Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractOccupational exposure and indoor air quality monitoring in a composting facility    Next AbstractA novel needle trap device with single wall carbon nanotubes sol-gel sorbent packed for sampling and analysis of volatile organohalogen compounds in air »

PeerJ Comput Sci


Title:A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm
Author(s):Heidari A; Jafari Navimipour N;
Address:"Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran. Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan"
Journal Title:PeerJ Comput Sci
Year:2021
Volume:20210510
Issue:
Page Number:e539 -
DOI: 10.7717/peerj-cs.539
ISSN/ISBN:2376-5992 (Electronic) 2376-5992 (Linking)
Abstract:"Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones' repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method"
Keywords:Cloud computing Discovery Inverted ant colony Resource Sla Service;
Notes:"PubMed-not-MEDLINEHeidari, Arash Jafari Navimipour, Nima eng 2021/06/05 PeerJ Comput Sci. 2021 May 10; 7:e539. doi: 10.7717/peerj-cs.539. eCollection 2021"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024