Resource overbooking and application profiling in shared hosting platforms
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Dynamic placement for clustered web applications
Proceedings of the 15th international conference on World Wide Web
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Autonomous learning for efficient resource utilization of dynamic VM migration
Proceedings of the 22nd annual international conference on Supercomputing
A dollar from 15 cents: cross-platform management for internet services
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
On Strategies for Dynamic Resource Management in Virtualized Server Environments
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Coupled placement in modern data centers
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Application Performance Isolation in Virtualization
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
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Existing Virtual Machine (VM) management systems rely on host resource utilization metrics to allocate and schedule VMs. Many management systems only consolidate and migrate VMs based on hosts' CPU utilizations. However, the performance of delay-sensitive workloads, such as web services and online transaction processing, can be severely degraded by contention on numerous of the hosts' components. Current VM management systems typically use threshold based rules to decide when to migrate VMs, rather than using application-level performance. This means that they cannot easily provide application-level service level objective (SLO) guarantees. Providing SLO guarantees is even more difficult when considering that today's enterprise applications often consist of multiple VM tiers. In this paper we show how the performance of a multi-tiered VM application can be empirically captured, modeled and scaled. This allows our management system to guarantee application-level performance, despite variable host utilization and VM workload levels. Additionally, it can predict the performance of an application at host utilization levels that have not been previously observed. This is achieved by performing regression analysis on the previously observed values and scaling the applications performance model. This allows the performance of a VM to be predicted before it is migrated to or from a new host. We have found that by dynamically, rather than statically, allocating resources, average response time can be improved by 30%. Additionally, we found that resource allocations can be reduced by 20%, with no degradation in response time.