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Using a Multi-Agent System and Artificial Intelligence for Monitoring and Improving the Cloud Performance and Security
Cracow University of Technology, Poland.
Cracow University of Technology, Poland.
Research and Academic Computer Network (NASK), Poland.
Linnaeus University, Faculty of Technology, Department of Computer Science. (Parallel Computing)
2017 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115Article in journal (Refereed) In press
Abstract [en]

Cloud Computing is one of the most intensively developed solutions  for large-scale distributed processing. Effective use of such environments, management of their high complexity and ensuring appropriate levels of Quality of Service (QoS) require advanced monitoring systems. Such monitoring systems have to support the scalability, adaptability and reliability of Cloud. Most of existing monitoring systems  do not incorporate any Artificial  Intelligence (AI) algorithms for supporting the change inside the task stream or environment itself. They  focus  only on monitoring or enabling the control of the system as a part of a separated service. An effective monitoring system for the Cloud environment should gather information about all stages of tasks processing and should actively control the monitored environment. In this paper, we present a novel Multi-Agent System based Cloud Monitoring (MAS-CM) model that supports the performance and security of tasks gathering, scheduling and execution processes in large-scale service-oriented environments. Such models are explicitly designed to control the performance and security objectives of the environment. In our work, we focus on prevention of unauthorized task injection and modification, optimization of scheduling process and maximization of resource usage.We evaluate the effectiveness of MAS-CM empirically using an evolutionary driven implementation of Independent Batch Scheduler and FastFlow framework. The obtained results demonstrate the effectiveness of the proposed approach and the performance improvement.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Cloud Computing, Cloud Monitoring, Multi-Agent Systems, Cloud Security, Genetic Algorithms, Artifficial Neural Networks, Independent Batch Scheduling
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-65567DOI: 10.1016/j.future.2017.05.046OAI: oai:DiVA.org:lnu-65567DiVA: diva2:1111902
Funder
EU, Horizon 2020
Available from: 2017-06-19 Created: 2017-06-19 Last updated: 2017-06-28

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S0167739X17310531

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf