Enforcing performance isolation across virtual machines in Xen
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
A Performance Isolation Algorithm for Shared Virtualization Storage System
NAS '09 Proceedings of the 2009 IEEE International Conference on Networking, Architecture, and Storage
Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Co-management of power and performance in virtualized distributed environments
GPC'11 Proceedings of the 6th international conference on Advances in grid and pervasive computing
TRACON: interference-aware scheduling for data-intensive applications in virtualized environments
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Queue - Virtualization
The Journal of Supercomputing
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The use of virtualization technology (VT) has become widespread in modern datacenters and Clouds in recent years. In spite of their many advantages, such as provisioning of isolated execution environments and migration, current implementations of VT do not provide effective performance isolation between virtual machines (VMs) running on a physical machine (PM) due to workload interference of VMs. Generally, this interference is due to contention on physical resources that impacts performance in different workload configurations. To investigate the impacts of this interference, we formalize the concept of interference for a consolidated multi-tenant virtual environment. This formulation, represented as a mathematical model, can be used by schedulers to estimate the interference of a consolidated virtual environment in terms of the processing and networking workloads of running VMs, and the number of consolidated VMs. Based on the proposed model, we present a novel batch scheduler that reduces the interference of running tenant VMs by pausing VMs that have a higher impact on proliferation of the interference. The scheduler achieves this by selecting a set of VMs that produce the least interference using a 0-1 knapsack problem solver. The selected VMs are allowed to run and other VMs are paused. Users are not troubled by the pausing and resumption of VMs for a short time because the scheduler has been designed for the execution of batch type applications such as scientific applications. Evaluation results on the makespan of VMs executed under the control of our scheduler have shown nearly 33% improvement in the best case and 7% improvement in the worst case compared to the case in which all VMs are running concurrently. In addition, the results show that our scheduling algorithm outperforms serial and random scheduling of VMs as well.