ energy consumption while the second problem maximizes the total users’ computation energy efficiency (CEE) for the proposed scheme. In both problems, the NOMA user pairing, the BS receive beamforming, the transmission time allocation, and the NOMA transmission power allocation coefficients are optimized, while taking into account the full-offloading requirements of each user as well as the predefined latency constraint of the system. The obtained results reveal new insights into the performance and design of multi-user NOMA-assisted cooperative THz-SIMO MEC systems. Particularly, with relatively high offloading rate demands (several Gbits/user), we show that (i) the proposed scheme can handle such demands while satisfying the predefined latency constraint, and (ii) the full-offloading model can be considered the most effective solution in conserving mobile devices’ resources as compared to the system with the partial-offloading model or the system without offloading.

QoS-aware offloading based on communication-computation resource coordination for 6G edge intelligence

Driven by the demands of diverse artificial intelligence (AI)-enabled application, Mobile Edge Computing (MEC) is considered one of the key technologies for 6G edge intelligence. In this paper, we consider a serial task model and design a quality of service (QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems, which can mitigate the I/O interference brought by resource reuse among virtual machines. Then we construct the system utility measuring QoS based on application latency and user devices' energy consumption. We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference. Simulation results demonstrate the proposed algorithm's significant advantages in terms of task completion time, terminal energy consumption and system resource utilization.

Intelligent Delay-Aware Partial Computing Task Offloading for Multiuser Industrial Internet of Things Through Edge Computing

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile-Edge Computing

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

Resource Allocation in a Relay-Aided Mobile Edge Computing System

Mobile edge computing (MEC) provides wireless devices (WDs) more computing capability and lower latency by allowing them to offload their computation tasks to a nearby more powerful server. Furthermore, adopting the relaying technique can effectively improve the offloading performance, especially when the wireless channel conditions between WD and MEC server are poor. In this article, we consider a multiuser relay-aided MEC system targeting at minimizing the energy consumption. The relay can either execute the computation task by itself or offload the task to the MEC server. Under the partial computation offloading mode, an energy minimization problem is investigated by jointly optimizing transmit power, offloading time duration, and central processing unit (CPU) frequencies. To solve the nonconvex optimization problem, an iterative algorithm based on successive convex approximation (SCA) is developed. Furthermore, the closed-form expressions for the optimal transmission powers and the CPU frequencies are derived. The simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computation offloading (MECO) has recently emerged as a promising method to support computation-intensive and latency-sensitive applications, significantly saving the battery energy of smart mobile devices (SMDs). However, on the one hand, the energy consumption depends on both the SMD and the MEC server, which makes it necessary to consider these two entities to achieve energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) system, efficient optimization algorithms based on binary offloading have received significant attention, while efficient algorithms for partial offloading under time-varying channels are seldom investigated. In this paper, we propose an energy-efficient algorithm based on deep reinforcement learning to optimize the overall energy cost in a real-time multi-user MEC system. We decompose the energy minimization problem into two sub-problems, where a deep neural network learns the optimal mapping between wireless channels and offloading ratios, and a closed-form solution for the optimal local frequency and a convex optimization algorithm are used to solve the resource allocation sub-problem. Finally, the extensive experiments demonstrate the effectiveness of our proposed algorithm in reducing the total energy consumption of the MECO system against several offloading schemes and achieving low processing latency fit to the time-varying wireless channels.

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

Deep reinforcement learning based joint partial computation offloading and resource allocation in mobility-aware MEC system

Mobile edge computing (MEC) emerges as a paradigm to free mobile devices (MDs) from increasingly dense computing workloads in 6G networks. The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers. Renewable energy harvested by energy harvesting equipments (EHQs) is considered as a promising power supply for users to process and offload tasks. In this paper, we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy. We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system. We propose an effective joint partial computation offloading and resource allocation (CORA) algorithm which is based on deep reinforcement learning (DRL) to obtain the optimal scheduling without prior knowledge of task arrival, renewable energy arrival as well as channel condition. The simulation results verify the efficiency of the proposed algorithm, which undoubtedly minimizes the cost of MDs compared with other benchmarks.

NOMA-Enabled Computation and Communication Resource Trading for a Multi-User MEC System

In this article, we establish a novel framework for a multi-user mobile edge computing (MEC) network in which a set of users with high downlink rate demands and a set of users with intensive computation tasks can collaborate to achieve a mutually-beneficial scenario such that completion time of the tasks is reduced and the base station (BS) can send more information at a higher rate to the downlink users. Specifically, by leveraging non-orthogonal multiple access (NOMA) for uplink and downlink traffic, the user with the computation task can offload shares of the computation task to the edge cloud and the downlink user. At the same time, this user forwards the information it receives from the BS to the downlink user. In this set up, we jointly optimize the communication resources, computational resources at the edge cloud and user devices, pairings among the two sets of users, the shares of computation tasks, and relay bits to minimize the total task completion time while satisfying downlink users’ incentive requirements. For a network with a single computation demanding user and a single downlink user, the optimal solution to the problem is provided. For a network with multiple users, the problem is non-convex and computationally challenging. Hence, we propose an efficient, low complexity algorithm that utilizes the bottleneck matching algorithm, convex optimization, and the block coordinate descent scheme to obtain a locally-optimal solution. Simulation results demonstrate that, as compared with the state-of-the-art, the total task completion time is greatly reduced (32%–51%), and a large computational energy savings at the edge cloud (38%–55%) is achieved. Simultaneously, the downlink users’ rates improve compared to the orthogonal transmission.

UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting

This article studies a mobile edge computing (MEC) with one edge node (EN), where multiple unmanned aerial vehicles (UAVs) act as users which have some heavy tasks. As the users generally have limitations in both calculating and power supply, the EN can help calculate the tasks and meanwhile supply the power to the users through energy harvesting. We optimize the system by proposing a joint strategy to unpacking and energy harvesting. Specifically, a deep reinforcement learning (DRL) algorithm is implemented to provide a solution to the unpacking, while several analytical solutions are given to the power allocation of energy harvesting among multiple users. In particular, criterion I is the equivalent power allocation, criterion II is designed through equal data rate, while criterion III is based on the equivalent transmission delay. We finally give some results to verify the joint strategy for the UAV-aided multiuser MEC system with energy harvesting.

Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing

Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G architecture, which can empower user equipments (UEs) with computation and energy resources offered by migrating workloads from UEs to the nearby MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly focus on facilitating the performance in the quasistatic system, and seldomly consider time-varying system conditions in the time domain. In this article, we investigate the joint optimization of computation offloading and resource allocation in a dynamic multiuser MEC system. Our objective is to minimize the energy consumption of the entire MEC system, by considering the delay constraint as well as the uncertain resource requirements of heterogeneous computation tasks. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem, and propose a value iteration-based reinforcement learning (RL) method, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Learning, to determine the joint policy of computation offloading and resource allocation. To avoid the curse of dimensionality, we further propose a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> network (DDQN)-based method, which can efficiently approximate the value function of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning. The simulation results demonstrate that the proposed methods significantly outperform other baseline methods in different scenarios, except the exhaustion method. Especially, the proposed DDQN-based method achieves very close performance with the exhaustion method, and can significantly reduce the average of 20%, 35%, and 53% energy consumption compared with offloading decision, local first method, and offloading first method, respectively, when the number of UEs is 5.

Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems

In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we propose joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we employ a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task. Despite the non-convexity of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms based on successive convex approximation and second-order cone techniques. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, and yields significant power savings compared to three baseline schemes. Moreover, our simulation results show that the proposed suboptimal algorithms offer different trade-offs between performance and complexity and attain a close-to-optimal performance at comparatively low complexity.

Performance Analysis of Mobile Edge Computing in Resource-Limited CSMA/CA Networks

Mobile Edge Computing (MEC) has proved its great potential for supporting the delay-stringent services in wireless networks. We notice that offloading performance of the MEC system is not only determined by the PHY-layer transmission rate, but also closely related to the performance of channel access and computation at the server. To this end, in this correspondence, we investigate a multiuser MEC system, where each user offloads packet to a single AP equipped with an MEC server by preempting the channel according to carrier sense multiple access with collision avoidance (CSMA/CA) protocol. We derive the effective offloading rate and delay by considering transmission outage caused by the poor channel condition, the MAC-layer channel competition, and the packet overflow due to the limited service capability of the server, where the service process is modelled as an M/M/1/B queue. The theoretical analysis is verified by extensive simulations, which has shown that the competition intensity indicated by the number of users and the packet length, as well as the processing capability of the MECserver including the buffer size and the computation speed, has profound impact on the offloading performance.

Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems

Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at the network edge. In this paper, we consider the AI service placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service placement solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.

Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.