关键词： Cloud Computing   Mobile Edge Computing   Resiliency   Scaling  

摘要： In In edge-cloud environments, mobile nodes face significant challenges due to their mobility and the distributed nature of the environment. The unstable communication links between mobile nodes and the cloud often lead to frequent disruptions in connectivity, posing obstacles to seamless operation and service delivery. Effective energy management strategies are crucial to address these challenges and ensure the long-term viability of mobile nodes. In this paper, we propose an architecture for energy-aware resilience in edge-cloud environments for a standalone mobile node in an edge-cloud environment that can operate seamlessly in the connection disruption from the cloud. Our architecture leverages machine learning-based energy consumption prediction techniques to forecast energy consumption patterns while considering dynamic network conditions. In addition, we propose a threshold-based control policy for autonomous node resilience, enabling mobile nodes to adaptively adjust their operations in response to fluctuating energy levels and network conditions of edge environments. Through proactive energy management strategies, such as workload autoscaling with energy awareness, we aim to minimize energy consumption and maximize node survival time, particularly under constrained conditions. Experimental evaluations demonstrate the efficiency of our proposed approach in extending node longevity and ensuring reliable operation in dynamic and resource-constrained edge-cloud environments. © 2024, Korean Institute of Communications and Information Sciences. All rights reserved.

关键词： Age of information   transmission-computation trade-off   mobile edge computing   service preemption  

摘要： The freshness of information in real-time monitoring systems has received increasing attention, with Age of Information (AoI) emerging as a novel metric for measuring information freshness. In many applications, update packets need to be computed before being delivered to a destination. Mobile edge computing (MEC) is a promising approach for efficiently accomplishing the computing process, where the transmission process and computation process are coupled, jointly affecting freshness. In this paper, we aim to minimize the average peak AoI (PAoI) in an MEC system. We consider the generate-at-will source model and study when to generate a new update in two edge server setups: 1) computing preemption, where the packet in the computing process will be preempted by the newly arrived one, and 2) non-preemption, where the newly arrived packet will wait in the queue until the current one completes computing. We prove that the fixed threshold policy is optimal in a non-preemptive system for arbitrary transmission time and computation time distributions. In a preemptive system, we show that the transmission-aware threshold policy is optimal when the computing time follows an exponential distribution. Our numerical simulation results not only validate the theoretical findings but also demonstrate that: 1) in our problem, preemptive systems are not always superior to non-preemptive systems, even with exponential distribution, and 2) as the ratio of the mean transmission time to the mean computation time increases, the optimal threshold increases in preemptive systems but decreases in non-preemptive systems.

关键词： Ultra-dense heterogeneous network   Mobile edge computing   Wireless resource management   Task offloading   Multi-agent deep deterministic policy gradient  

摘要： With the rapid development of IoT technology, various computation-intensive and latency-sensitive tasks have emerged in large numbers, which impose higher requirements on the processing efficiency of the tasks. In this paper, by fusing mobile edge computing (MEC) and ultra-dense heterogeneous network (UD-HetNet) technologies, we design the scenario of UD-HetNet with MEC to improve the efficiency of computation and communication during task processing. However, how to design a effective task offloading and resource management strategy is still a major challenge in this scenario. Therefore, in this paper, we propose a distributed task offloading and wireless resource management framework that optimizes task offloading, local computation frequency scaling, subchannel allocation, and transmit power regulation strategies to reduce system overhead (weighted sum of delay and energy consumption of task processing) effectively. First, we design a task offloading for multi-base station (BS) collaboration base on priority algorithm and optimize the local computation frequency using convex optimization theory. Following by, we introduce a multi-agent deep deterministic policy gradient (MADDPG) technology to optimize subchannel allocation and transmit power regulation during task offloading to accommodate the dynamic and variable nature of the channel. Finally, user equipment (UE)-edge server (ES), UE-subchannel, and subchannel-power matching are achieved. Simulation results show that our algorithm has significant advantages in balancing the ES load, improving channel utilization, and reducing system overhead.

关键词： Mobile edge computing   multiple edge service providers   task offloading   resource expansion  

摘要： As a potential computing solution for fast-growing mobile and IoT applications, edge computing has been developed rapidly. However, due to the relatively limited resources of each edge node, it is difficult for edge nodes to provide quality-guaranteed services to dynamic and massive computation tasks individually. To address this challenge, this paper proposes a two-stage resource expansion and sharing scheme, named SESAME, to enable resource sharing among the edge nodes within/across multiple edge service providers (ESPs), to improve the overall efficiency of the edge computing system. To facilitate the operation and reduce complexity, the resource management scheme has both long-term and short-term decision periods. During the long-term period, an optimal conservative estimation-based resource expansion and pricing strategy has been designed to ensure the system stability and the interests of ESPs. During the short-term period, a resource-sharing strategy considering the internal and external behaviors of ESPs has been proposed to reduce resource-sharing costs while fully utilizing internal resources. In such a way, the resources from different ESPs can collaborate efficiently. Extensive experiments on real datasets show that our algorithm can effectively reduce ESP costs and improve system stability.

关键词： Mobile edge computing   edge server placement   recursive clustering   workload balancing   network traffic  

摘要： With the rapid advancement of the Internet of Things and 5G networks in smart cities, the inevitable generation of massive amounts of data, commonly known as big data, has introduced increased latency within the traditional cloud computing paradigm. In response to this challenge, Mobile Edge Computing (MEC) has emerged as a viable solution, offloading a portion of mobile device workloads to nearby edge servers equipped with ample computational resources. Despite significant research in MEC systems, optimizing the placement of edge servers in smart cities to enhance network performance has received little attention. In this article, we propose RESP, , a novel Recursive clustering technique for Edge Server Placement in MEC environments. RESP operates based on the median of each cluster determined by the number of base transceiver stations, strategically placing edge servers to achieve workload balance and minimize network traffic between them. Our proposed clustering approach substantially improves load balancing compared to existing methods and demonstrates superior performance in handling traffic dynamics. Through experimental evaluation with real- world data from Shanghai Telecom's base station dataset, our approach outperforms several representative techniques in terms of workload balancing and network traffic optimization. By addressing the ESP problem and introducing an advanced recursive clustering technique, this work makes a substantial contribution to optimizing mobile edge computing networks in smart cities. The proposed algorithm outperforms alternative methodologies, demonstrating a 10% average improvement in optimizing network traffic. Moreover, it achieves a 53% more suitable result in terms of computational load.

关键词： Mobile edge computing   Service caching   Task offloading  

摘要： With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing -intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

关键词： Jamming   Autonomous aerial vehicles   Task analysis   Heuristic algorithms   Optimization   Resource management   Computational modeling   UAV communication   mobile edge computing   anti-jamming   resource management   reinforcement learning  

摘要： In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

关键词： Mobile edge computing   successive convex approximation   unmanned aerial vehicle   resource management   Mobile edge computing   successive convex approximation   unmanned aerial vehicle   resource management  

摘要： Unmanned aerial vehicles (UAVs) have been widely utilized to expand wireless network coverage and provide computation service for Internet-of-Things (IoT) devices in signal-blocked or shadowed environments. In this paper, we propose a novel multi-UAV-enabled mobile edge computing (MEC) architecture in which multiple UAVs provide both communication and computation services for IoT devices that cannot directly access the ground edge clouds. To achieve min-max fairness of energy consumption among UAVs, we minimize the maximal energy consumption among UAVs by jointly optimizing computation offloading decisions, communication and computation resource allocation, UAV positions, and task splitting decisions, while meeting the delay requirement of all tasks. The required optimization is a large-scale mixed-integer non-linear program that is generally intractable. To solve this problem, we propose an efficient iterative algorithm based on the successive convex approximation (SCA). The simulation results show that the proposed scheme outperforms various baseline schemes in processing computation-intensive and latency-critical tasks.

关键词： Convex optimization   Energy efficient   Mobile edge computing   Task offloading   Unmanned aerial vehicles  

摘要： The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

关键词： Satellites   Task analysis   Games   Internet of Vehicles   Heuristic algorithms   Computational modeling   Vehicle dynamics   6G   mobile edge computing   Nash equilibrium   Stackelberg game   Internet of Vehicles  

摘要： With the development of Internet of Vehicles (IOV) technology, minimizing network latency while performing computationally intensive tasks has become a challenge. As the most advanced data communication technology, 6G can significantly reduce the latency. Additionally, utilizing remote satellites for assistance can alleviate the computational pressure. In this paper, an Integrated Satellite-Terrestrial Internet of Vehicles (IST-IOV) environment was established with 6G networks. Firstly, we established the Edge Computation Service Provider (ECSP) to manage task allocation and data flow. Secondly, considering energy consumption, latency, and offloading process, we define utility values for vehicles and ECSP, construct a Stackelberg bi-level dynamic cooperative game. The existence of the unique Nash equilibrium in the game has also been proved by backward induction methods. Finally, based on the interaction between vehicles and ECSP, we proposed a computation offloading algorithm called Dynamic Cooperative Game Theory in IOV (DCGT-IOV). The DCGT-IOV algorithm can find the optimal offloading strategy for vehicles and the optimal pricing strategy for ECSP based on the gradient of the utility function. Simulation results demonstrate that the proposed algorithm achieves higher overall utility in various scenarios compared with other strategies and exhibits good convergence properties.