Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Quantifying the performance isolation properties of virtualization systems
Proceedings of the 2007 workshop on Experimental computer science
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Application Performance Isolation in Virtualization
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
Resource management for isolation enhanced cloud services
Proceedings of the 2009 ACM workshop on Cloud computing security
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee
MASCOTS '10 Proceedings of the 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Runtime measurements in the cloud: observing, analyzing, and reducing variance
Proceedings of the VLDB Endowment
Initial Findings for Provisioning Variation in Cloud Computing
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Enhancement of Xen's scheduler for MapReduce workloads
Proceedings of the 20th international symposium on High performance distributed computing
The Computer Journal
Autonomic SLA-Driven Provisioning for Cloud Applications
CCGRID '11 Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Pesto: online storage performance management in virtualized datacenters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Proceedings of the 2nd ACM Symposium on Cloud Computing
Variations in Performance and Scalability When Migrating n-Tier Applications to Different Clouds
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Future Generation Computer Systems
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Autonomic Resource Management with Support Vector Machines
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
IEEE Transactions on Parallel and Distributed Systems
URL: A unified reinforcement learning approach for autonomic cloud management
Journal of Parallel and Distributed Computing
The seven deadly sins of cloud computing research
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
Hi-index | 0.00 |
Hosting a multi-tier application using an Infrastructure-as-a-Service (IaaS) cloud requires deploying components of the application stack across virtual machines (VMs) to provide the application's infrastructure while considering factors such as scalability, fault tolerance, performance and deployment costs (# of VMs). This paper presents results from an empirical study which investigates implications for application performance and resource requirements (CPU, disk and network) resulting from how multi-tier applications are deployed to IaaS clouds. We investigate the implications of: (1) component placement across VMs, (2) VM memory size, (3) VM hypervisor type (KVM vs. Xen), and (4) VM placement across physical hosts (provisioning variation). All possible deployment configurations for two multi-tier application variants are tested. One application variant was computationally bound by the application middleware, the other bound by geospatial queries. The best performing deployments required as few as 2 VMs, half the number required for VM-level service isolation, demonstrating potential cost savings when components can be consolidated. Resource utilization (CPU time, disk I/O, and network I/O) varied with component deployment location, VM memory allocation, and the hypervisor used (Xen or KVM) demonstrating how application deployment decisions impact required resources. Isolating application components using separate VMs produced performance overhead of ~1%-2%. Provisioning variation of VMs across physical hosts produced overhead up to 3%. Relationships between resource utilization and performance were assessed using multiple linear regression to develop a model to predict application deployment performance. Our model explained over 84% of the variance and predicted application performance with mean absolute error of only ~0.3 s with CPU time, disk sector reads, and disk sector writes serving as the most powerful predictors of application performance.