Performance Measurement and Interference Profiling in Multi-tenant Clouds

The ongoing rush for cloud-based services by small, medium, and large-scale organizations to reduce operational cost and to have more flexibility in the deployment and management of business applications cannot be overemphasized. However, the performance of in-cloud applications is often quite disappointing and unpredictable. Cloud users often perceive the sub optimal and unpredictable performance as anomalies as it is hard to conduct capacity planning based on such unreliable measurements. Performance interference due to resource sharing has been well studied in literature. Representative work includes the study of shared CPU caches, memory bandwidth, hard disks, network bandwidth, and the fair allocation CPU time. There lacks a comprehensive understanding of the complex interplay for shared resource under contention such as CPU. In this research work, we focus on predicting application performance by establishing a mathematical relationship between the high-level performance and the low-level CPU multiplexing. We design a synthetic workload with controllable CPU demands to emulate interference workloads in the cloud. We begin our measurements in a controlled environment to study the impact of CPU allocation on application performance. Based on the results from our experiments, we established the interdependency between CPU steal time, and application performance, and confirms that the percentage of CPU steal time influence application performance, even when workloads of equal parameters were submitted for processing on the same system platform. Our experimental results were evaluated against similar experimental results we performed on Amazon EC2 m3.medium model instance. We confirmed that the workload runtime duration on Amazon EC2 m3. medium model instance are significantly been impacted by high CPU steal time percentage due to interference from co-tenants. Therefore, the workload runtime slowdown percentage on submitted workloads on Amazon EC2.medium model ins- ance is the hidden cost incurred by Cloud subscribers in term of time lost. Cloud service providers should pay close attention to CPU steal time percentage as part of system optimization efforts on Xen based cloud platform. We present Multi-tenant Performance Measurement and Interference Profiling system, a Xen based multi-tenant cloud environment designed for performance measurement and profiling.