关键词： 5G-   IMT-2020   IMT-2030   framework   mobile edge computing   fog computing  

摘要： Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

关键词： Task analysis   Time factors   Optimization   Servers   Collaboration   System performance   Computational modeling   Data reusing   computation offloading   content caching   mobile edge computing  

摘要： Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.

关键词： Servers   Resource management   Throughput   Stochastic processes   Optimization   Edge computing   Computational modeling   Mobile edge computing   user allocation   Lyapunov optimization   resource allocation  

摘要： Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) - an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

关键词： Streaming media   Switches   Costs   Servers   Transcoding   Three-dimensional displays   Heuristic algorithms   360-degree video streaming   tiling   mobile edge computing   switching cost   multi-armed bandit  

摘要： Virtual Reality (VR) and Augmented Reality (AR) applications are expected to be a key driver for the 5G network. The need of the MEC support for caching and transcoding of omnidirectional content has been identified as a major topic of research. In this paper, we study the optimal caching problem in an MEC-enabled VR system for tile-based viewport-adaptive 360-degree video streaming. For content caching, the replacement of the cached content inevitably incurs content switching cost, which is largely ignored in caching policy design. In this work, we aim to find the optimal caching policy to optimize the long-term transmission quality by considering both transmission latency and content switching cost. We apply the combinatorial multi-armed bandit (CMAB) theory to solve the problem with no a-priori knowledge on content popularity. Moreover, the CUCB with switching cost (CUCBSC) algorithm is adopted in this scenario. A transformation mechanism is designed to transform the generated single period optimization (SPO) problem into a multiple choice knapsack problem (MCKP). Rigorous theoretical analysis on the performance of the proposed algorithm is also provided. Finally, the effectiveness of the proposed learning based caching policy is confirmed by simulation results in terms of learning, hit-ratio, transmission and content switching delay.

关键词： Wireless communication   Cameras   5G mobile communication   Object detection   Wearable computers   Ions   Image edge detection   Mobile edge computing   millimeter-wave   5G wireless   smart wearables   mobile machine vision   deep learning   object detection  

摘要： Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been a growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing closer to end-users. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection in the case of a powerful, new smart wearable called (VISION)-I-4, for the Blind-and-Visually Impaired (BVI). The current (VISION)-I-4 system is an instrumented book-bag with high-resolution cameras, vision processing, and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge server to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) sub-6-GHz carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and detailed assessment of edge computing for object detection with mmWave and sub-6-GHz connectivity in an application with both high bandwidth and low latency requirements.

关键词： Task analysis   Energy consumption   Servers   Resource management   Costs   Wireless communication   Optimization   Computation offloading   deep reinforcement learning   energy efficiency   mobile edge computing   resource allocation  

摘要： Mobile edge computation offloading (MECO) has recently emerged as a promising method to support computation-intensive and latency-sensitive applications, significantly saving the battery energy of smart mobile devices (SMDs). However, on the one hand, the energy consumption depends on both the SMD and the MEC server, which makes it necessary to consider these two entities to achieve energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) system, efficient optimization algorithms based on binary offloading have received significant attention, while efficient algorithms for partial offloading under time-varying channels are seldom investigated. In this paper, we propose an energy-efficient algorithm based on deep reinforcement learning to optimize the overall energy cost in a real-time multi-user MEC system. We decompose the energy minimization problem into two sub-problems, where a deep neural network learns the optimal mapping between wireless channels and offloading ratios, and a closed-form solution for the optimal local frequency and a convex optimization algorithm are used to solve the resource allocation sub-problem. Finally, the extensive experiments demonstrate the effectiveness of our proposed algorithm in reducing the total energy consumption of the MECO system against several offloading schemes and achieving low processing latency fit to the time-varying wireless channels.

关键词： mobile edge computing   MEC   slack time   deadline and energy-efficient task scheduling   DEETS   task scheduling   mob-cloud architecture  

摘要： These days, more and more web are converting into mobile devices such as the internet of things (IoT). These applications are distributed in nature and installed on mobile and servers to achieve many business goals. With this motivation, mobile edge computing (MEC) is an evolving paradigm that reduces the energy consumption of mobile devices through the offload process. However, currently all studies only concentrated on mobile energy and widely overlooked the energy usage of the computing node resources and application deadlines during offloading and scheduling in the MEC network. This research suggests a new mobile edge cloud (mob-cloud) architecture that minimises the nodes energy and meets the deadline requirements of applications. This research formulates this problem as the convex optimisation problem under linear integer programming. The mob-cloud consists of different types of virtual machines which are implementing at the edge computing. This study introduces a novel deadline and energy-efficient task scheduling (DEETS) algorithm framework consisting of task sequencing, resource searching, and mobility-aware scheduling. Furthermore, DEETS keeps tradeoff performances between resource energy usage and task deadline. Simulation results show that the proposed DEETS outperformed all contemporary methods regarding energy usage and deadlines of applications by 50%.

关键词： Servers   Cloud computing   Resource management   Quality of service   Task analysis   Internet of Things   Delays   Mobile edge computing   cloud computing   data center hierarchy   mixed integer linear programming   genetic algorithm   particle swarm optimization  

摘要： Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their quality of service requirements, we propose a MILP (Mixed Integer Linear Programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles. We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, large-scale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to GA and PSO solutions.

关键词： Mobile edge computing  

摘要： This paper studies one typical mobile edge computing (MEC) system, where a single user has some intensively calculating tasks to be computed by M edge nodes (ENs) with much more powerful calculating capability. In particular, unmanned aerial vehicle (UAV) can act as the ENs due to its flexibility and high mobility in the deployment. For this system, we propose several EN selection criteria to improve the system whole performance of computation and communication. Specifically, criterion I selects the best EN based on maximizing the received signal-to-noise ratio (SNR) at the EN, criterion II performs the selection according to the most powerful calculating capability, while criterion III chooses one EN randomly. For each EN selection criterion, we perform the system performance evaluation by analyzing outage probability (OP) through deriving some analytical expressions. From these expressions, we can obtain some meaningful insights regarding how to design the MEC *** finally perform some simulation results to demonstrate the effectiveness of the proposed MEC network. In particular, criterion I can exploit the full diversity order equal to M. © 2022. Jun Liu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Mobile handsets   Task analysis   Cloud computing   Servers   Device-to-device communication   Energy consumption   Resource management   Computation offloading   social-aware   mobile edge computing   social networks   mobility  

摘要： With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users' Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users' QoS requirements with the help of deployed MEC facilities is still challenging since mobile users' service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users' willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn-Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users' time and energy consumption in extended real datasets.