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. 

scholarly journals A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

2021 ◽  
Vol 18 (22) ◽  
pp. 413
Author(s):  
Ismail Zaharaddeen Yakubu ◽  
Lele Muhammed ◽  
Zainab Aliyu Musa ◽  
Zakari Idris Matinja ◽  
Ilya Musa Adamu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fog Computing ◽  
Dynamic Resource Allocation ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Agent Based ◽  
Edge Network ◽  
Dynamic Resource ◽  
Iot Devices

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery. HIGHLIGHTS The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one GRAPHICAL ABSTRACT

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.

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

scholarly journals Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1460
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Yonggeun Hong ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Objective Function ◽  
Smart Home ◽  
Edge Computing ◽  
Rule Based ◽  
Home Appliances ◽  
Iot Devices ◽  
Optimization Mechanism

In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.

scholarly journals An efficient population-based multi-objective task scheduling approach in fog computing systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zahra Movahedi ◽  
Bruno Defude ◽  
Amir mohammad Hosseininia
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Rapid Development ◽  
Fog Computing ◽  
Scheduling Problem ◽  
Distributed Resources ◽  
Iot Applications ◽  
Whale Optimization ◽  
Iot Devices

AbstractWith the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

scholarly journals An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4527
Author(s):  
Abid Ali ◽  
Muhammad Munawar Iqbal ◽  
Harun Jamil ◽  
Faiza Qayyum ◽  
Sohail Jabbar ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Task Scheduling ◽  
Time Management ◽  
Mobile Cloud Computing ◽  
Fog Computing ◽  
Mobile Cloud ◽  
Computational Performance ◽  
Task Offloading

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

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