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Capacity Management Approaches for Compute Clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science. (GIRD)
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches.

Place, publisher, year, edition, pages
Umeå: Umeå universitet , 2013. , 66 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2013:24
Keyword [en]
Cloud computing, Capacity Management
National Category
Computer Science
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-87242Libris ID: 15761199ISBN: 978-91-7459-788-2 (print)OAI: oai:DiVA.org:umu-87242DiVA: diva2:707752
Presentation
2013-12-19, Umeå universitet, Umeå, 10:00
Opponent
Supervisors
Available from: 2014-04-03 Created: 2014-03-25 Last updated: 2014-04-03Bibliographically approved
List of papers
1. A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling
Open this publication in new window or tab >>A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling
2013 (English)In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, ACM Press, 2013, Article no. 6- p.Conference paper, Published paper (Refereed)
Abstract [en]

An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.

Place, publisher, year, edition, pages
ACM Press, 2013
Keyword
Cloud computing, Autoscaling, Autonomous computing, Vertical elasticity, Horizontal elasticity
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-79781 (URN)10.1145/2494621.2494628 (DOI)978-1-4503-2172-3 (ISBN)
Conference
2013 ACM International Conference on Cloud and Autonomic Computing, CAC 2013, Miami, FL, United States, 5 August 2013 through 9 August 2013
Available from: 2013-09-02 Created: 2013-09-02 Last updated: 2015-12-15Bibliographically approved
2. Unifying cloud management: towards overall governance of business level objectives
Open this publication in new window or tab >>Unifying cloud management: towards overall governance of business level objectives
2011 (English)In: Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, IEEE Computer Society , 2011, -597 p.Conference paper, Published paper (Refereed)
Abstract [en]

We address the challenge of providing unified cloud resource management towards an overall business level objective, given the multitude of managerial tasks to be performed and the complexity of any architecture to support them. Resource level management tasks include elasticity control, virtual machine and data placement, autonomous fault management, etc, which are intrinsically difficult problems since services normally have unknown lifetime and capacity demands that varies largely over time. To unify the management of these problems, (for optimization with respect to some higher level business level objective, like optimizing revenue while breaking no more than a certain percentage of service level agreements)becomes even more challenging as the resource level managerial challenges are far from independent. After providing the general problem formulation, we review recent approaches taken by the research community, including mainly general autonomic computing technology for large-scale environments and resource level management tools equipped with some business oriented or otherwise qualitative features. We propose and illustrate a policy-driven approach where a high-level management system monitors overall system and services behavior and adjusts lower level policies (e.g., thresholds for admission control, elasticity control, server consolidation level, etc) for optimization towards the measurable business level objectives.

Place, publisher, year, edition, pages
IEEE Computer Society, 2011
Keyword
Cloud governance, autonomic computing, policy-driven management
National Category
Computer Science
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-40343 (URN)10.1109/CCGrid.2011.65 (DOI)978-1-4577-0129-0 (ISBN)
Conference
The 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Newport Beach, CA, USA, May 23 - 26, 2011
Available from: 2011-02-22 Created: 2011-02-22 Last updated: 2015-12-15Bibliographically approved
3. Peer to peer resource management for cloud data centers
Open this publication in new window or tab >>Peer to peer resource management for cloud data centers
(English)Manuscript (preprint) (Other academic)
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-87480 (URN)
Available from: 2014-04-02 Created: 2014-04-02 Last updated: 2014-04-03Bibliographically approved

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Capacity Management Approaches for Compute Clouds(476 kB)720 downloads
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