关键词： Task analysis   Stochastic processes   Distributed computing   Resilience   Servers   Security   Resource management   Auction theory   Bayesian methods   coded distributed computing   game theory   incomplete information   Internet of things   machine learning   Markov processes   mobile edge computing   resiliency   resource allocation and pricing   security  

摘要： This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as "masters" which offload their computations to edge devices acting as "workers". The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master;ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.

关键词： mobile edge computing   computational offloading   deep learning   cost function   remote execution   energy efficiency  

摘要： In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions.

关键词： cost minimization model   energy consumption   Internet of things   mobile edge computing   scheduling algorithm  

摘要： Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

关键词： Object detection   Servers   Energy consumption   Batteries   Wireless communication   Computational modeling   Analytical models   Augmented reality   energy efficiency   mobile edge computing   object detection   cache management  

摘要： In this letter, we propose a novel joint mobile cache and power management scheme for energy-efficient mobile augmented reality (MAR) services in mobile edge computing (MEC). For this purpose, depending on whether the cache is hit or not, analytical models for 1) the energy consumption of an MAR device (MD) and 2) the service latency for the MAR are derived and investigated. Considering a tradeoff between energy and service latency in terms of the cache size in MAR with MEC, we design a theoretical framework for the mobile cache management of the MD to optimize the mobile cache size as well as transmission power while guaranteeing the required service latency. From the numerical experiments, we evaluate the performance of the proposed scheme and demonstrate insights regarding the optimization of the performance of MEC-assisted MAR services with a mobile cache.

关键词： Mobile edge computing   Dynamic caching   Cache algorithm   Wireless network  

摘要： Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive and latency-sensitive applications on the eve of the large-scale commercial operation of 5G. However, the slow-start phenomenon incurred by existing request history-based caching strategies limits the performance of wireless edge caching, especially in the dynamic scenario where both mobile devices and contents arrive and leave periodically. On the other hand, it is also a hard task for deep reinforcement learning-based methods to adapt to the dynamics of the environment. In this backdrop, a new caching algorithm, called Similarity-Aware Popularity-based Caching (SAPoC), is presented in this paper to promote the performance of wireless edge caching in dynamic scenarios through utilizing the similarity among contents. In SAPoC algorithm, a content?s popularity is determined by not only its requests history but also its similarity with existing popular ones to enable a quick-start of newly arrived contents. A series of simulation experiments are conducted to evaluate SAPoC algorithm?s performance. Results have shown that SAPoC outperforms several typical proposals in both cache hit ratio and energy consumption.

关键词： computing offloading   energy-efficient optimization   heterogeneous network   Internet of Things in power system   mobile edge computing  

摘要： Based on the heterogeneous network architecture, in this paper by taking into account the mobile edge computing (MEC) architecture of Internet of things in power systems using three-tier computing, the energy consumption model and delay model are established respectively for each tier. Then the related tasks are offloaded from local computation in smart mobile device (SMD) to the MEC servers of small base station (SBS) and macro base station (MBS). To minimize the energy consumption due to the computing offloading, through variable substitution and decomposition, then by taking advantage of theoretical derivation based on the objective function monotonicity, the optimal solution is finally obtained. Simulation results show that, in comparison with other methods, the proposed scheme can achieve the effects of energy saving with less time delay. © 2021 ELECTRIC POWER. All rights reserved.

关键词： Mobile edge computing   Fog computing   Biosurveillance systems   Edge cloud data management  

摘要： Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate responses to biological threats and consequently save human lives. Nevertheless, biosurveillance systems are costly to implement and hard to operate. Furthermore, they rely on static infrastructures that might not cope with the evolving dynamics of the monitored environment. In this paper, we present a reorganizing biosurveillance framework for the detection and localization of biological threats with fog and mobile edge computing support. In the proposed framework, a hierarchy of fog nodes are responsible for aggregating monitoring data within their regions and detecting potential threats. Although fog nodes are deployed on a fixed base station infrastructure, the framework provides an innovative technique for reorganizing the monitored environment structure to adapt to the evolving environmental conditions and to overcome the limitations of the static base station infrastructure. Evaluation results illustrate the ability of the framework to localize biological threats and detect infected areas. Moreover, the results show the effectiveness of the reorganization mechanisms in adjusting the environment structure to cope with the highly dynamic environment.

关键词： analytical modeling   mean response time   mobile edge computing   performance   availability   stochastic Petri net  

摘要： Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with p-value equal to 0.684 by t-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization.

关键词： Task analysis   Optimization   MIMO communication   Heuristic algorithms   Energy consumption   Delays   Servers   Mobile edge computing   MIMO   Lyapunov optimization   energy harvesting  

摘要： By providing spatial diversity gain, the incorporation of multiple antennas into mobile edge computing (MEC) systems can improve the transmission performance. Meanwhile, employing energy harvesting (EH) helps enhance the system sustainability. In this paper, we focus on multi-input multi-output (MIMO) MEC systems with EH and studies the computation offloading. The design objective is to minimize the time average of a weighted sum of energy consumption and execution delay, meanwhile stabilizing the battery energy queue. To this end, we formulate the problem as a statistic program and propose a dynamic computation offloading (DCO) algorithm in which the transmitter covariance matrix, CPU-cycle frequencies for local computing, and partial offloading ratio are jointly optimized. Based on Lyapunov optimization, the program is first transformed into a nonconvex per-time slot problem. Then, we solve it by the successive convex approximation (SCA) technique, where a sequence of convex problems are created and solved. Simulation results demonstrate that the proposed algorithm is asymptotically optimal and outperforms several benchmark schemes in terms of both the average system cost and task drop ratio.

关键词： data placement   genetic algorithm   mobile edge computing   particle swarm optimization  

摘要： Mobile edge computing (MEC) necessitates cost-effective deployment for executing scientific workflows with different tasks and datasets, which provides computing, storage and network control at the network edge. However, the execution of scientific workflows in MEC results in heavy costs of data placement including data transmission and data storage. Although there are solutions for data placement in traditional cloud computing, they cannot effectively respond to the latency-sensitive property of scientific workflows, which leads to the excessive costs of data placement. To cope with this problem, we combine the advantages of MEC and cloud computing and propose a genetic algorithm particle swarm optimization (GAPSO) based method to explore the optimal strategy of data placement for scientific workflows in MEC. First, a unified model of data placement is designed to explore a cost-effective strategy, which considers the different characteristics between MEC and cloud computing as well as the impact of latency constraint on transmission costs. Next, the advantages of genetic algorithm (GA) and particle swarm optimization (PSO) are integrated to optimize the proposed model, which utilities the fast convergence of PSO and the crossover and mutation operations of GA. Simulations using real-world scientific workflows show the effectiveness of the proposed method for reducing data placement costs in MEC.