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： 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： 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： Mobile Edge Computing   Offloading   Pricing   Resource allocation   Game theory  

摘要： Mobile Edge Computing is a new computing paradigm that offers cloud computation capabilities at the edge of the mobile network. Due to the proximity to Mobile Users (MUs), the edge cloud can be accessed with low latency by resource-limited mobile devices with the aim of extending their capabilities through computation offloading. Despite its unique features, the efficiency of offloading is highly influenced by the purchasing power of MUs as well as the competition arisen to access both limited communication and computation resources. However, since the MUs' demands for these resources change dynamically, the pricing approaches should be capable of adapting in real-world scenarios. To address the aforementioned challenges, we propose a game-based distributed scheme to jointly and dynamically allocate and price resources required for proper offloading in a two-tier NOMA-based mobile system. To this end, we consider the incentives of both the edge provider and MUs as well as conflicts arising from their interactions. Under the proposed scheme, MUs should pay not only based on the given resources but also based on their transmission power level as a punishment for their produced interference. We formulate the interactions among the monopolist edge provider and MUs using the Stackelberg game. Further, the power allocation in NOMA and the competition of MUs over computation resources are modeled using game theory and shown admitting the potential and the weakly coupled constraint game, respectively. Furthermore, we prove the existence and uniqueness of the equilibrium of both corresponding games and calculate it for both the edge provider and MUs. Simulation results demonstrate the efficiency of the proposed approach in terms of the utility of MUs and the edge provider, average allocated communication and computation resources, optimal pricing, and the number of rejected MUs under different purchasing power.

关键词： Cloud computing   Energy efficient resource allocation   Mobile edge computing   Service rate   User equipment   Utility function  

摘要： Limited battery and computing resources of mobile devices (MDs) induce performance limitations in mobile edge computing (MEC) networks. Computational offloading has the capability to provide computing and storage resources to MDs for resource-intensive tasks execution. Therefore, to minimize energy consumption and service delay, MDs offload the resource-intensive tasks to nearby mobile edge server (MES) for execution . However, due to time varying network conditions and limited computing resources at MES also, the offloading decision taken by MDs may not achieve the lowest cost. In this paper, we propose an energy efficient and faster deep learning based offloading technique (EFDOT) to minimize the overall cost of MDs. We formulate a cost function which considers the energy consumption and service delay of MDs, radio resources, energy consumption and delay due to task partitioning, and computing resources of the MDs and MESs. Due to high computational overhead of this comprehensive cost function, we generate a training dataset to train a deep neural network (DNN) in order to make the decision making process faster. The proposed work finds the optimal number of components, task partitioning, and fine-grained offloading policy simultaneously. We formulate the fine-grained offloading decision problem in MEC as multi-label classification problem and propose EFDOT to minimize the computation and offloading overhead. The simulation results show high accuracy of the DNN and high performance of EFDOT in terms of energy consumption, service delay, and battery life.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： —In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing. © 2021, CC BY.

关键词： context‐   awareness   data abstraction   edge computing environment   embedding convergence   knowledge representation  

摘要： Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object.

关键词： Mobile edge computing  

摘要： We consider a mobile edge computing scenario where a number of devices want to perform a linear inference Wx on some local data x given a network-side matrix W. The computation is performed at the network edge over a number of edge servers. We propose a coding scheme that provides information-theoretic privacy against z colluding (honest-but-curious) edge servers, while minimizing the overall latency-comprising upload, computation, download, and decoding latency-in the presence of straggling servers. The proposed scheme exploits Shamir's secret sharing to yield data privacy and straggler mitigation, combined with replication to provide spatial diversity for the download. We also propose two variants of the scheme that further reduce latency. For a considered scenario with 9 edge servers, the proposed scheme reduces the latency by 8% compared to the nonprivate scheme recently introduced by Zhang and Simeone, while providing privacy against an honest-but-curious edge server. Copyright © 2021, The Authors. All rights reserved.

关键词： Task analysis   Energy consumption   Delays   Mobile handsets   Cloud computing   Servers   Edge computing   Mobile edge computing   fog computing   computation sharing   NSGA2   multi-objective optimization   evolutionary algorithms   energy consumption   delay  

摘要： In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.