关键词： Servers   Quality of service   Power demand   Delays   Planning   Task analysis   Minimization   Mobile edge computing   quality of service   power efficiency   resource optimization   integer gaining and sharing knowledge (IGSK)   workload-based threshold control  

摘要： Mobile edge computing (MEC) allows mobile devices to offload computationally intensive tasks to nearby base stations equipped with computational resources. MEC also provides a solution for supporting low latency responses. However, MEC increases the system power consumption by adding extra computational units. It is important to carefully plan computational resources to achieve a balance between quality of service (QoS) and power consumption. Moreover, the MEC system control should shut down unused resources when traffic rates fall to further decrease the power consumption. In this letter, we investigate a workload-based core power state control for cooperative MEC systems and a heterogeneous resource planning method. We also formulate a joint resource planning and workload-threshold optimization problem for power consumption minimization. The problem is solved using a hybrid method using metaheuristic and heuristic algorithms. The problem is first solved for an equivalent uniform system using integer gaining and sharing knowledge (IGSK). The resources are then redistributed over the base stations using a heuristic that considers the effect of power state control on core distribution. The results show that the iterative algorithm can accomplish a satisfactory QoS with the same number of resources obtained by other approaches or less. Additionally, threshold control reduced the system power consumption by 19.6%.

关键词： degradable system   microservice   mobile edge computing   performability   resource allocation  

摘要： This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud-native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resource-constrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability-aware resource allocation (PARA), a PARA on the edges for cloud-native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified performance-reliability measure for evaluating such degradable systems. To maximize the long-term overall performability, we model the resource optimization problem and then develop an online greedy heuristic algorithm. The algorithm predicts services' resource demands and then adapts the online allocation. The experimental results show that PARA reduces the reallocation overhead by 47.7%-53.6%, and improves the long-term overall performability by 23.14%-43.25% of existing state-of-the-art works.

关键词： caching and forwarding optimisation   mobile edge computing   wireless distributed network   wireless network optimisation  

摘要： For caching and forwarding problems in mobile networks, a new method of caching and forwarding in mobile networks based on boundary computing is proposed. Using Problem Optimisation to Transform Joint Optimal Problems into Linear Programming Problems and online algorithms, such as real-time caching and forwarding, are designed. The network ranking leader, consumption amount, and network signal reception of the three algorithms are compared. The study found that in terms of network sort captain, there are significant differences between the CPLEX algorithm, the cell clustering for spatial transcriptomics (CCST) algorithm, and edge computing methods. The CCST algorithm and edge computing have little difference in queue length with small queue length and low latency. However, the CPLEX algorithm has a long queue, resulting in high latency. In terms of amount spent, at the beginning, the edge computing method requires more costly than the linear programming method. But as the beta value increases, so does its amount spent. Although the amount spent of the CPLEX algorithm is low, it is not accepted by users in terms of network delay. In terms of the network signal reception, the edge computing method is 18.3% higher than the linear programming method, and the network signal reception of the CPLEX algorithm is low. Under the premise of higher network signal reception and smaller queue length, higher delivery costs can be achieved and can be adjusted by the size. It can make the consumption amount and the network congestion level to be satisfied.

关键词： Mobile edge computing   Energy efficient   Computation offloading   Polynomial time approximation scheme  

摘要： Mobile edge computing (MEC) allows a mobile device to offload tasks to the nearby server for remote execution to enhance the performance of user equipment. A major challenge of MEC is to design an efficient algorithm for task allocation. In contrast to previous work on MEC, which mainly focuses on single-task allocation for a mobile device with only one task to be completed, this paper considers a mobile device with multiple tasks or an application with multiple tasks. This assumption does not hold in real settings because a mobile device may have multiple tasks waiting to execute. We address the problem of task allocation with minimum total energy consumption considering multi-task settings in MEC, in which a mobile device has one or more tasks. We consider the binary computation offloading mode and formulate multi-task allocation as an integer programming problem that is strongly NP-hard. We propose an approximation algorithm and show it is a polynomial-time approximation scheme that saves the maximum energy. Therefore, our proposed algorithm achieves a tradeoff between optimality loss and time complexity. We analyze the performance of the proposed algorithm by performing extensive experiments. The results of the experiments demonstrate that our proposed approximation algorithm is capable of finding near-optimal solutions, and achieves a good balance of speed and quality.

关键词： Mobile edge computing   Energy balance   Greedy algorithm   Blockchain   Task offloading  

摘要： Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

关键词： Mobile edge computing  

摘要： Service migration is one of the key topics of Internet of Vehicles (IoV). MEC (mobile edge computing), which is carried on the roadside unit (RSU), could serve as a service provider and provide a V2I (vehicle to infrastructure) cooperation service. To solve the high migration rate caused by the vehicle's high mobility feature, MSCN (mobile secondary computing node) framework is defined. To study service migration features under the MSCN framework further, in this paper, road vehicle's motion features, e.g., driving intention and regional traffic condition, are introduced to construct the Markov decision model, which is used to explain the service migration decision procedure, while DIMDP (driving intention-based MDP) is defined. Corresponding cost functions are defined, while the optimal object is given. A two-way road scenario is selected as a typical scenario. NS3 platform is employed to fulfill the simulation process. Simulation results show that the proposed service migration strategy performs well and is intensive to vehicle density change.

关键词： Mobile edge computing   Deep reinforcement learning   Computation offloading   Delay   Energy consumption  

摘要： Mobile edge computing (MEC) is an effective computing paradigm for mobile devices in the 5G era to reduce computing delay and energy consumption. However, in a multi-user resource competition environment, the revenue-driven behavior of edge servers will cause some users to increase delays or fail tasks. Considering this situation, we take the success rate of computation offloading as the trust value of the edge server, and build a system model from the user's perspective, taking delay and energy consumption as the multi-objective task of joint optimization. In the optimization goal, we consider three factors: offloading delay, energy consumption, and queuing delay. Simultaneously minimizing energy consumption and delay is a contradiction problem. Therefore, we solve the problem based on the principle of reducing energy consumption as much as possible when the offload success rate (decreasing delay) is prioritized. Further, we build the problem as a Markov decision problem (MDP) with multi-factor reward value, and treat the trust value as a state of the system. Finally, we use an extended deep deterministic policy gradient (DDPG) algorithm (a DDPG algorithm with multi-objective reward) to work around this problem. Experimental results show that our proposed scheme can better reduce the delay and energy consumption in computation offloading of mobile users (MUs) significantly better than the baseline schemes. The advantages of our proposed scheme are more obvious in an environment where computing resources are tight.

关键词： Mobile edge computing  

摘要： Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy. © 2022, CC BY.

关键词： Mobile edge computing  

摘要： Facing a vast amount of connections, huge performance demands, and the need for reliable connectivity, the sixth generation of communication networks (6G) is envisioned to implement disruptive technologies that jointly spur connectivity, performance, and reliability. In this context, this paper proposes, and evaluates the benefit of, a hybrid central cloud (CC) computing and mobile edge computing (MEC) platform, especially introduced to balance the network resources required for joint computation and communication. Consider a hybrid cloud and MEC system, where several power-hungry multi-antenna unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden. While the multi-antenna base stations are connected to the cloud via capacity-limited fronthaul links, the UAVs serve the cell-edge users with limited power and computational capabilities. The paper then considers the problem of maximizing the weighted network sum-rate subject to per-user delay, computational capacity, and power constraints, so as to determine the beamforming vectors and computation allocations. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on `0-norm relaxation, successive convex approximation, and fractional programming, and has the compelling ability to be implemented in a distributed fashion across the multiple UAVs and the CC. The paper results illustrate the numerical prospects of the proposed algorithm for enabling joint communication and computation, and highlight the appreciable improvements of data processing delays and throughputs as compared to conventional system strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile edge computing   Trajectory optimization   Heterogeneous UAV  

摘要： The research on the Internet of Things (IoT) and edge computing, especially Unmanned Aerial Vehicles assisted Mobile Edge Computing (UAV-assisted MEC) attracts more and more interests of researchers. Nowadays, MEC systems with multiple UAVs have great research value, especially for emergency communication scenarios. In this paper, a heterogeneous UAVs assisted MEC system was proposed, and with the goal of minimization the edge side energy consumption, a parallel processing was involved to jointly optimize the communication scheduling, forwarding power, offloading data size, computing frequency and the trajectory of the UAV. The problem is formulated as a Mixed Integer Non-Linear Programming (MINLP), hard to solve. Therefore, we divided the MINLP into two sub-problems by applying the Block Coordinate Descent (BCD) method, and solved it using the Lagrangian Duality (LD) method and Successive Convex Optimization (SCO). The UAV's trajectory was initialized as a circular, and then it was optimized by using the standardized convex solver design algorithm. In addition, a heuristic algorithm was put forward to obtain the optimization solution, while we set other benchmarks and the Monte Carlo (MC) algorithm to justify its validity and rationality. The simulation results showed that our strategy has better performance at minimizing energy consumption compared with other algorithms.