Performance Interference of Memory Thrashing in Virtualized Cloud Environments: A Study of Consolidated n-Tier Applications

Modern datacenters employ server virtualization and consolidation to reduce the cost of operation and to maximize profit. However, interference among consolidated virtual machines (VMs) has barred mission-critical applications due to unpredictable performance. Through extensive measurements of RUBBoS n-tier benchmark, we found a major source of performance unpredictability: the memory thrashing caused by VM consolidation can reduce the system throughput by 46% although memory was not over-committed. On a physical host with 4 consolidated VMs, we observed two distinct operational modes during a typical RUBBoS benchmark experiment. Over the first half of run-time session we found frequent CPU IOwait causing very long response time requests even though the system is under read-only CPU intensive workload, however, the latter half showed no such CPU abnormalities (IOwait). Using ElbaLens - a lightweight tracing tool, we conducted fine-grain analyses at time granularities as short as 50ms and found that the abnormal IOwait is caused by transient memory thrashing among consolidated VMs. The abnormal IOwait induces queue overflows that propagate through the entire n-tier system, resulting in very long response time requests due to frequent TCP retransmissions. We provide three practical techniques such as VM migration, memory reallocation, soft resource reallocation and show that they can mitigate the effects of performance interference among consolidated VMs.

[1]  Calton Pu,et al.  Challenges and Opportunities in Consolidation at High Resource Utilization: Non-monotonic Response Time Variations in n-Tier Applications , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[2]  Eric Bouillet,et al.  Efficient resource provisioning in compute clouds via VM multiplexing , 2010, ICAC '10.

[3]  Prashant J. Shenoy,et al.  Provisioning multi-tier cloud applications using statistical bounds on sojourn time , 2012, ICAC '12.

[4]  Xiaohong Jiang,et al.  Two Optimization Mechanisms to Improve the Isolation Property of Server Consolidation in Virtualized Multi-core Server , 2010, 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC).

[5]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

[6]  Amin Vahdat,et al.  Enforcing Performance Isolation Across Virtual Machines in Xen , 2006, Middleware.

[7]  Calton Pu,et al.  Variations in Performance Measurements of Multi-core Processors: A Study of n-Tier Applications , 2013, 2013 IEEE International Conference on Services Computing.

[8]  Calton Pu,et al.  The Impact of Soft Resource Allocation on n-Tier Application Scalability , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

[9]  Akshat Verma,et al.  CloudBridge: on integrated hardware-software consolidation , 2011, PERV.

[10]  Ching-Hsien Hsu,et al.  Automatic Memory Control of Multiple Virtual Machines on a Consolidated Server , 2017, IEEE Transactions on Cloud Computing.

[11]  Calton Pu,et al.  IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[12]  George Varghese,et al.  Difference engine , 2010, OSDI.

[13]  Calton Pu,et al.  An Analysis of Performance Interference Effects in Virtual Environments , 2007, 2007 IEEE International Symposium on Performance Analysis of Systems & Software.

[14]  Ching-Hsien Hsu,et al.  Optimizing Energy Consumption with Task Consolidation in Clouds , 2014, Inf. Sci..

[15]  Calton Pu,et al.  Lightning in the Cloud: A Study of Transient Bottlenecks on n-Tier Web Application Performance , 2014, TRIOS.

[16]  Calton Pu,et al.  Lightning in the cloud: a study of very short bottlenecks on n-tierweb application performance , 2014 .

[17]  Zhenhuan Gong,et al.  PAC: Pattern-driven Application Consolidation for Efficient Cloud Computing , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[18]  Lizy Kurian John,et al.  Performance impact of virtual machine placement in a datacenter , 2012, 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC).

[19]  Ishan Banerjee,et al.  Memory Overcommitment in the ESX Server , 2014 .

[20]  Calton Pu,et al.  Revisiting Performance Interference among Consolidated n-Tier Applications: Sharing is Better Than Isolation , 2013, 2013 IEEE International Conference on Services Computing.

[21]  Calton Pu,et al.  Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[22]  Xiaomin Zhang,et al.  Characterization & analysis of a server consolidation benchmark , 2008, VEE '08.