关键词： Mobile edge computing   task offloading   service caching   dependency   approximation  

摘要： In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1). Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 21-47 percent compared with other alternatives.

关键词： e-commerce   customer behavior   Pareto/NBD model   SVR model   ARIMA model   mobile edge computing  

摘要： Mobile edge computing is trending nowadays for its computation efficiency and *** rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the *** paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine *** general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources ***,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

关键词： Servers   Handover   Base stations   Wireless communication   Edge computing   Task analysis   Delays   Mobile edge computing   deep reinforcement learning   mobility management   ultra-dense edge computing  

摘要： Ultra-dense edge computing is expected to provide delay-sensitive and computational-intensive services for mobile devices. Due to the complexity and unpredictability of the network environment, it is challenging to ensure the continuity and security of computing offloading services in the process of user movement. Most existing works consider the decisions of communication handover and computational offloading simultaneously while ignoring the security on offloading tasks. In light of this, we propose a secure mobility management framework for blockchain-based ultra-dense edge computing, where blockchain reduces duplicate authentication between edge servers. We jointly optimize the wireless handover and service migration decisions between base stations, which is translated into a multi-objective dynamic optimization problem using the Lyapunov optimization. The optimization problem is solved by deep reinforcement learning approach based on the Actor-Critic method. Finally, we use simulation studies to evaluate the performance of the proposed scheme. The results show that, compared with other existing schemes, the proposed scheme can reduce the average delay of computing tasks, the rate of tasks failure and the rate of handover.

关键词： Mobile edge computing   Handover authentication   Mobility patterns  

摘要： With the advancement in technology and the exponential growth of mobile devices, network traffic has increased manifold in cellular networks. Due to this reason, latency reduction has become a challenging issue for mobile devices. In order to achieve seamless connectivity and minimal disruption during movement, latency reduction is crucial in the handover authentication process. Handover authentication is a process in which the legitimacy of a mobile node is checked when it crosses the boundary of an access network. This paper proposes an efficient technique that utilizes mobility patterns of the mobile node and mobile Edge computing framework to reduce handover authentication latency. The key idea of the proposed technique is to categorize mobile nodes on the basis of their mobility patterns. We perform simulations to measure the networking latency. Besides, we use queuing model to measure the processing time of an authentication query at an Edge servers. The results show that the proposed approach reduces the handover authentication latency up to 54% in comparison with the existing approach.

关键词： Energy efficiency   Data gathering   IoT networks   Mobile edge computing  

摘要： The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robots have become an important part of IoT networks as smart devices and are widely used to support versatile domain applications. In this setting, gathering sensory data from social-aware mobile robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially when considering fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.

关键词： Artificial intelligence   Servers   Mobile handsets   Wireless communication   Task analysis   Resource management   Computational modeling   Edge intelligence   mobile edge computing   service placement   resource allocation  

摘要： 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.

关键词： Edge intelligence   mobile edge computing   machine learning   resource-constrained   IoT   low-power   deep learning   embedded   distributed computing  

摘要： Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the loT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

关键词： Optimization   Task analysis   Wireless communication   Resource management   Stability analysis   Computational modeling   Stochastic processes   Mobile edge computing   resource allocation   Lyapunov optimization   deep reinforcement learning  

摘要： Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing the future realizations of random channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low computation time that is particularly suitable for real-time implementation in fast fading environments.

关键词： Trajectory   Monitoring   Heuristic algorithms   Throughput   Edge computing   COVID-19   Approximation algorithms   Dynamic scheduling   Unmanned aerial vehicles   Task analysis   UAV   mobile edge computing   trajectory design   task scheduling   5G communications  

摘要： Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

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

摘要： In the mobile edge computing environment, edge servers, edge base stations and other intermediate servers have limited computing, storage resources and limited coverage, resulting in increased content access delays and reduced user experience quality. Content caching methods based on file value and corresponding algorithms are proposed. This method uses Markov chains to predict the mobile user's trajectory in the next time slot, and active caching and cache replacement algorithms are designed to minimize content access latency. In addition, because edge servers are connected to many different access points or base stations, whether or not to migrate the ongoing edge services and where to migrate the services are the key issues to consider when any user moves outside the service area of the related edge servers. To reduce the overhead of service migration due to the mobility of users and the complex topological relationship between edge servers, a de-centralized service migration algorithm is proposed, which integrates the analysis and selection of the optimal edge base station and service placement decisions with the objective of minimizing the overhead of service migration. The experimental results show that the proposed edge caching and service migration methods reduce content access latency and service migration overhead.