Towards an Adaptive OS Noise Mitigation Technique for Microbenchmarking on Apple Ipad Devices

This study investigates levels of Operating System (OS) noise on Apple iPad mobile devices. OS noise causes variations in application performance that interfere with microbenchmark results. OS noise manifests in collected data through extreme outliers and variations in skewness. Using our collected data, we develop an iterative, semi-automated outlier removal process for Apple iPad OS noise profiles. The profiles generated by outlier removal represent the first step toward an adaptive noise mitigation technique, which presents opportunities for use in microbenchmarking across other mobile platforms

Using a Microbenchmark to Compare Function as a Service Solutions

The Function as a Service (FaaS) subtype of serverless computing provides the means for abstracting away from servers on which developed software is meant to be executed. It essentially offers an event-driven and scalable environment in which billing is based on the invocation of functions and not on the provisioning of resources. This makes it very attractive for many classes of applications with bursty workload. However, the terms under which FaaS services are structured and offered to consumers uses mechanisms like GB–seconds (that is, X GigaBytes of memory used for Y seconds of execution) that differ from the usual models for compute resources in cloud computing. Aiming to clarify these terms, in this work we develop a microbenchmark that we use to evaluate the performance and cost model of popular FaaS solutions using well known algorithmic tasks. The results of this process show a field still very much under development, and justify the need for further extensive benchmarking of these services

Software Microbenchmarking in the Cloud. How Bad is it Really?

Rigorous performance engineering traditionally assumes measuring on bare-metal environments to control for as many confounding factors as possible. Unfortunately, some researchers and practitioners might not have access, knowledge, or funds to operate dedicated performance-testing hardware, making public clouds an attractive alternative. However, shared public cloud environments are inherently unpredictable in terms of the system performance they provide. In this study, we explore the effects of cloud environments on the variability of performance test results and to what extent slowdowns can still be reliably detected even in a public cloud. We focus on software microbenchmarks as an example of performance tests and execute extensive experiments on three different well-known public cloud services (AWS, GCE, and Azure) using three different cloud instance types per service. We also compare the results to a hosted bare-metal offering from IBM Bluemix. In total, we gathered more than 4.5 million unique microbenchmarking data points from benchmarks written in Java and Go. We find that the variability of results differs substantially between benchmarks and instance types (by a coefficient of variation from 0.03% to > 100%). However, executing test and control experiments on the same instances (in randomized order) allows us to detect slowdowns of 10% or less with high confidence, using state-of-the-art statistical tests (i.e., Wilcoxon rank-sum and overlapping bootstrapped confidence intervals). Finally, our results indicate that Wilcoxon rank-sum manages to detect smaller slowdowns in cloud environments

Exploring Fully Offloaded GPU Stream-Aware Message Passing

Modern heterogeneous supercomputing systems are comprised of CPUs, GPUs, and high-speed network interconnects. Communication libraries supporting efficient data transfers involving memory buffers from the GPU memory typically require the CPU to orchestrate the data transfer operations. A new offload-friendly communication strategy, stream-triggered (ST) communication, was explored to allow offloading the synchronization and data movement operations from the CPU to the GPU. A Message Passing Interface (MPI) one-sided active target synchronization based implementation was used as an exemplar to illustrate the proposed strategy. A latency-sensitive nearest neighbor microbenchmark was used to explore the various performance aspects of the implementation. The offloaded implementation shows significant on-node performance advantages over standard MPI active RMA (36%) and point-to-point (61%) communication. The current multi-node improvement is less (23% faster than standard active RMA but 11% slower than point-to-point), but plans are in progress to purse further improvements.Comment: 12 pages, 17 figure

Master of Science

thesisEfficient movement of massive amounts of data over high-speed networks at high throughput is essential for a modern-day in-memory storage system. In response to the growing needs of throughput and latency demands at scale, a new class of database systems was developed in recent years. The development of these systems was guided by increased access to high throughput, low latency network fabrics, and declining cost of Dynamic Random Access Memory (DRAM). These systems were designed with On-Line Transactional Processing (OLTP) workloads in mind, and, as a result, are optimized for fast dispatch and perform well under small request-response scenarios. However, massive server responses such as those for range queries and data migration for load balancing poses challenges for this design. This thesis analyzes the effects of large transfers on scale-out systems through the lens of a modern Network Interface Card (NIC). The present-day NIC offers new and exciting opportunities and challenges for large transfers, but using them efficiently requires smart data layout and concurrency control. We evaluated the impact of modern NICs in designing data layout by measuring transmit performance and full system impact by observing the effects of Direct Memory Access (DMA), Remote Direct Memory Access (RDMA), and caching improvements such as Intel® Data Direct I/O (DDIO). We discovered that use of techniques such as Zero Copy yield around 25% savings in CPU cycles and a 50% reduction in the memory bandwidth utilization on a server by using a client-assisted design with records that are not updated in place. We also set up experiments that underlined the bottlenecks in the current approach to data migration in RAMCloud and propose guidelines for a fast and efficient migration protocol for RAMCloud