Proceedings Article10.1145/3287921.3287984
An Evolutionary Algorithm for Solving Task Scheduling Problem in Cloud-Fog Computing Environment
Huynh Thi Thanh Binh,Do Bao Son,Pham Anh Duc,Binh Minh Nguyen +3 more
- 06 Dec 2018
- pp 397-404
67
TL;DR: TCaS - an evolutionary algorithm to deal with Bag-of-Tasks application in cloud-fog computing environment is proposed, which performs much better than BLA in execution time, simultaneously, satisfies user's requirement.
read more
Abstract: Recently, IoT (Internet of Things) has grown steadily, which generates a tremendous amount of data and puts pressure on the cloud computing infrastructures. Fog computing architecture is proposed to be the next generation of the cloud computing to meet the requirements of the IoT network. One of the big challenges of fog computing is resource management and operating function, as task scheduling, which guarantees a high-performance and cost-effective service. We propose TCaS - an evolutionary algorithm to deal with Bag-of-Tasks application in cloud-fog computing environment. By addressing the tasks in this distributed system, our proposed approach aimed at achieving the optimal tradeoff between the execution time and operating costs. We verify our proposal by extensive simulation with various size of data set, and the experimental results demonstrate that our scheduling algorithm outperforms 38.6% Bee Life Algorithm (BLA) in time-cost tradeoff, especially, performs much better than BLA in execution time, simultaneously, satisfies user's requirement.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization
TL;DR: Two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations.
A Novel Bio-Inspired Hybrid Algorithm (NBIHA) for Efficient Resource Management in Fog Computing
TL;DR: The evaluation results show that the proposed approach (NBIHA) shows promising results in terms of energy consumption, execution time, and average response time in comparison to the state-of-the-art scheduling techniques.
160
Task Offloading in Fog Computing for Using Smart Ant Colony Optimization
Amit Kishor,Chinmay Chakarbarty +1 more
TL;DR: Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA.
132
A survey on computation offloading and service placement in fog computing-based IoT
TL;DR: This paper surveys the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner to improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay.
120
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
TL;DR: In this paper, the authors provide an overview of research problems in cloud computing and edge computing and recent progresses in addressing them with the help of Computational Intelligence (CI) techniques, with the aim of offering insights to the readers and motivating new research directions.
References
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
TL;DR: In this paper, the authors propose a simulator, called iFogSim, to model IoT and fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost.
1.4K
Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption
TL;DR: By sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.
783
•Book
Nonlinear integer programming
Duan Li,Xiaoling Sun +1 more
- 01 Jan 2006
TL;DR: This book systemically investigates theory and solution methodologies for general nonlinear integer programming, and provides a timely and comprehensive summary of the theoretical and algorithmic development in the last 30 years on this topic.
421
Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System
TL;DR: Fog computation and MCPS are integrated to build fog computing supported MCPS (FC-MCPS), and an LP-based two-phase heuristic algorithm is proposed that produces near optimal solution and significantly outperforms a greedy algorithm.
397