关键词： Deep Q learning   Mobile Edge computing   SDN   Edge caching   Service migration  

摘要： With the increase of mobile terminal equipment and network mass data, users have higher requirements for delay and service quality. To reduce user access latency and more effectively cache diverse content in the edge network, a low-latency edge caching method is proposed. The cache model based on base station cooperation is established and the delay in different transmission modes is considered. Finally, the problem of minimizing latency is transformed into a problem of maximizing cache reward, and a greedy algorithm based on the original dual interior point is used to obtain the strategy of the original problem. Meanwhile, in order to improve service quality and balance communication overhead and migration overhead, a migration method based on balanced communication overhead and migration overhead is proposed. The model that balances communication overhead and migration overhead is established, and the reinforcement learning method is used to obtain a migration scheme that maximizes accumulated revenue. Comparison results show that our caching method can enhance the cache reward and reduce delay. Meanwhile, the migration algorithm can increase service migration revenue and reduce communication overhead.

关键词： NOMA   Delays   Task analysis   Energy consumption   Silicon carbide   Decoding   Optimization   Mobile edge computing   task offloading   energy consumption   delay   multiple access  

摘要： The wireless networks beyond the fifth generation (5G) are envisioned to be the platform that will support a vast amount of diversified data-driven applications with stringent requirements in terms of computational accuracy, delay, and energy efficiency. The fulfillment of this objective can be achieved by the convergence of communication and computing networks, enabling the exploitation of edge computing resources and the joint orchestration of the corresponding resources. Mobile edge computing (MEC), which refers to the use of edge serves for offloading tasks from mobile devices, is a particularly promising approach to provide the required computational performance for emerging internet-of-things applications, such as the smart grids, smart industry, healthcare, and smart farming. In this work, we propose the use of an advanced multiple access technique and its joint design with adaptive task offloading, in order to reduce delay and energy consumption. More specifically, the use of generalized hybrid orthogonal/non-orthogonal multiple access (OMA/NOMA) for MEC is introduced, which is theoretically superior to other alternatives from the existing literature. In more detail, the proposed scheme is based on the joint utilization of dynamic user scheduling among OMA/NOMA phases and variable decoding order during the successive interference cancellation in NOMA phase. Also, the system's orchestration is optimized for both full and partial task offloading. Specifically, in full offloading scenario, the user scheduling, time allocation, and power control are jointly optimized. Regarding partial offloading, the computational resources, i.e., the clock speed of the local processors and the number of offloaded bits, are jointly optimized with the communication resources, taking into account the constraint of the energy that is consumed for both local processing and task offloading, which is particularly challenging due to the non-convex nature of the corresponding optimizat

关键词： Reliability   Wireless communication   Videos   Edge computing   Delays   Bandwidth   Task analysis   Virtual reality   sub-6 GHz and mmWave heterogeneous networks   reliability enhancement   mobile edge computing   stochastic geometry  

摘要： The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz networks. In this paper, we propose a dual-connectivity sub-6 GHz and mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of sub-6 GHz links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that sub-6 GHz links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, sub-6 GHz and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical simulations demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at sub-6 GHz BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.

关键词： Mobile edge computing   Stackelberg game   Computing offloading   Utility maximization  

摘要： We propose a new approach for the organic integration of edge cloud offloading decision and Stackelberg game pricing to address the problem that the current Stackelberg games all allocate edge cloud computing resources equally and ignore the difference of different users' demand for computing resources. Firstly, the Stackelberg game theory is used to establish a model of the optimal amount of data to be offloaded by users and the optimal number of computing resource blocks to be purchased, which converts the multivariate offloading decision problem of users into a univariate optimization problem, simplifies the offloading decision problem of users, and proves the existence of Nash equilibrium. Secondly, the KKT condition is applied to realize the offloading decision of users to purchase the optimal computing resource blocks. The upper and lower bounds of edge cloud pricing are established. Finally, a dynamic programming-based offloading (DPPO) algorithm for edge cloud pricing is proposed to achieve the optimal pricing of edge cloud utility and maximize each user's own utility. The simulation results show that the proposed method not only achieves the equilibrium of edge cloud utility and user utility, but also has good convergence and scalability. The DPPO algorithm yields better results than with different pricing and offloading strategies.

关键词： Delays   Servers   Energy consumption   Costs   Optimization   Array signal processing   Task analysis   Hybrid-dependency   mobile edge computing   parallel offloading and computation  

摘要： This letter proposes an effective dependency-aware subtask offloading and computation scheme in mobile edge computing (MEC) enabled networks, where we construct sequential execution windows of the MEC server by incorporating the hybrid dependencies among subtasks of an application, and the subtasks within the same subtask execution window could be offloaded and computed in parallel. By doing so, the completion delay and energy consumption of the application can be reduced. To make the proposed scheme achieve the minimum completion delay and energy consumption, we further jointly optimize the transmission rate and start execution time of subtasks within each execution window. The formulated problem is non-convex and difficult to solve. To make it tractable, we design a bisection search and successive convex approximation based iterative algorithm. Simulation results validate that compared with other existing schemes, our proposed scheme could reduce the summation of the completion delay and energy consumption by 10.36%.

关键词： Delays   Interference   Queueing analysis   Servers   Signal to noise ratio   Wireless communication   NOMA   Mobile edge computing   delay   Poisson cluster process   stochastic network calculus   orthogonal frequency division multiple access  

摘要： Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers' locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers' and users' spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

关键词： Enterprise cloud   Mobile edge computing   Delay tolerance   Data congestion avoidance   Ant colony algorithm   Algorithm design  

摘要： Traditional large-scale enterprise cloud system has the problem of large delay. Therefore, a delay tolerant data congestion avoidance algorithm based on modular computing is designed. The resource allocation rules of rbfr strategy in traditional enterprise cloud are improved to change the traditional interactive overall mode and make it *** modular thinking to construct the data congestion control algorithm, mainly in the control layer to achieve the model, through the introduction of ant colony algorithm in modularization, the design of delay tolerant data congestion avoidance algorithm is realized. In order to verify the algorithm's congestion avoidance effect, comparative experiments are designed. Experimental results show that the packet loss rate of large enterprise cloud system under this algorithm is lower than that of traditional delay tolerant data congestion avoidance algorithm.

关键词： Mobile edge computing   computation offloading   latency   genetic algorithm   synchronous data flow graph  

摘要： With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered - with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

关键词： Eavesdropping   Task analysis   Jamming   Computational modeling   Surveillance   Receivers   Mobile handsets   Legitimate surveillance   proactive eavesdropping   mobile edge computing   computation offloading   suspicious communications  

摘要： In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better.

关键词： Task analysis   Wireless communication   Resource management   Optimization   Computational modeling   Central Processing Unit   Servers   Binary offloading   mobile edge computing   partial offloading   resource allocation   wireless power transfer  

摘要： This paper studies a joint design of resource allocation and task offloading in a wireless powered mobile edge computing network involving different types of computation tasks. To deal with diverse computation tasks, we explore a mixed-offloading paradigm to support the coexistence of partial and binary offloading modes. Specifically, devices harvest energy from an access point (AP) via wireless power transfer (WPT) and utilize the harvested energy to execute their computation tasks using partial or binary offloading. Based on a practical non-linear energy harvesting model, a residual energy maximization problem is formulated by jointly optimizing the transmit power of the AP, the offloading power of devices, the time allocation on WPT and task offloading, and the task partitions and the binary offloading decisions of devices, which turn out to be a non-convex mixed-integer non-linear programming problem. Thus, we develop an efficient dual-layer optimization algorithm by decomposing the optimization problem into an inner and outer layer structure that aims to obtain resource allocation and offloading decisions. Simulation results show that our proposed scheme achieves residual energy gains compared to existing schemes.