scholarly journals Efficient Multi-Player Computation Offloading for VR Edge-Cloud Computing Systems

2020 ◽  
Vol 10 (16) ◽  
pp. 5515 ◽  
Author(s):  
Abdullah Alshahrani ◽  
Ibrahim A. Elgendy ◽  
Ammar Muthanna ◽  
Ahmed Mohammed Alghamdi ◽  
Adel Alshamrani
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Low Complexity ◽  
Computation Offloading ◽  
Low Latency ◽  
Integer Optimization ◽  
New Paradigm ◽  
Efficient Manner ◽  
Battery Capacity ◽  
Network Latency

Virtual reality (VR) is considered to be one of the main use cases of the fifth-generation cellular system (5G). In addition, it has been categorized as one of the ultra-low latency applications in which VR applications require an end-to-end latency of 5 ms. However, the limited battery capacity and computing resources of mobile devices restrict the execution of VR applications on these devices. As a result, mobile edge-cloud computing is considered as a new paradigm to mitigate resource limitations of these devices through computation offloading process with low latency. To this end, this paper introduces an efficient multi-player with multi-task computation offloading model with guaranteed performance in network latency and energy consumption for VR applications based on mobile edge-cloud computing. In addition, this model has been formulated as an integer optimization problem whose objective is to minimize the sum cost of the entire system in terms of network latency and energy consumption. Afterwards, a low-complexity algorithm has been designed which provides comprehensive processes for deriving the optimal computation offloading decision in an efficient manner. Furthermore, we provide a prototype and real implementation for the proposed system using OpenAirInterface software. Finally, simulations have been conducted to validate our proposed model and prove that the network latency and energy consumption can be reduced by up to 26.2%, 27.2% and 10.9%, 12.2% in comparison with edge and cloud execution, respectively.

Research on Communication Scheduling Algorithm for Smart Home in Internet of Things Under Cloud Computing

2019 ◽  
Vol 23 (1) ◽  
pp. 124-128 ◽  
Author(s):  
Jie Zhang ◽  
◽  
Mantao Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Objective Function ◽  
Smart Home ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Low Latency ◽  
Communication Scheduling

The current communication scheduling algorithm for smart home cannot realize low latency in scheduling effect with unreasonable control of communication throughput and large energy consumption. In this paper, a communication scheduling algorithm for smart home in Internet of Things under cloud computing based on particle swarm is proposed. According to the fact that the transmission bandwidth of any data flow is limited by the bandwidth of network card of sending end and receiving end, the bandwidth limits of network card of smart home communication server are used to predict the maximum practicable bandwidth of data flow. Firstly, the initial value of communication scheduling objective function of smart home and particle swarm is set, and the objective function is taken as the fitness function of particle. Then the current optimal solution of objective function is calculated through predicted value and objective function, current position and flight speed of particle should be updated until the iteration conditions are met. Finally, the optimal solution is output, the communication scheduling of smart home is thus realized. Experiments show that this algorithm can realize low latency with small energy consumption, and the throughput is relatively reasonable.

scholarly journals Task Offloading Strategy of 6G Heterogeneous Edge-Cloud Computing Model considering Mass Customization Mode Collaborative Manufacturing Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hang Zhou ◽  
Yong Xiang ◽  
Hao-Feng Li ◽  
Rong Yuan
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy System ◽  
Low Energy ◽  
Low Latency ◽  
Computing Services ◽  
Proposed Model ◽  
Cloud Server ◽  
Low Energy Consumption ◽  
Power And Energy

With the continuous integration of cloud computing, edge computing, and Internet of things (IoT), various mobile applications will emerge in future 6G network. Driven by real-time response and low energy consumption requirements, mobile edge-cloud computing (MECC) will play an important role to improve user experience and reduce costs. However, due to the complexity of applications, the computing capacity of devices cannot meet the low-latency and low energy consumption requirement. Meanwhile, subject to the limited supplement of power and energy system, the heterogeneous multilayer mobile edge-cloud computing (HetMECC) is proposed to join cloud server, edge server, and terminal devices for data calculation and transmission. By dividing computing tasks, terminal applications can receive reliable and efficient computing services. The simulation results show that the proposed model can achieve the low-latency requirement of data calculation and transmission and improve the robustness of architecture.

scholarly journals Computation Offloading and Scheduling in Edge-Fog Cloud Computing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Dadmehr Rahbari ◽  
Mohsen Nickray
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Test Environment ◽  
Iot Devices ◽  
And Task

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment. 

Unsupervised Deep Learning for Binary Offloading in Mobile Edge Computation Network

2021 ◽  
Author(s):  
Xue Chen ◽  
Hongbo Xu ◽  
Guoping Zhang ◽  
Yun Chen ◽  
Ruijie Li
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Energy Consumption ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Low Complexity ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Unsupervised Deep Learning ◽  
Execution Delay

Abstract Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on mobile device (MD). The resource allocation of MEC is an optimization problem, however, the existing large amount of computation may hinder its practical application. In this work, we propose a multiuser MEC framework based on unsupervised deep learning (DL) to reduce energy consumption and computation by offloading tasks to edge servers. The binary offloading decision and resource allocation are jointly optimized to minimize energy consumption of MDs under latency constraint and transmit power constraint. This joint optimization problem is a mixed integer nonconvex problem which result in the gradient vanishing problem in backpropagation. To address this, we propose a novel binary computation offloading scheme (BCOS), in which a deep neural network (DNN) with an auxiliary network is designed. By using the auxiliary network as a teacher network, the student network can obtain the lossless gradient information in joint training phase. As a result, the sub-optimal solution of the optimization problem can be acquired by the learning-based BCOS. Simulation results demonstrate that the BCOS is effective to solve the binary offloading problem by the trained network with low complexity.

scholarly journals An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 46 ◽  
Author(s):  
Said BEN ALLA ◽  
Hicham BEN ALLA ◽  
Abdellah TOUHAFI ◽  
Abdellah EZZATI
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Centers ◽  
High Stress ◽  
Electricity Consumption ◽  
New Paradigm ◽  
Energy Aware ◽  
Carbon Footprints ◽  
Efficient Energy ◽  
Tasks Scheduling

Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users’ requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users’ priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints.

The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Piyush Rawat ◽  
Siddhartha Chauhan
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Energy Levels ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Function ◽  
Efficient Manner ◽  
Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

A survey on cloud computing privacy and security compliances in health domain (Preprint)

2020 ◽  
Author(s):  
Zahra Zandesh
Keyword(s):  
Cloud Computing ◽  
New Paradigm ◽  
Data Governance ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Memory Space ◽  
Health Domain ◽  
Security Services ◽  
Cloud Compliance ◽  
Complicated Nature

BACKGROUND The complicated nature of cloud computing encompassing internet-based technologies and service models for delivering IT applications, processing capability, storage, and memory space and some notable features motivate organizations to migrate their core businesses to the cloud. Consequently, healthcare organizations are much interested to migrate to this new paradigm despite challenges about security, privacy and compliances issues. OBJECTIVE The present study was conducted to investigate all related cloud compliances in health domain in order to find gaps in this context. METHODS All works on cloud compliance issues were surveyed after 2013 in health domain in PubMed, Scopus, Web of Science, and IEEE Digital Library databases. RESULTS Totally, 36 compliances had been found in this domain used in different countries for a variety of purposes. Initially, all founded compliances were divided into three parts as well as five standards, twenty-eight legislations and three policies and guidelines each of which is presented here by in detail. CONCLUSIONS Then, some main headlines like compliance management, data management, data governance, information security services, medical ethics, and patients' rights were recommended in terms of any compliance or frameworks and their corresponding patterns which should be involved in this domain.

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