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Resource Management in Cloud Computing and Distributed Computing

Resource Management in Cloud Computing and Distributed Computing. Shaquille Wilkins Zak Lowman. Contents. Introduction Resource Management Systems Architectures Risk and Concerns Solutions and Techniques Conclusion. Introduction.

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Resource Management in Cloud Computing and Distributed Computing

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  1. Resource Management in Cloud Computing and Distributed Computing Shaquille Wilkins Zak Lowman

  2. Contents • Introduction • Resource Management Systems • Architectures • Risk and Concerns • Solutions and Techniques • Conclusion

  3. Introduction • Cloud Computing technology offers a new concept in computing in which a data can be stored, accessed and altered at alternate execution times on various computers that are all synced.

  4. Resource Management Systems • The brain of a cloud.  • Resource manager: • Local machines • Servers • Scheduling • Routing controllers

  5. Architectures • Centralization • Grid • Cell Structure

  6. GRID Types • Computational • Numerals • Data • Data warehousing • Service

  7. Risk and Concerns • Data Storage – How much data can we store? • How to allocate the data? • Will it be fast enough? • What makes it better? • What about performance isolation?

  8. Solutions and Techniques • Hierarchical Scaling • Focuses on cluster managementRMSs are built on top of each other in order to reach scale • Flat Scaling • Focuses on efficiency checksConfigured to have the most output speed possible. Decisions are made using data collected and aggregated over the large number of hosts and VMs

  9. Examples • NetSuite'sOpenAir • Invoice, employee time sheet, and expense management.  • Dashboard feedback. • Global. • Citrix • Desktop, Application and Server support.  • Centralizes applications in data warehouse. • On-demand resources. • Scalability.

  10. Conclusion Cloud computing is one of the fastest growing technologies of our time. With its expansion, it requires modified protocols and techniques to complete more complicated tasks RMS is

  11. References • [1] VMware http://labs.vmware.com/publications/cloud- scale • [2] Ajay Gulati, GaneshaShanmuganathan, and Anne Holler http://www.usenix.org/event/hotcloud11/tec h/final_files/Gulati.pdf • [3] Cynthia K. Westhttp://wwpi.com/index.php?option=com_co ntent&view=article&id=7576:case-study- project-and-resource-management-cloud- computing-with-project-insight- &catid=230:cloud- computing&Itemid=2701186 • [4] VMware Distributed Resource Scheduler - Dynamic Resource Balancing, 2011. • http://vmware.com/products/drs. • [5] VMware Distributed Resource Scheduler - Dynamic Resource Balancing, 2011. • http://vmware.com/products/drs. • [6] Klaus Krauter, RajkumarBuyyahttp://www.buyya.com/papers/gridtaxon omy.pdf • [7] Chee Shin Yeo and RajkumarBuyyahttp://www.buyya.com/papers/txnomy_cl usterms.pdf • [8] Ignacio M. Llorentehttp://cloudcomputing.sys- con.com/node/856815

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