NDLI: Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Content Provider	Springer Nature Link
Author	Gari, Carlos García Mateos, Cristian Pacini, Elina
Copyright Year	2014
Abstract	Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.
Ending Page	522
Page Count	28
Starting Page	495
File Format	PDF
ISSN	0010485X
e-ISSN	14365057
Journal	Computing
Issue Number	5
Volume Number	98
Language	English
Publisher	Springer Vienna
Publisher Date	2014-06-13
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ant Colony Optimization Problem solving (heuristics, search strategies, etc.) Cloud Computing Distributed systems Software Engineering Performance evaluation; queueing; scheduling Computer Application in Administrative Data Processing Computer Science Artificial Intelligence (incl. Robotics) VM Scheduling Information Systems Applications (incl. Internet) Genetic Algorithms Computer Communication Networks Particle Swarm Optimization
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computational Mathematics Numerical Analysis Computer Science Applications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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