Dynamic VM migration: assessing its risks & rewards using a benchmark
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
TRACON: interference-aware scheduling for data-intensive applications in virtualized environments
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Autonomic Resource Management with Support Vector Machines
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Packet aggregation based network I/O virtualization for cloud computing
Computer Communications
Towards workload-aware virtual machine consolidation on cloud platforms
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Resource-freeing attacks: improve your cloud performance (at your neighbor's expense)
Proceedings of the 2012 ACM conference on Computer and communications security
Cost-Aware and SLO-Fulfilling Software as a Service
Journal of Grid Computing
Optimizing data migration for cloud-based key-value stores
Proceedings of the 21st ACM international conference on Information and knowledge management
A Survey on Database Performance in Virtualized Cloud Environments
International Journal of Data Warehousing and Mining
Modeling I/O interference for data intensive distributed applications
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Evaluating I/O aware network management for scientific workflows on networked clouds
NDM '13 Proceedings of the Third International Workshop on Network-Aware Data Management
Batch scheduling of consolidated virtual machines based on their workload interference model
Future Generation Computer Systems
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Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the in-depth understanding of the interference overheads in I/O channel in the driver domain and VMM.