Search results for "mobile edge computing"
showing 10 items of 11 documents
Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint
2019
Mobile Edge Computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; 3) high quality-of-service (QoS) requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services i…
Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach
2021
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 …
A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing
2020
In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realis…
Performance Analysis of Memory Cloning Solutions in Mobile Edge Computing
2018
This paper deals with the problem of service migration in the emerging scenarios of Mobile Edge Computing. Mobile edge computing is achieved by moving the traditional cloud infrastructures, exploited by many today applications, close to the network edge in order to reduce the response times in the so called tactile-internet. However, because of user mobility, such an application architecture may pose the problem of service migration in case of handover from one server site to another. After introducing the current solutions for dealing with service migration and, in particular, the approaches based on service decomposition into multiple layers, we quantify the migration time and the service…
NFVMon: Enabling Multioperator Flow Monitoring in 5G Mobile Edge Computing
2018
With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management…
Low-Latency Infrastructure-Based Cellular V2V Communications for Multi-Operator Environments With Regional Split
2021
Mobile network operators are interested in providing Vehicle-to-Vehicle (V2V) communication services using their cellular infrastructure. Regional split of operators is one possible approach to support multi-operator infrastructure-based cellular V2V communication. In this approach, a geographical area is divided into non-overlapping regions, each one served by a unique operator. Its main drawback is the communication interruption motivated by the inter-operator handover in border areas, which prevents the fulfillment of the maximum end-to-end (E2E) latency requirements of fifth generation (5G) V2V services related to autonomous driving. In this work, we enable a fast inter-operator handove…
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
2019
Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…
Multi-objective optimization for computation offloading in mobile-edge computing
2017
Mobile-edge cloud computing is a new cloud platform to provide pervasive and agile computation augmenting services for mobile devices (MDs) at anytime and anywhere by endowing ubiquitous radio access networks with computing capabilities. Although offloading computations to the cloud can reduce energy consumption at the MDs, it may also incur a larger execution delay. Usually the MDs have to pay cloud resource they used. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and price cost of offloading process in a mobile-edge cloud system. Specifically, both wireless transmission and computing capabilities are explicitly and jointly co…
Latency-Oblivious Distributed Task Scheduling for Mobile Edge Computing
2018
Mobile Edge Computing (MEC) is emerging as one of the effective platforms for offloading the resource- and latency-constrained computational services of modern mobile applications. For latency- and resource-constrained mobile devices, the important issues include: 1) minimize end-to-end service latency; 2) minimize service completion time; 3) high quality-of-service (QoS) requirement to offload the complex computational services. To address the above issues, a latencyoblivious distributed task scheduling scheme is designed in this work to maximize the QoS performance and goodput for the MEC services. Unlike most of the existing works, we consider the latency-oblivious property of different …
Resource Allocation and Computation Offloading for Wireless Powered Mobile Edge Computing
2022
In this paper, we investigate a resource allocation and computation offloading problem in a heterogeneous mobile edge computing (MEC) system. In the considered system, a wireless power transfer (WPT) base station (BS) with an MEC sever is able to deliver wireless energy to the mobile devices (MDs), and the MDs can utilize the harvested energy for local computing or task offloading to the WPT BS or a Macro BS (MBS) with a stronger computing server. In particular, we consider that the WPT BS can utilize full- or half-duplex wireless energy transmission mode to empower the MDs. The aim of this work focuses on optimizing the offloading decision, full/half-duplex energy harvesting mode and energ…