Retro: Targeted Resource Management in Multi-tenant Distributed Systems

Abstract In distributed systems shared by multiple tenants, effective resource management is an important pre-requisite to providing quality of service guarantees. Many systems deployed today lack performance isolation and experience contention, slowdown, and even outages caused by aggressive workloads or by improperly throttled maintenance tasks such as data replication. In this work we present Retro, a resource management framework for shared distributed systems. Retro monitors per-tenant resource usage both within and across distributed systems, and exposes this information to centralized resource management policies through a high-level API. A policy can shape the resources consumed by a tenant using Retro's control points, which enforce sharing and ratelimiting decisions. We demonstrate Retro through three policies providing bottleneck resource fairness, dominant resource fairness, and latency guarantees to high-priority tenants, and evaluate the system across five distributed systems: HBase, Yarn, MapReduce, HDFS, and Zookeeper. Our evaluation shows that Retro has low overhead, and achieves the policies' goals, accurately detecting contended resources, throttling tenants responsible for slowdown and overload, and fairly distributing the remaining cluster capacity

The University of Dayton Exponent, April 1945

Published monthly, usually October through May, in the interest of the students of the University of Dayton. Contents include essays, editorials, poems, plays, histories, and other creative works.https://ecommons.udayton.edu/exponent/1367/thumbnail.jp

Updating the TSP Quality Plan Using Monte Carlo Simulation

Humphrey tries to answer one of software management’s biggest questions, showing how one naval organization with large system projects, over a 15-year period, used the TSP to help them with planning and tracking, meeting schedules, and understanding knowledge work. by Watts S. Humphrey An Interview with Watts S. Humphrey Who else can boast more than a half-century in the software industry? Humphrey sits down with CrossTalk to reflect on some of his most illuminating experiences in the software industry and discusses the past, present, and future of his innovations—including the TSP

Representation and recognition of human actions in video

PhDAutomated human action recognition plays a critical role in the development of human-machine communication, by aiming for a more natural interaction between artificial intelligence and the human society. Recent developments in technology have permitted a shift from a traditional human action recognition performed in a well-constrained laboratory environment to realistic unconstrained scenarios. This advancement has given rise to new problems and challenges still not addressed by the available methods. Thus, the aim of this thesis is to study innovative approaches that address the challenging problems of human action recognition from video captured in unconstrained scenarios. To this end, novel action representations, feature selection methods, fusion strategies and classification approaches are formulated. More specifically, a novel interest points based action representation is firstly introduced, this representation seeks to describe actions as clouds of interest points accumulated at different temporal scales. The idea behind this method consists of extracting holistic features from the point clouds and explicitly and globally describing the spatial and temporal action dynamic. Since the proposed clouds of points representation exploits alternative and complementary information compared to the conventional interest points-based methods, a more solid representation is then obtained by fusing the two representations, adopting a Multiple Kernel Learning strategy. The validity of the proposed approach in recognising action from a well-known benchmark dataset is demonstrated as well as the superior performance achieved by fusing representations. Since the proposed method appears limited by the presence of a dynamic background and fast camera movements, a novel trajectory-based representation is formulated. Different from interest points, trajectories can simultaneously retain motion and appearance information even in noisy and crowded scenarios. Additionally, they can handle drastic camera movements and a robust region of interest estimation. An equally important contribution is the proposed collaborative feature selection performed to remove redundant and noisy components. In particular, a novel feature selection method based on Multi-Class Delta Latent Dirichlet Allocation (MC-DLDA) is introduced. Crucial, to enrich the final action representation, the trajectory representation is adaptively fused with a conventional interest point representation. The proposed approach is extensively validated on different datasets, and the reported performances are comparable with the best state-of-the-art. The obtained results also confirm the fundamental contribution of both collaborative feature selection and adaptive fusion. Finally, the problem of realistic human action classification in very ambiguous scenarios is taken into account. In these circumstances, standard feature selection methods and multi-class classifiers appear inadequate due to: sparse training set, high intra-class variation and inter-class similarity. Thus, both the feature selection and classification problems need to be redesigned. The proposed idea is to iteratively decompose the classification task in subtasks and select the optimal feature set and classifier in accordance with the subtask context. To this end, a cascaded feature selection and action classification approach is introduced. The proposed cascade aims to classify actions by exploiting as much information as possible, and at the same time trying to simplify the multi-class classification in a cascade of binary separations. Specifically, instead of separating multiple action classes simultaneously, the overall task is automatically divided into easier binary sub-tasks. Experiments have been carried out using challenging public datasets; the obtained results demonstrate that with identical action representation, the cascaded classifier significantly outperforms standard multi-class classifiers

Efficient Online Processing for Advanced Analytics

With the advent of emerging technologies and the Internet of Things, the importance of online data analytics has become more pronounced. Businesses and companies are adopting approaches that provide responsive analytics to stay competitive in the global marketplace. Online analytics allow data analysts to promptly react to patterns or to gain preliminary insights from early results that aid in research, decision making, and effective strategy planning. The growth of data-velocity in a variety of domains including, high-frequency trading, social networks, infrastructure monitoring, and advertising require adopting online engines that can efficiently process continuous streams of data. This thesis presents foundations, techniques, and systems' design that extend the state-of-the-art in online query processing to efficiently support relational joins with arbitrary join-predicates (beyond traditional equi-joins); and to support other data models (beyond relational) that target machine learning and graph computations. The thesis is divided into two parts: We first present a brief overview of Squall, our open-source online query processing engine that supports SQL-like queries on top of streams. Then, we focus on extending Squall to support efficient theta-join processing. Scalable distributed join processing requires a partitioning policy that evenly distributes the processing load while minimizing the size of maintained state and duplicated messages. Efficient load-balance demands apriori-statistics which are not available in the online setting. We propose a novel operator that continuously adjusts itself to the data dynamics, through adaptive dataflow routing and state repartitioning. It is also resilient to data-skew, maintains high throughput rates, avoids blocking during state repartitioning, and behaves as a black-box dataflow operator with provable performance guarantees. Our evaluation demonstrates that the proposed operator outperforms the state-of-the-art static partitioning schemes in resource utilization, throughput, and execution time up to 7x. In the second part, we present a novel framework that supports the Incremental View Maintenance (IVM) of workloads expressed as linear algebra programs. Linear algebra represents a concrete substrate for advanced analytical tasks including, machine learning, scientific computation, and graph algorithms. Previous works on relational calculus IVM are not applicable to matrix algebra workloads. This is because a single entry change to an input-matrix results in changes all over the intermediate views, rendering IVM useless in comparison to re-evaluation. We present Lago, a unified modular compiler framework that supports the IVM of a broad class of linear algebra programs. Lago automatically derives and optimizes incremental trigger programs of analytical computations, while freeing the user from erroneous manual derivations, low-level implementation details, and performance tuning. We present a novel technique that captures $\Delta$ changes as low-rank matrices. Low-rank matrices are representable in a compressed factored form that enables cheaper computations. Lago automatically propagates the factored representation across program statements to derive an efficient trigger program. Moreover, Lago extends its support to other domains that use different semi-ring configurations, e.g., graph applications. Our evaluation results demonstrate orders of magnitude (10x-1

1881-08-04

The Old Commonwealth was a weekly newspaper published in Harrisonburg, Va., between 1865 and 1884

Santa Clara Magazine, Volume 27 Number 3, Winter 1985

2 - WHEN ARE LEADERS AT THEIR BEST? By James M. Kouzes and Barry Z. Posner. There is a revolution in leadership style taking place in corporate America. This deals with how ordinary people get extraordinary things done in organizations. 7 - A MEDITATION IN ST. IVES By William J. Rewak, S.J. Some reflections on the essence of work taken from Father Rewak\u27s travel diary during a trip to England last summer. 10 - TAKING LAUGHTER SERIOUSLY By John Morreall. A philosophy professor shows how important humor is to human life and how understanding our laughter can help us understand our humanity. 14 - IN THE MANNER OF ANDY ROONEY By James P. Degnan. A wonderful spoof of one of America\u27s favorite commentators. 15 - WHAT\u27S A COLLEGE TEACHER TO DO? By Christiaan T. Lievestro. The trick, the author explains, is to turn students on so they will go on by themselves, liberated from the teacher. 20 - PAULO FREIRE HAS HIS SAY By James Torrens, S.J. A look at the famous Third World educator during his brief visit to Santa Clara last year. 23 - GIVING PSYCHOLOGY AWAY By Dale G. Larson. A new model of mental health training is emerging among a growing number of psychologists who want to share their skills with others. 27 - NEWS OF SANTA CLARA New leaders take over in the President\u27s Club and the Bronco Bench, and the activities of faculty on sabbatical leaves during the 1984-85 academic year are summarized. 30 - CAMPAIGN FOR SANTA CLARA By Kenneth E. Cool. An update on the Institute of Agribusiness in the Leavey School of Business and on the new Institute for Information Storage Technology in the School of Engineering. Also, a report on the progress of the engineering campaign as it climbs toward its $8.9 million goal.https://scholarcommons.scu.edu/sc_mag/1070/thumbnail.jp

Frontier and Midland, Autumn 1938

This is volume 19, number 1.https://scholarworks.umt.edu/frontier/1064/thumbnail.jp