Abstract-While workload colocation is a necessity to increase energy efficiency of contemporary multicore hardware, it also increases the risk of performance anomalies due to workload interference. Pinning certain workloads to a subset of CPUs is a simple approach to increasing workload isolation, but its effect depends on workload type and system architecture. Apart from common sense guidelines, the effect of pinning has not been extensively studied so far. In this paper we study the impact of CPU pinning on performance interference and energy efficiency for pairs of colocated workloads. Besides various combinations of workloads, virtualization and resource isolation, we explore the effects of pinning depending on the level of background load. The presented results are based on more than 1000 experiments carried out on an Intel-based NUMA system, with all power management features enabled to reflect real-world settings. We find that less common CPU pinning configurations improve energy efficiency at partial background loads, indicating that systems hosting colocated workloads could benefit from dynamic CPU pinning based on CPU load and workload type.

The Java Virtual Machine (JVM) has become an execution platform targeted by many programming languages. However, unlike with Java, a statically-typed language, the performance of the JVM and its Just-In-Time (JIT) compiler with dynamically-typed languages lags behind purpose-built language-specific JIT compilers. In this paper, we aim to contribute to the understanding of the workloads imposed on the JVM by dynamic languages. We use various metrics to characterize the dynamic behavior of a variety of programs written in three dynamic languages (Clojure, Python, and Ruby) executing on the JVM. We identify the differences with respect to Java, and briefly discuss their implications.

Hardware virtualization is the prevalent way to share data centers among different tenants. In this paper we present a large scale workload characterization study that aims to a better understanding of the state-of-the-practice, i.e., how data centers in the private cloud are used by their customers, how physical resources are shared among different tenants using virtualization, and how virtualization technologies are actually employed. Our study focuses on all corporate data centers of a major infrastructure provider that are geographically dispersed across the entire globe and reports on their observed usage across a 19-day period. We especially focus on how virtual machines are deployed across different physical resources with an emphasis on processors and memory, focusing on resource sharing and usage of physical resources, virtual machine life cycles, and migration patterns and frequencies. Our study illustrates that there is a huge tendency in over provisioning resources while being conservative to the several possibilities opened up by virtualization (e.g., migration and co-location), showing tremendous potential for the development of policies aiming to reduce data center operational costs.

Compared to functional unit testing, automated performance testing is difficult, partially because correctness criteria are more difficult to express for performance than for functionality. Where existing approaches rely on absolute bounds of the execution time, we aim to express assertions on code performance in relative, hardware-independent terms. To this end, we introduce Stochastic Performance Logic (SPL), which allows making statements about relative method performance. Since SPL interpretation is based on statistical tests applied to performance measurements, it allows (for a special class of formulas) calculating the minimum probability at which a particular SPL formula holds. We prove basic properties of the logic and present an algorithm for SAT-solver-guided evaluation of SPL formulas, which allows optimizing the number of performance measurements that need to be made. Finally, we propose integration of SPL formulas with Java code using higher-level performance annotations, for performance testing and documentation purposes.