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Hecatonchire: Enabling Multi-Host Virtual Machines by Resource Aggregation and Pooling
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distribuerade system)
SAP Research Belfast.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud and Grid Computing)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud and Grid Computing)
2014 (English)Report (Other academic)
Abstract [en]

Vertical elasticity, or scale-up of individual virtual ma- chines is hard to perform in today’s cloud environments due to limitations in the amount of hardware resources available in single servers. We propose a novel approach that allows aggregation of memory, compute and I/O resources from multiple physical machines in resource pools which in turn are used to seamlessly provision vir- tual machines with the right amount of resources. We present our architecture and highlight key functionality such as transparent and resilient memory aggregation and fast live migration. Our approach is validated by a demonstration using benchmarks and a real-world big- data application. Performance results indicate a very low overhead in using aggregated memory as well as a sig- nificant improvement in live migration performance.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University , 2014. , 6 p.
Series
Report / UMINF, ISSN 0348-0542 ; 14.11
Keyword [en]
Virtualization, Operating Systems, Disaggregation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-87902OAI: oai:DiVA.org:umu-87902DiVA: diva2:712161
Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2014-04-14Bibliographically approved
In thesis
1. Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
Open this publication in new window or tab >>Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A key aspect of cloud computing is the promise of infinite, scalable resources, and that cloud services should scale up and down on demand. This thesis investigates methods for dynamic resource allocation and management of services in cloud datacenters, introducing new approaches as well as improvements to established technologies.Virtualization is a key technology for cloud computing as it allows several operating system instances to run on the same Physical Machine, PM, and cloud services normally consists of a number of Virtual Machines, VMs, that are hosted on PMs. In this thesis, a novel virtualization approach is presented. Instead of running each PM isolated, resources from multiple PMs in the datacenter are disaggregated and exposed to the VMs as pools of CPU, I/O and memory resources. VMs are provisioned by using the right amount of resources from each pool, thereby enabling both larger VMs than any single PM can host as well as VMs with tailor-made specifications for their application. Another important aspect of virtualization is live migration of VMs, which is the concept moving VMs between PMs without interruption in service. Live migration allows for better PM utilization and is also useful for administrative purposes. In the thesis, two improvements to the standard live migration algorithm are presented, delta compression and page transfer reordering. The improvements can reduce migration downtime, i.e., the time that the VM is unavailable, as well as the total migration time. Postcopy migration, where the VM is resumed on the destination before the memory content is transferred is also studied. Both userspace and in-kernel postcopy algorithms are evaluated in an in-depth study of live migration principles and performance.Efficient mapping of VMs onto PMs is a key problem for cloud providers as PM utilization directly impacts revenue. When services are accepted into a datacenter, a decision is made on which PM should host the service VMs. This thesis presents a general approach for service scheduling that allows for the same scheduling software to be used across multiple cloud architectures. A number of scheduling algorithms to optimize objectives like revenue or utilization are also studied. Finally, an approach for continuous datacenter consolidation is presented. As VM workloads fluctuate and server availability varies any initial mapping is bound to become suboptimal over time. The continuous datacenter consolidation approach adjusts this VM-to-PM mapping during operation based on combinations of management actions, like suspending/resuming PMs, live migrating VMs, and suspending/resuming VMs. Proof-of-concept software and a set of algorithms that allows cloud providers to continuously optimize their server resources are presented in the thesis.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2014. 26 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2014:09
Keyword
Cloud computing, virtualization, distributed infrastructure, live migration, scheduling
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-87904 (URN)978-91-7601-038-9 (ISBN)
Public defence
2014-05-06, Naturvetarhuset, N320, Umeå universitet, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2014-04-15 Created: 2014-04-14 Last updated: 2014-04-14Bibliographically approved

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