Computation Offloading and Scheduling in Edge-Fog Cloud Computing

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.

Scheduling of fog networks with optimized knapsack by symbiotic organisms search

Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms

Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time

BRAIN Journal - High Performance Data mining by Genetic Neural Network

ABSTRACT Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well in comparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced

BRAIN Journal - High Performance Data mining by Genetic Neural Network

<div><i>Abstract</i></div><div><br></div><div>Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well in</div><div>comparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.</div><div><br></div><div><b>Find more at:</b></div><div><b>https://www.edusoft.ro/brain/index.php/brain/article/view/421</b><br></div