关键词： Task analysis   Servers   Energy consumption   Face recognition   Computational modeling   Receivers   Time factors   Mobile Edge computing   workflow management   energy consumption   computation offloading   directed acyclic graph  

摘要： With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.

关键词： mobile edge computing   IoT   deep learning   cluster head   K-means   energy efficient algorithm  

摘要： In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.

关键词： Servers   Cloud computing   Costs   Games   Collaboration   Bandwidth   Wireless communication   Computation offloading   cost optimization   mobile edge computing   cloud computing  

摘要： Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

关键词： UAV   Mobile edge computing   User cooperation   Computation offloading   Trajectory design  

摘要： Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can provide flexible compu-tation services to user equipments (UEs). On the one hand, the UAV can be flexibly deployed to provide computation services to UEs in remote areas or where intensive computing is required. On the other hand, the UAV can approach UEs conveniently to enhance the offloading performance. However, when UEs are widely distributed, the UEs will consume high energy to offload tasks to the UAV, or the UAV will need to consume significant energy to fly close to different UEs to ensure reliable computation offloading, which is unfriendly to the energy-constrained UAV. To tackle these issues, in this paper, we propose a cooperative task offloading scheme for the UAV-enabled MEC systems. Specifically, we consider a UAV-enabled MEC system consisting of a UAV serving as a MEC server for multiple near and far UEs, where each UE has a dividable computation task to be computed and can offload a part of the task to the UAV for computing. The task offloading of each far UE is proposed to be assisted by an associated near UE, where each far UE first sends its task to the associated near UE, and then the near UE offloads its own task and the task from the far UE to the UAV. An iterative algorithm based on the block coordinate descent method is proposed to optimize the UAV's trajectory, the computation and communication resources for minimizing the weighted sum energy consumption of the UEs and the UAV. Specifically, the UAV's trajectory is optimized based on the successive convex approximation method, and the computation and communication resources are optimized via the Lagrangian dual method. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that compared to the state-of-the-art algorithms in existing literature, the proposed algorithm achieves much lower energy consumption, especially when the UEs carry more task data.

关键词： mobile edge computing   proactive self-healing   autonomous computing   fault tolerance   cyber security  

摘要： The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry's growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur. This paper aims to identify, evaluate, and synthesize studies related to proactive self-healing approaches in MEC environments. The authors conducted a systematic literature review (SLR) using four well-known digital libraries (IEEE Xplore, Web of Science, ProQuest, and Scopus) and one academic search engine (Google Scholar). The review retrieved 920 papers, and 116 primary studies were selected for in-depth analysis. The SLR results are categorized into edge resource management methods and self-healing methods and approaches in MEC. The paper highlights the challenges and open issues in MEC, such as offloading task decisions, resource allocation, and security issues, such as infrastructure and cyber attacks. Finally, the paper suggests future work based on the SLR findings.

关键词： Multimodal semantic communication   Deep reinforcement learning   Content popularity   Bidirectional caching   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) enables immersive XR with multimodal data by coordinating com-munication, computation, and caching (3C) resources upcoming 6G. The traditional communication constrained by Shannon's theorem cannot accommodate the user demands for ultra-reliability and high throughput. In contrast, the semantic communication improves the quality of service and user experience by exploiting the semantic features. This paper constructs a multi-user MEC structure based on multimodal semantic communication for interactive AR/VR games. We construct a bidirectional caching task model to achieve cache-enhanced computing. To minimize the system cost, including the user latency, energy consumption, and storage size, we propose a content popularity-based DQN (CP-DQN) algorithm to make caching decisions. Then the CP-DQN is extended to the cache-computation coordination optimization algorithm (CCCA) to achieve the 3C resources tradeoff. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of caching hit ratio, computation cost and edge resource utilization.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： The Internet of Vehicles (IoVs) is considered a critical technology for developing future intelligent transportation systems. The next-generation IoVs technology integrates Mobile Edge Computing (MEC) with V2X, 5G/6G, etc. applications, which can significantly improve the quality of vehicular services. With the emergence of 6G communication technologies, large-scale MEC facilities will be intensively deployed. While providing a wide range of computing services for vehicles, it also brings high energy consumption. To realize sustainable and green IoVs, introducing sleep mechanisms for MEC servers is a promising solution. In this paper, we study the dynamic sleep and service association problem for edge servers in MEC-assisted IoVs networks. Energy consumption is saved by switching the operating mode of the edge servers between sleep and active based on satisfying the quality of service. To tackle this problem, we propose the Latency-aware Iterative Self-organized Clustering algorithm (DISC), which enables self-organized cluster splitting and merging with latency awareness. DISC can efficiently determine the optimal number of edge servers to be activated and their locations based on the distribution of vehicular tasks. Sufficient simulation results show that the proposed algorithm can efficiently improve resource utility and reduce energy consumption, and is applicable to different scales of IoVs networks. © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Recently, Mobile Edge Computing (MEC) and service migration policies have shown promising results to improve the user experience and optimize infrastructure resources. In addition, Unmanned Aerial Vehicles (UAVs) appear as a promising solution to provide cloud service in collaboration with traditional MEC scenarios. However, in conjunction with the resources available on users’ devices, contextual information has been ignored by most policies. In this article, we propose a service migration strategy based on contextual information and evaluate the influence of user mobility on migration strategies, called DVSM. Simulation results highlight the superior performance of the DVSM compared to state-of-the-art algorithms and a performance equivalent to the optimal solution when thecollection and analysis of context information are carried out correctly. © 2023, Brazilian Computing Society. All rights reserved.

关键词： Energy consumption   Servers   Cellular networks   Trajectory   Optimization   Interference   Edge computing   Mobile edge computing   service migration   multi-user   Lyapunov optimization   particle swarm optimization  

摘要： Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

关键词： mobile edge computing   video analytics   digital twin   6G   deep reinforcement learning  

摘要： Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital *** this paper,with the aim of developing energy-efficient digital twin in 6G,we investigate real-time video analytics based on cameras mounted on mobile devices with edge *** problem is challenging because 1)mobile devices are with limited battery life and lightweight computation capability,and 2)the captured video frames of mobile devices are continuous changing,which makes the corresponding tasks arrival *** achieve energy-efficient video analytics in digital twin,by taking energy consumption,analytics accuracy,and latency into consideration,we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework,which can utilized digital twin models to achieve joint offloading decision and configuration *** edge nodes help to collect the information on network topology and task *** simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.