scholarly journals A novel approach for IoT tasks offloading in edge-cloud environments

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals A Novel Approach for IoT Tasks Offloading in Edge-Cloud Environments

2021 ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT task to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts the fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

2019 ◽  
Vol 9 (6) ◽  
pp. 4908
Author(s):  
Mohamed El Ghmary ◽  
Tarik Chanyour ◽  
Youssef Hmimz ◽  
Mohammed Ouçamah Cherkaoui Malki
Keyword(s):  
Mobile Devices ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Heuristic Solution ◽  
Huge Impact ◽  
Energy Constrained ◽  
Processing And Storage ◽  
Smart Mobile ◽  
Computational Resources ◽  
And Storage

<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>

scholarly journals Edge-Computing Architectures for Internet of Things Applications: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6441 ◽  
Author(s):  
Salam Hamdan ◽  
Moussa Ayyash ◽  
Sufyan Almajali
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Resource Limitation ◽  
Edge Computing ◽  
Iot Applications ◽  
Life Tasks ◽  
Iot Devices ◽  
Processing And Storage ◽  
Time Critical ◽  
And Storage

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

scholarly journals Towards Supporting IoT System Designers in Edge Computing Deployment Decisions

2021 ◽  
Author(s):  
◽  
Majid Ashouri
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Edge Computing ◽  
Use Case ◽  
Bandwidth Utilization ◽  
Quality Requirements ◽  
Network Layers ◽  
Supporting Tool ◽  
Computational Resources ◽  
And Storage

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

Understanding Decentralized and Dynamic Brokerage in Federated Cloud Environments

2012 ◽  
pp. 36-56 ◽  
Author(s):  
Nicolò Maria Calcavecchia ◽  
Antonio Celesti ◽  
Elisabetta Di Nitto
Keyword(s):  
Cloud Computing ◽  
Alternative Scenario ◽  
Advantages And Disadvantages ◽  
Computing Paradigm ◽  
Business Opportunities ◽  
Cloud Federation ◽  
Cloud Providers ◽  
Cloud Environments ◽  
Computational Resources ◽  
And Storage

The advent of the cloud computing paradigm offers different ways both to sell services and to exploit external computational resources according to a pay-per-use economic model. Nowadays, cloud computing clients can buy various form of IaaS, PaaS, and SaaS from cloud providers. Besides this form of pay-per-use, the perspective of cloud federation offers further business opportunities for small/medium providers which hold physical datacenters. Considering the cloud computing ecosystem, besides large cloud providers, smaller ones are also becoming popular even though their own virtualization infrastructures (i.e., deployed in their datacenters) cannot directly compete with the bigger market leaders. The result is that often small/medium clouds have to exploit the services of mega-providers in order to develop their business logic and their cloud-based services. To this regard, a possible future alternative scenario is represented by the promotion of cooperation among small/medium cloud providers, thus enabling the sharing of computational and storage resources. However, in order to achieve such an environment, several issues have to be addressed. One of these challenges is how to plan brokerage strategies allowing cloud providers to discover other providers for partnership establishment. This chapter focuses on different possible centralized and decentralized approaches in designing a brokerage strategy for cloud federation, analyzing their features, advantages and disadvantages.

Enabling Service Cache in Edge Clouds

2021 ◽  
Vol 2 (3) ◽  
pp. 1-24
Author(s):  
Chih-Kai Huang ◽  
Shan-Hsiang Shen
Keyword(s):  
Virtual Machines ◽  
Replacement Policy ◽  
Cache Replacement ◽  
Cache Replacement Policy ◽  
Average Latency ◽  
Hit Rates ◽  
Iot Devices ◽  
Multi Access ◽  
Computational Resources ◽  
The Cost

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.

scholarly journals Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4798
Author(s):  
Fangni Chen ◽  
Anding Wang ◽  
Yu Zhang ◽  
Zhengwei Ni ◽  
Jingyu Hua
Keyword(s):  
Energy Consumption ◽  
Transmission Rate ◽  
Edge Computing ◽  
Energy Harvest ◽  
Total System ◽  
Mobile Edge Computing ◽  
Weight Factor ◽  
Iot Devices ◽  
System Variables ◽  
The Relationship

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

scholarly journals Transparent CoAP Services to IoT Endpoints through ICN Operator Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1339 ◽  
Author(s):  
Hasan Islam ◽  
Dmitrij Lagutin ◽  
Antti Ylä-Jääski ◽  
Nikos Fotiou ◽  
Andrei Gurtov
Keyword(s):  
Storage Capacity ◽  
Communication Overhead ◽  
Full Potential ◽  
Core Network ◽  
Computation Complexity ◽  
State Management ◽  
Iot Devices ◽  
Novel Applications ◽  
And Storage ◽  
The Internet Of Things

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol which is intended to be used for constrained networks and devices. CoAP and its extensions (e.g., CoAP observe and group communication) provide the potential for developing novel applications in the Internet-of-Things (IoT). However, a full-fledged CoAP-based application may require significant computing capability, power, and storage capacity in IoT devices. To address these challenges, we present the design, implementation, and experimentation with the CoAP handler which provides transparent CoAP services through the ICN core network. In addition, we demonstrate how the CoAP traffic over an ICN network can unleash the full potential of the CoAP, shifting both overhead and complexity from the (constrained) endpoints to the ICN network. The experiments prove that the CoAP Handler helps to decrease the required computation complexity, communication overhead, and state management of the CoAP server.

scholarly journals Blockchain Processing Technique Based on Multiple Hash Chains for Minimizing Integrity Errors of IoT Data in Cloud Environments

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4679
Author(s):  
Yoon-Su Jeong
Keyword(s):  
Distributed Processing ◽  
Data Integrity ◽  
Processing Technique ◽  
Code Length ◽  
Process Data ◽  
Secure Storage ◽  
Hash Code ◽  
Linkage Information ◽  
Cloud Environments ◽  
Iot Devices

As IoT (Internet of Things) devices are diversified in the fields of use (manufacturing, health, medical, energy, home, automobile, transportation, etc.), it is becoming important to analyze and process data sent and received from IoT devices connected to the Internet. Data collected from IoT devices is highly dependent on secure storage in databases located in cloud environments. However, storing directly in a database located in a cloud environment makes it not only difficult to directly control IoT data, but also does not guarantee the integrity of IoT data due to a number of hazards (error and error handling, security attacks, etc.) that can arise from natural disasters and management neglect. In this paper, we propose an optimized hash processing technique that enables hierarchical distributed processing with an n-bit-size blockchain to minimize the loss of data generated from IoT devices deployed in distributed cloud environments. The proposed technique minimizes IoT data integrity errors as well as strengthening the role of intermediate media acting as gateways by interactively authenticating blockchains of n bits into n + 1 and n − 1 layers to normally validate IoT data sent and received from IoT data integrity errors. In particular, the proposed technique ensures the reliability of IoT information by validating hash values of IoT data in the process of storing index information of IoT data distributed in different locations in a blockchain in order to maintain the integrity of the data. Furthermore, the proposed technique ensures the linkage of IoT data by allowing minimal errors in the collected IoT data while simultaneously grouping their linkage information, thus optimizing the load balance after hash processing. In performance evaluation, the proposed technique reduced IoT data processing time by an average of 2.54 times. Blockchain generation time improved on average by 17.3% when linking IoT data. The asymmetric storage efficiency of IoT data according to hash code length is improved by 6.9% on average over existing techniques. Asymmetric storage speed according to the hash code length of the IoT data block was shown to be 10.3% faster on average than existing techniques. Integrity accuracy of IoT data is improved by 18.3% on average over existing techniques.

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