scholarly journals Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment

2021 ◽  
Vol 12 (6) ◽  
pp. 2581-2593
Author(s):  
C. Anuradha, M. Ponnavaikko
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Smart Cities ◽  
Edge Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Major Barrier

Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.

scholarly journals SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Juan Fang ◽  
Kai Li ◽  
Juntao Hu ◽  
Xiaobin Xu ◽  
Ziyi Teng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Simulated Annealing Algorithm ◽  
Smart Cities ◽  
Structural Characteristics ◽  
Edge Computing ◽  
The Internet ◽  
Sensors And Actuators ◽  
Application Module

The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption.

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

scholarly journals Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 48 ◽  
Author(s):  
Ming Zhao ◽  
Ke Zhou
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Energy Optimization ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Innovative Technique ◽  
Simulation Results ◽  
Bp Model

Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.

scholarly journals Homomorphic Evaluation of the Integer Arithmetic Operations for Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Changqing Gong ◽  
Mengfei Li ◽  
Liang Zhao ◽  
Zhenzhou Guo ◽  
Guangjie Han
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Operation Rules ◽  
Original Scheme ◽  
Time Overhead

With the rapid development of the 5G network and Internet of Things (IoT), lots of mobile and IoT devices generate massive amounts of multisource heterogeneous data. Effective processing of such data becomes an urgent problem. However, traditional centralised models of cloud computing are challenging to process multisource heterogeneous data effectively. Mobile edge computing (MEC) emerges as a new technology to optimise applications or cloud computing systems. However, the features of MEC such as content perception, real-time computing, and parallel processing make the data security and privacy issues that exist in the cloud computing environment more prominent. Protecting sensitive data through traditional encryption is a very secure method, but this will make it impossible for the MEC to calculate the encrypted data. The fully homomorphic encryption (FHE) overcomes this limitation. FHE can be used to compute ciphertext directly. Therefore, we propose a ciphertext arithmetic operation that implements data with integer homomorphic encryption to ensure data privacy and computability. Our scheme refers to the integer operation rules of complement, addition, subtraction, multiplication, and division. First, we use Boolean polynomials (BP) of containing logical AND, XOR operations to represent the rulers. Second, we convert the BP into homomorphic polynomials (HP) to perform ciphertext operations. Then, we optimise our scheme. We divide the ciphertext vector of integer encryption into subvectors of length 2 and increase the length of private key of FHE to support the 3-multiplication level additional. We test our optimised scheme in DGHV and CMNT. In the number of ciphertext refreshes, the optimised scheme is reduced by 2/3 compared to the original scheme, and the time overhead of our scheme is reduced by 1/3. We also examine our scheme in CNT of without bootstrapping. The time overhead of optimised scheme over DGHV and CMNT is close to the original scheme over CNT.

scholarly journals When Sensor-Cloud Meets Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5324 ◽  
Author(s):  
Tian Wang ◽  
Yucheng Lu ◽  
Zhihan Cao ◽  
Lei Shu ◽  
Xi Zheng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Edge Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Control Center ◽  
Computing Center ◽  
Sensor Cloud ◽  
Evaluation Mechanism ◽  
And Control

Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.

scholarly journals Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 312 ◽  
Author(s):  
Ibrahim Alghamdi ◽  
Christos Anagnostopoulos ◽  
Dimitrios P. Pezaros
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Edge Computing ◽  
Cloud Services ◽  
Mobile Edge Computing ◽  
Sequential Decision ◽  
Mobile Nodes ◽  
Real World Data ◽  
Optimal Stopping Theory ◽  
Execution Delay

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.

scholarly journals ARTIFICIAL INTELLIGENCE AS A SERVICE IN DISTRIBUTED MULTI ACCESS EDGE COMPUTING ON 5G EXTRACTING DATA USING IOT AND INCLUDING AR/VR FOR REAL-TIME REPORTING

2021 ◽  
Vol 9 (1) ◽  
pp. 912-931
Author(s):  
Pavan Madduru
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Virtual Machines ◽  
Smart Cities ◽  
Assistive Technologies ◽  
Edge Computing ◽  
Smart Devices ◽  
The Internet ◽  
Iot Applications ◽  
The Internet Of Things

To meet the growing demand for mobile data traffic and the stringent requirements for Internet of Things (IoT) applications in emerging cities such as smart cities, healthcare, augmented / virtual reality (AR / VR), fifth-generation assistive technologies generation (5G) Suggest and use on the web. As a major emerging 5G technology and a major driver of the Internet of Things, Multiple Access Edge Computing (MEC), which integrates telecommunications and IT services, provides cloud computing capabilities at the edge of an access network. wireless (RAN). By providing maximum compute and storage resources, MEC can reduce end-user latency. Therefore, in this article we will take a closer look at 5G MEC and the Internet of Things. Analyze the main functions of MEC in 5G and IoT environments. It offers several core technologies that enable the use of MEC in 5G and IoT, such as cloud computing, SDN / NFV, information-oriented networks, virtual machines (VMs) and containers, smart devices, shared networks and computing offload. This article also provides an overview of MEC's ​​role in 5G and IoT, a detailed introduction to MEC-enabled 5G and IoT applications, and future perspectives for MEC integration with 5G and IoT. Additionally, this article will take a closer look at the MEC research challenges and unresolved issues around 5G and the Internet of Things. Finally, we propose a use case that MEC uses to obtain advanced intelligence in IoT scenarios.

scholarly journals Proposing a Novel IoT Framework by Identifying Security and Privacy Issues in Fog Cloud Services Network

2021 ◽  
Vol 9 (5) ◽  
pp. 592-596
Keyword(s):  
Resource Allocation ◽  
Cloud Services ◽  
Security And Privacy ◽  
The Internet ◽  
Application Framework ◽  
Cloud Provider ◽  
Iot Applications ◽  
Execution Model ◽  
Privacy Issues ◽  
The Internet Of Things

These days, the Internet of Things applications are growing progressively. However, the existing frameworks for IoT applications are notsatisfied the security, allocation, and provisioning requirements.We propose a novel secure IoT application framework. Fog cloud execution model is serverless computing which is run by the server in cloud provider and the allocation of machine resources is dynamically managed. The application which consumes the actual amount of resources, the pricing is based upon that rather on pre-purchase units of capacity. The proposed framework consists of different methods, such as secure mobility, resource allocation, provisioning, and prediction under blockchain technologies.

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