Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Memory resource management in VMware ESX server
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
CHAMELEON: a self-evolving, fully-adaptive resource arbitrator for storage systems
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Model-based resource provisioning in a web service utility
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Queue - Virtualization
Queue - Virtualization
A dollar from 15 cents: cross-platform management for internet services
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
CARVE: A Cognitive Agent for Resource Value Estimation
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Profiling and modeling resource usage of virtualized applications
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
PARDA: proportional allocation of resources for distributed storage access
FAST '09 Proccedings of the 7th conference on File and storage technologies
VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Fingerprinting the datacenter: automated classification of performance crises
Proceedings of the 5th European conference on Computer systems
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Decentralized deduplication in SAN cluster file systems
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
JustRunIt: experiment-based management of virtualized data centers
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Virtualization: Blessing or Curse?
Queue - Security
mClock: handling throughput variability for hypervisor IO scheduling
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Utilization and SLO-Based control for dynamic sizing of resource partitions
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Self-adaptive and sensitivity-aware QoS modeling for the cloud
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Black box scheduling for resource intensive virtual machine workloads with interference models
Future Generation Computer Systems
Hi-index | 0.00 |
With the growing adoption of virtualized datacenters and cloud hosting services, the allocation and sizing of resources such as CPU, memory, and I/O bandwidth for virtual machines (VMs) is becoming increasingly important. Accurate performance modeling of an application would help users in better VM sizing, thus reducing costs. It can also benefit cloud service providers who can offer a new charging model based on the VMs' performance instead of their configured sizes. In this paper, we present techniques to model the performance of a VM-hosted application as a function of the resources allocated to the VM and the resource contention it experiences. To address this multi-dimensional modeling problem, we propose and refine the use of two machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). We evaluate these modeling techniques using five virtualized applications from the RUBiS and Filebench suite of benchmarks and demonstrate that their median and 90th percentile prediction errors are within 4.36% and 29.17% respectively. These results are substantially better than regression based approaches as well as direct applications of machine learning techniques without our refinements. We also present a simple and effective approach to VM sizing and empirically demonstrate that it can deliver optimal results for 65% of the sizing problems that we studied and produces close-to-optimal sizes for the remaining 35%.