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In this paper, we consider non-clairvoyant task offloading for random tasks in mobile edge computing within the framework of combinatorial optimisation. For offline non-clairvoyant task offloading, we propose a non-clairvoyant task offloading algorithm, which is able to determine a task offloading strategy without knowing the amount of computation and communication of any task. For online non-clairvoyant task offloading, we propose a randomised online task offloading algorithm, which is able to make an offloading decision for an arrival task without knowing anything about future tasks and other tasks. For both algorithms, we analyse the probability of certain performance guarantee. We also demonstrate numerical data. To the best of the author's knowledge, this is the first paper which considers both offline and online non-clairvoyant task offloading in mobile edge computing, together with analytical results on performance guarantee with high probability. [ABSTRACT FROM AUTHOR]

In the present literature, various algorithms are available for computing the shortest path between two objects. The maximum number of these algorithms compute the shortest path either between two static objects or one static object and one dynamic object. This article presents an insight to integrated Mobile Edge Computing (MEC) based smart devices for tracking mobile rogue objects based on dynamic source and dynamic destination optimal cost estimation. This device considers any two mobile objects to estimate the shortest path between them. The proposed Ant Colony Optimization (ACO) based algorithm considers the property of dead-end removal and nth path exploration with efficient self-loop removal strategy. To review the performance of the proposed algorithm, experimentations are carried out and compared with several well-established shortest cost estimation techniques available in the literature – Floyd Warshall, Bellman Ford, Dijkstra, A* algorithms and the only dynamic shortest path algorithm. The detailed algorithmic comparisons clearly indicate the superiority of the proposed one over the existing dynamic and present state-of-the-art shortest path estimation methodologies. [ABSTRACT FROM AUTHOR]

As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity servers are deployed in the edge to form a decentralised network of mini datacenters each serving a localised region. A challenge is how to place these servers geographically to maximise the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms. [ABSTRACT FROM AUTHOR]

The communication services of multimedia, such as live online and short videos, gained significant attention and have been prominent in daily social interactions due to the fast development of 5G technology. However, the increase in multimedia communication requirements presents further difficulties for both the communication capacity of the wireless network and its processing capability. Mobile Edge Computing (MEC) is considered an effective technology to overcome the mentioned challenges. The subject of long-term task allocation including resource coordination is the primary focus of this study, with a special emphasis on multimedia services that must be processed, uploaded, and shared in network. The optimization issue is formulated as a stochastic optimization issue to reduce the system's time-average energy consumption. An online energy-efficient task allocation and computing offloading method is proposed to determine task allocation, coordinate and optimize wireless, computing resource allocation, considering dynamic wireless state and service delay constraints. The proposed method is implemented in MATLAB and the efficacy of the MCO-VEA framework is estimated and performance metrics, such as minimum user rate, sum rate, energy, and spectral performance are analyzed. Then, the performance of MEC-MCO-VEA provides 22.35%, 25.84% and 36.58% higher throughput and 25.45%, 41.28%, and 28.56% higher energy efficiency compared to the existing models, such as an energy-effective multimedia task assignment with computing offloading for mobile edge computing (BA-LO-EE), energy efficiency using a communication deep neural network optimized with power allocation optimization (EE-CDNN-PAO), and energy efficiency using deep transfer deterministic policy gradient with deep deterministic policy gradient algorithm (DT-DPG-EE), respectively. [ABSTRACT FROM AUTHOR]

This study presents an innovative architecture leveraging Deep Reinforcement Learning (DRL) to optimize online service delivery in Mobile Edge Computing (MEC) networks. Our proposed method, the DRL-based dynamic Service Function Chain (SFC) placement with parallelized Virtual Network Functions (VNFs), aims to maximize Long-Term Cumulative Reward (LTCR). By parallelizing VNFs, our approach achieves computational acceleration, enhancing the efficiency of online service provisioning. Furthermore, we enhance future request processing capabilities by extracting the distribution of initialized VNFs. Through comprehensive simulations, our proposed architecture demonstrates a notable improvement, with approximately a 9.5% increase in the average number of accepted requests. [ABSTRACT FROM AUTHOR]

The exponential growth of networking technology has resulted in a massively expanded computer ecosystem. With implications in the Industrial Internet of Things (IIoT) and virtual reality, mobile networks operate and provide a multi-aspect strategy for multiple resource allocation paradigms and service-oriented possibilities in the computing sectors. The Mobile Edge Computing (MEC) model combines a virtual source with edge communication among execution. Thus, this study is to develop and implement a revolutionary resource allocation technique in the IIoT by combining optimal Reinforcement Learning (RL) with a hybrid meta-heuristic algorithm. The three basic levels in the proposed paradigm are "Data input layer," "Data management layer," and "Data analytics layer". The data management layer is responsible for the data collected from edge devices and external devices. The proposed model's goal at the data management layer is to provide an intelligent work allocation mechanism. The Harris Hawks-Spider Monkey Optimization (HH-SMO) method combines Harris Hawks Optimization (HHO) and Spider Monkey Optimization (SMO) to find the best job allocation. From the statistical analysis, the mean of HH-SMO-RL is 15.88%, 14.91%, 14.79%, and 5.99% superior to SMO-RL, HHO-RL, JA-RL, and DHOA-RL respectively, which has shown the resource allocation in IIoT using optimized RL respectively. [ABSTRACT FROM AUTHOR]

Recently, we have seen an increase of the use of Cloud computing to provide Modelling & Simulation services. In general, mobility support is lacking since clients normally contact the same centralised clouds regardless of their locations. In this research, we propose a method and algorithms to build Fogs as private services in which different middleware running in varied Virtual Machines (on various distributed Fogs and Cloud backbone) to expose different services. Clients access services via nearby Fogs, which then discover the required resources to run experiments (on Fogs or cloud backbone). Our focus is on mobility, to permit clients to conduct experiments (and visualise simulation results) using mobile devices. Mobility support is further improved by introducing the novel concept of a mobile simulation experiment: if a device moves away from a given Fog zone, the experiment moves with the device. We present a prototype implementation, evaluation results, and different case studies. [ABSTRACT FROM AUTHOR]