The nature of statistical learning theory
The nature of statistical learning theory
httperf—a tool for measuring web server performance
ACM SIGMETRICS Performance Evaluation Review
Formal requirements for virtualizable third generation architectures
Communications of the ACM
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Predicting Inter-Thread Cache Contention on a Chip Multi-Processor Architecture
HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Diagnosing performance overheads in the xen virtual machine environment
Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
The Architecture of Virtual Machines
Computer
Performance prediction based on inherent program similarity
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Scheduling I/O in virtual machine monitors
Proceedings of the fourth ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Characterization & analysis of a server consolidation benchmark
Proceedings of the fourth ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Towards modeling & analysis of consolidated CMP servers
ACM SIGARCH Computer Architecture News
Profiling and modeling resource usage of virtualized applications
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Enforcing performance isolation across virtual machines in Xen
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
VM3: Measuring, modeling and managing VM shared resources
Computer Networks: The International Journal of Computer and Telecommunications Networking
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Application classification through monitoring and learning of resource consumption patterns
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Tackling the challenges of server consolidation on multi-core systems
IISWC '10 Proceedings of the IEEE International Symposium on Workload Characterization (IISWC'10)
Towards Pay-As-You-Consume Cloud Computing
SCC '11 Proceedings of the 2011 IEEE International Conference on Services Computing
Adaptive Disk I/O Scheduling for MapReduce in Virtualized Environment
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Modeling virtualized applications using machine learning techniques
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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Modern datacenters consist of increasingly powerful hardware. Achieving high levels of utilization on this hardware often requires the execution of multiple concurrent workloads. Virtualization has emerged as an efficient means to isolate workloads by partitioning large physical resources using self-contained virtual machine images. Despite the many advantages, some challenges regarding performance isolation still need to be addressed. Unmanaged multiplexing of resource intensive workloads has the potential to cause unexpected variances in workload performance. In this paper, we address this issue using performance models based on the runtime characteristics of virtualized workloads. A set of resource intensive workloads is benchmarked with increasing degrees of multiplexing. Resource usage profiles are constructed using the metrics made available by the Xen hypervisor. Based on these profiles, performance degradation is predicted using several existing modeling techniques. In addition, we propose a novel approach using both the classification and regression capabilities of support vector machines. Application clustering is used to identify several application types with distinct performance profiles. Finally, we evaluate the developed performance models by introducing several new scheduling techniques. We demonstrate that the integration of these models in the scheduling logic can significantly improve the overall performance of multiplexed workloads.