Wednesday, 13 December 2017

Session 8: Performance Analysis and Benchmarking II

Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning

Authors:

Nikolas Roman Herbst (Karlsruhe Institute of Technology)
Nikolaus Huber (Karlsruhe Institute of Technology)
Samuel Kounev (Karlsruhe Institute of Technology)
Erich Amrehn (IBM Research & Development)

Abstract:

As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

DOI: 10.1145/2479871.2479899

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COSBench: Cloud Object Storage Benchmark

Authors:

Qing Zheng (Shanghai Jiao Tong University)
Haopeng Chen (Shanghai Jiao Tong University)
Yaguang Wang (Intel Asia-Pacific R&D Ltd.)
Jian Zhang (Intel Asia-Pacific R&D Ltd.)
Jiangang Duan (Intel Asia-Pacific R&D Ltd.)

Abstract:

With object storage systems being increasingly recognized as a preferred way to expose one’s storage infrastructure to the web, the past few years have witnessed an explosion in the acceptance of these systems. Unfortunately, the proliferation of available solutions and the complexity of each individual one, coupled with a lack of dedicated workload, makes it very challenging for one to evaluate and tune the performance of different systems. To help address this problem, we present the Cloud Object Storage Benchmark (COSBench). It is a benchmark tool that we have developed at Intel with the goal of facilitating both performance comparison and system optimization of these systems. In this paper, we describe the design and implementation of this tool, focusing on its extensibility and scalability. In addition, we discuss how people can use this tool to perform system characterization and how the latter can facilitate system comparison and optimization. To demonstrate the value of our tool, we report the results of our experiments conducted on two Swift setups we built in our lab. We also share some of our experiences in turning our setups to achieve higher performance.

DOI: 10.1145/2479871.2479900

Full text: PDF

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Parallelism Profiling and Wall-time Prediction for Multi-threaded Applications

Authors:

Achille Peternier (University of Lugano)
Walter Binder (University of Lugano)
Akira Yokokawa (University of Lugano)
Lydia Chen (IBM Research Lab)

Abstract:

A detailed and accurate characterization of the parallelism of applications is essential for predicting their wall-time on different platforms, both for an application running in isolation and for a set of consolidated applications executing on the same platform. However, prevailing profilers are often based on sampling and do not provide exact information on the parallelism of the profiled application. In this paper we present a novel profiler that logs all thread scheduling activities within the operating system kernel. These logs enable us to accurately characterize applications’ parallelism on a given platform by computing the number of threads that are active at each moment. We also present a simple mathematical prediction model to estimate wall-time for program execution on a k2-core machine using profiles collected using a k1-core machine (of the same architecture and running at the same clock speed). We use our profiler to assess the parallelism of several CPU-bound DaCapo benchmarks and evaluate the accuracy of our prediction model.

DOI: 10.1145/2479871.2479901

Full text: PDF

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