scholarly journals An Optimization Method for Mobile Edge Service Migration in Cyberphysical Power System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qian Cao ◽  
Qilin Wu ◽  
Bo Liu ◽  
Shaowei Zhang ◽  
Yiwen Zhang
Keyword(s):  
Optimization Problem ◽  
Geographical Location ◽  
High Mobility ◽  
Optimization Method ◽  
Edge Computing ◽  
Transmission Power ◽  
Continuous Change ◽  
Service Migration ◽  
Transmission Edge

To relieve the pressure of processing computation-intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user’s activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.

scholarly journals Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Haowei Lin ◽  
Xiaolong Xu ◽  
Juan Zhao ◽  
Xinheng Wang
Keyword(s):  
Cloud Computing ◽  
Mobile Application ◽  
High Mobility ◽  
Edge Computing ◽  
The Other ◽  
Mobile Users ◽  
Computing Power ◽  
Service Migration ◽  
The Stability ◽  
Multi Access

Abstract The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services’ migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.

scholarly journals A dynamic service migration strategy based on mobility prediction in edge computing

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199340
Author(s):  
Lanlan Rui ◽  
Shuyun Wang ◽  
Zhili Wang ◽  
Ao Xiong ◽  
Huiyong Liu
Keyword(s):  
Optimization Method ◽  
Edge Computing ◽  
User Mobility ◽  
Mobile Edge Computing ◽  
Frequent User ◽  
Migration Strategy ◽  
Service Interruption ◽  
Cost Constraints ◽  
Initial Location ◽  
Service Migration

Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

scholarly journals Dynamic Service Migration in Ultra-Dense Multi-Access Edge Computing network for High Mobility Scenarios

2020 ◽  
Author(s):  
Haowei Lin ◽  
Xiaolong Xu ◽  
Juan Zhao ◽  
Xinheng Wang
Keyword(s):  
Cloud Computing ◽  
Mobile Applications ◽  
High Mobility ◽  
Edge Computing ◽  
The Other ◽  
Mobile Users ◽  
Computing Power ◽  
Service Migration ◽  
The Stability ◽  
Multi Access

Abstract The Multi-Access Edge Computing (MEC) has higher computing power than user equipment and lower latency than remote cloud computing, making new types of services and mobile applications keep emerging. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services' migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the Infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.

scholarly journals Service Migration in Edge Computing Environments for Connected Autonomous Vehicles

2020 ◽  
Author(s):  
Lucas Sousa Pacheco ◽  
Denis Lima Rosário ◽  
Eduardo Coelho Cerqueira ◽  
Leandro Aparecido Villas
Keyword(s):  
Autonomous Vehicles ◽  
High Mobility ◽  
Future Generation ◽  
Edge Computing ◽  
Quality Level ◽  
Mobility Prediction ◽  
Processing Capacity ◽  
Prediction Scheme ◽  
Service Migration ◽  
Computing Environments

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

Heuristic Approaches for Non-Convex Problems

2014 ◽  
Vol 2 (1) ◽  
pp. 74-98
Author(s):  
Rosario Toscano ◽  
Ioan Alexandru Ivan
Keyword(s):  
Geometric Programming ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Method ◽  
Measurement Process ◽  
Stochastic Methods ◽  
Spiral Inductor ◽  
Convex Problems ◽  
Smooth Optimization

This paper aims at solving difficult optimization problems arising in many engineering areas. To this end, two recently developed optimization method will be introduced: the heuristic Kalman algorithms (HKA) and the quasi geometric programming (QGP) problems. The principle of HKA is to consider the optimization problem as a measurement process intended to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, is developed to improve the quality of the estimate obtained through a measurement process. A significant advantage of HKA against other stochastic methods lies mainly in the small number of parameters which have to be set by the user. In this paper we also introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non smooth optimization problem is motivated by the fact that many engineering problems can be formulated as a QGP. To solve this kind of problems (QGP), an algorithm is proposed which is based on the resolution of a succession of standard GP. An interesting feature of the proposed approach is that it does not need to develop specific program solver and works well with any existing solver able to solve conventional GP. In the last part of the paper, it is to shown that HKA and QGP can be efficiently used to solve difficult non-convex optimization problems. In particular, we have addressed the problem of robust structured control and on-ship spiral inductor design. Numerical experiments exemplify the resolution of this kind of problems.

scholarly journals Video Pre-Caching Joint Hand-Off and Content Delivery in Multi-Access Edge Computing Based EONs

2021 ◽  
Author(s):  
Yutong Chai ◽  
Shan Yin ◽  
Lihao Liu ◽  
Liyou Jiang ◽  
Shanguo Huang
Keyword(s):  
Quality Of Service ◽  
High Mobility ◽  
Content Delivery ◽  
Edge Computing ◽  
The Other ◽  
Latency Time ◽  
Hand Off ◽  
Simulation Results ◽  
Multi Access

Multi-access Edge Computing (MEC) performs as a feasible solution when it comes to content delivery, for it can bring contents much closer to users. However, the hand-off (HO) and latency that occur in user movement reduce the users’ quality of service. In this work, we consider the problem of high mobility handoff and content delivery of video streaming in the MEC based EONs. We propose a video pre-caching algorithm considering handoff and content delivery. The algorithm firstly selects the content delivery method and chunks the video accordingly using a preset threshold. Secondly, the algorithm chooses the shortest transmission path and calculates the latency time using Dijkstra method. Simulation results show that our algorithm significantly reduces the latency time and balances the server load compared to the other two baselines.

scholarly journals Task Migration Based on Reinforcement Learning in Vehicular Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sungwon Moon ◽  
Jaesung Park ◽  
Yujin Lim
Keyword(s):  
Quality Of Service ◽  
Reinforcement Learning ◽  
Load Balancing ◽  
Learning Algorithm ◽  
High Mobility ◽  
Edge Computing ◽  
Task Completion ◽  
Task Migration ◽  
Delay Constraints

Multiaccess edge computing (MEC) has emerged as a promising technology for time-sensitive and computation-intensive tasks. With the high mobility of users, especially in a vehicular environment, computational task migration between vehicular edge computing servers (VECSs) has become one of the most critical challenges in guaranteeing quality of service (QoS) requirements. If the vehicle’s tasks unequally migrate to specific VECSs, the performance can degrade in terms of latency and quality of service. Therefore, in this study, we define a computational task migration problem for balancing the loads of VECSs and minimizing migration costs. To solve this problem, we adopt a reinforcement learning algorithm in a cooperative VECS group environment that can collaborate with VECSs in the group. The objective of this study is to optimize load balancing and migration cost while satisfying the delay constraints of the computation task of vehicles. Simulations are performed to evaluate the performance of the proposed algorithm. The results show that compared to other algorithms, the proposed algorithm achieves approximately 20–40% better load balancing and approximately 13–28% higher task completion rate within the delay constraints.

HASTANELERİN HİZMET KALİTESİNİN HASTA TATMİN ÖLÇEĞİ İLE ÖLÇÜLMESİ: KARABÜK DEVLET HASTANESİNDE YATAN HASTALAR ÜZERİNDE BİR ARAŞTIRMA THE MEASUREMENT SERVICE QUALITY OF THE HOSPITALS BY THE PATIENT SCALE: A RESEARCH ON THE PATIENTS IN KARABUK STATE HOSP

2013 ◽  
Vol 1 (1) ◽  
pp. 100
Author(s):  
Selçuk Yurtsever
Keyword(s):  
Patient Satisfaction ◽  
Health Services ◽  
Literature Review ◽  
Private Sector ◽  
State Hospital ◽  
Continuous Change ◽  
Satisfaction Scale ◽  
The World ◽  
Main Factors

It has been known that both in the world and in Turkey a continuous change has been experienced in the provision of health services in recent years. In this sense by adopting the customer(client) focused approach of either public or private sector hospitals; it has been seen that they are in the struggle for presenting a right, fast, trustuble, comfy service. The purpose of this research is to measure the satisfaction degree, expectations and perceptions of the patients in Karabük State Hospital through comparison. In this context, the patient satisfaction scale which has been developed as a result of literature review has been used and by this scale it has been tried to measure the satisfaction levels of the patients in terms of material and human factors which are the two main factors of the service that was presented. In the study, with the scales of Servqual and 0-100 Points together, in the part of the analysis MANOVA have been used. The expectations and the perceptions of the patient has been compared first by generally and then by separating to different groups according to the various criterias and in thisway it has been tried to be measured their satisfaction levels. According to the results that were obtained, although, the satisfaction levels of the patients who have taken service from Karabük State Hospital are high in terms of thedoctors and the nurses; it has been reached to the result that their satisfaction levels are low in terms of the materials that have been used at the presenting of the service and the management.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

The Improvement of Newton Method and the Validation of Optimization Idea of Blind Walking Repeatedly

2011 ◽  
Vol 250-253 ◽  
pp. 4061-4064
Author(s):  
Chun Ling Zhang
Keyword(s):  
Saddle Point ◽  
Newton Method ◽  
Optimization Problem ◽  
Initial Point ◽  
Optimization Method ◽  
Maximum Point ◽  
Optimal Result ◽  
Feasible Domain ◽  
Parallel Arithmetic ◽  
Newton Direction

The existence of maximum point, oddity point and saddle point often leads to computation failure. The optimization idea is based on the reality that the optimum towards the local minimum related the initial point. After getting several optimal results with different initial point, the best result is taken as the final optimal result. The arithmetic improvement of multi-dimension Newton method is improved. The improvement is important for the optimization method with grads convergence rule or searching direction constructed by grads. A computational example with a saddle point, maximum point and oddity point is studied by multi-dimension Newton method, damped Newton method and Newton direction method. The importance of the idea of blind walking repeatedly is testified. Owing to the parallel arithmetic of modernistic optimization method, it does not need to study optimization problem with seriate feasible domain by modernistic optimization method.

Sign in / Sign up

Export Citation Format

Share Document