Advanced Technology Group
in the CSO Office
PUBLICATIONS

Yodea: Workload Pattern Assessment Tool for Cloud Migration

Rukma Talwadker and Cijo George, NetApp

As the news around cloud repatriations gets real, many cloud technologists associate them with poor understanding of the applications and their usage patterns by the enterprises. Our solution, Yodea, is a tool cum methodology to analyze work-load patterns in the light of cloud suitability. We bring forward compute patterns which can benefit from cloud economics with on-demand compute scaling. Yodea further ranks workloads in terms of their cloud suitability on the basis of these metrics. After the fact analysis of storage workloads for a customer install-base, features 38% of the “already in cloud” volumes in the top 100 ranked list by Yodea.



Fail-Slow at Scale: Evidence of Hardware Performance Faults in Large Production Systems

Haryadi S. Gunawi and Riza O. Suminto, University of Chicago; Russell Sears and Casey Golliher, Pure Storage; Swaminathan Sundararaman, Parallel Machines; Xing Lin and Tim Emami, NetApp; Weiguang Sheng and Nematollah Bidokhti, Huawei; Caitie McCaffrey, Twitter; Gary Grider and Parks M. Fields, Los Alamos National Laboratory; Kevin Harms and Robert B. Ross, Argonne National Laboratory; Andree Jacobson, New Mexico Consortium; Robert Ricci and Kirk Webb, University of Utah; Peter Alvaro, University of California, Santa Cruz, Mingzhe Hao, Huaicheng Li, and H. Birali Runesha, University of Chicago

Fail-slow hardware is an under-studied failure mode. We present a study of 114 reports of fail-slow hardware incidents, collected from large-scale cluster deployments in 14 institutions. We show that all hardware types such as disk, SSD, CPU, memory, and network components can exhibit performance faults. We made several important observations such as faults convert from one form to another, the cascading root causes and impacts can be long, and fail-slow faults can have varying symptoms. From this study, we make suggestions to vendors, operators, and systems designers.



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FELLOWSHIPS

Eamonn Keogh, UC Riverside – August 2018

Time Series Snippets: A New Analytics Primitive with applications to IoT Edge Computing

While most of today’s always-connected tech devices take advantage of cloud computing, many Internet of Things (IoT) developers increasingly understand the benefits of doing more analytics on the devices themselves, a philosophy known as edge computing. By performing analytic tasks directly on the sensor, edge computing can drastically reduce the bandwidth, cloud processing, and cloud storage needed.



John Paparrizos, University of Chicago – August 2018

Accelerating Internet of Things Data Analytics through Scalable Time-Series Representation Learning

Kernel methods, a class of machine learning algorithms for pattern recognition, have shown a great deal of promise in the analysis of complex, real-world, data. However, kernel methods remain largely unexplored in the analysis of time- varying measurements (i.e., time series), which is becoming increasingly prevalent across scientific disciplines, industrial settings, and Internet of Things (IoT) applications. Until now, research in time-series analysis has focused on designing methods for three components, namely, (i) representation methods; (ii) comparison functions; and (iii) indexing mechanisms. Unfortunately, these components have typically been investigated and developed independently, resulting in methods that are incompatible with each other. The lack of a unified approach has hindered progress towards scalable analytics over massive time-series collections.



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