This work presents an approach to isolate cache space while supporting shared data. Enabling shared data caching is challenging because it causes interference making it unsuitable for real-time multicores. Unlike prior works, we aim to introduce isolation, but our approach enables caching of shared data, and promotes isolated cache analysis for individual cores. The crux behind our approach is that shared data isolation can be achieved by partitioning the replacement information instead of the cache's data storage. Consequently, this work introduces ParRP, a novel hardware cache partitioning scheme for realtime cache-coherent multicores. Our evaluation using the gem5 simulator shows that, by providing isolation for shared data, the worst-case execution time of multi-threaded tasks can be lower by 2.4× at the cost of a 16.5% decrease in average-case performance.

In this paper, a Deep Neural Network is trained using Reinforcement Learning in order to drift on arbitrary trajectories which are defined by a sequence of waypoints. In a first step, a highly accurate vehicle simulation is used for the training process. Then, the obtained policy is refined and validated on a self-built model car. The chosen reward function is inspired by the scoring process of real life drifting competitions. It is kept simple and thus applicable to very general scenarios. The experimental results demonstrate that a relatively small network, given only a few measurements and control inputs, already achieves an outstanding performance. In simulation, the learned controller is able to reliably hold a steady state drift. Moreover, it is capable of generalizing to arbitrary, previously unknown trajectories and different driving conditions. After transferring the learned controller to the model car, it also performs surprisingly well given the physical constraints.

Modern analytical workloads are highly heterogeneous and massively complex, making generic out of the box query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workload, and make major strides in pushing previous research ideas to production deployment. Along the way we solve several operational challenges including, making steering actions more manageable, keeping the costs of steering within budget, and avoiding unexpected performance regressions in production. Our resulting system, QO-Advisor, essentially externalizes the query planner to a massive offline pipeline for better exploration and specialization. We discuss various aspects of our design and show detailed results over production SCOPE workloads at Microsoft, where the system is currently enabled by default.

We demonstrate Pipemizer , an optimizer and recommender aimed at improving the performance of queries or jobs in pipelines. These job pipelines are ubiquitous in modern data analytics due to jobs reading output files written by other jobs. Given that more than 650k jobs run on Microsoft's SCOPE job service per day and about 70% have inter-job dependencies, identifying optimization opportunities across query jobs is of considerable interest to both cluster operators and users. Pipemizer addresses this need by providing recommendations to users, allowing users to understand their system, and facilitating automated application of recommendations. Pipemizer introduces novel optimizations that include holistic pipeline-aware statistics generation, inter-job operator push-up, and job split & merge. This demonstration showcases optimizations and recommendations generated by Pipemizer , enabling users to understand and optimize job pipelines.

Big data systems have become increasingly complex making the job of a query optimizer incredibly difficult. This is due to more complicated decision making, more complex query plans seen, and more tedious objective functions in cloud-based big data workloads. As a result, production cloud query optimizers are often far from optimal. In this paper, we describe building a learning query optimizer for big data workloads at Microsoft. We make four major contributions. First, we describe the challenges in cloud query optimizers based on our observations from the big data workloads at Microsoft. Second, we discuss what makes machine learning an attractive approach to aid the big data query optimizers in decision making. Third, we present Microlearner, a practical approach to characterize large cloud workloads into smaller subsets and build micromodels over each subset to tame the complexity of big data workloads And finally, we describe the productization of Microlearner, using learned cardinality as a concrete example, via performance results over very large production workloads and illustrating the various challenges involved in deployment.

Modern data processing systems require optimization at massive scale, and using machine learning to optimize these systems (ML-for-systems) has shown promising results. Unfortunately, ML-for-systems is subject to over generalizations that do not capture the large variety of workload patterns, and tend to augment the performance of certain subsets in the workload while regressing performance for others. In this paper, we introduce a performance safeguard system, called PerfGuard , that designs pre-production experiments for deploying ML-for-systems. Instead of searching the entire space of query plans (a well-known, intractable problem), we focus on query plan deltas (a significantly smaller space). PerfGuard formalizes these differences, and correlates plan deltas to important feedback signals, like execution cost. We describe the deep learning architecture and the end-to-end pipeline in PerfGuard that could be used with general relational databases. We show that this architecture improves on baseline models, and that our pipeline identifies key query plan components as major contributors to plan disparity. Offline experimentation shows PerfGuard as a promising approach, with many opportunities for future improvement.

The twenty-first century has been dominated by the need for large scale data processing, marking the birth of big data platforms such as Cosmos. This paper describes the evolution of the exabyte-scale Cosmos big data platform at Microsoft; our journey right from scale and reliability all the way to efficiency and usability, and our next steps towards improving security, compliance, and support for heterogeneous analytics scenarios. We discuss how the evolution of Cosmos parallels the evolution of the big data field, and how the changes in the Cosmos workloads over time parallel the changing requirements of users across industry.

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes of data. However, as the complexity and scale of analytical jobs increase, they encounter a number of unforeseen problems, hotspots with large intermediate data on temporary storage, longer job recovery time after failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer. Given a set of constraints and an objective function at compile-time, Phoebe is able to determine the decomposition of job plans, and the optimal set of checkpoints to preserve their outputs to durable global storage. Phoebe consists of three machine learning predictors and one optimization module. For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. Using these predictions, we formulate checkpoint optimization as an integer programming problem and propose a scalable heuristic algorithm that meets the latency requirement of the production environment. We demonstrate the effectiveness of Phoebe in production workloads, and show that we can free the temporary storage on hotspots by more than 70% and restart failed jobs 68% faster on average with minimum performance impact. Phoebe also illustrates that adding multiple sets of checkpoints is not cost-efficient, which dramatically reduces the complexity of the optimization.

Query processing over big data is ubiquitous in modern clouds, where the system takes care of picking both the physical query execution plans and the resources needed to run those plans, using a cost-based query optimizer. A good cost model, therefore, is akin to better resource efficiency and lower operational costs. Unfortunately, the production workloads at Microsoft show that costs are very complex to model for big data systems. In this work, we investigate two key questions: (i) can we learn accurate cost models for big data systems, and (ii) can we integrate the learned models within the query optimizer. To answer these, we make three core contributions. First, we exploit workload patterns to learn a large number of individual cost models and combine them to achieve high accuracy and coverage over a long period. Second, we propose extensions to Cascades framework to pick optimal resources, i.e, number of containers, during query planning. And third, we integrate the learned cost models within the Cascade-style query optimizer of SCOPE at Microsoft. We evaluate the resulting system, Cleo, in a production environment using both production and TPC-H workloads. Our results show that the learned cost models are 2 to 3 orders of magnitude more accurate, and 20X more correlated with the actual runtimes, with a large majority (70%) of the plan changes leading to substantial improvements in latency as well as resource usage.

Right-sizing resource allocation for big-data queries, particularly in serverless environments, is critical for improving infrastructure operational efficiency, capacity availability, query performance predictability, and for reducing unnecessary wait times. In this paper, we present AutoToken --- a simple and effective predictor for estimating the peak resource usage of recurring big data queries. It uses multiple query plan identifiers to identify recurring query templates and to learn models with the goal of reducing over-allocation in future instances of those queries. AutoToken is computationally light, for both training and scoring, is easily deployable at scale, and is integrated with the Peregrine workload optimization infrastructure at Microsoft. We extensively evaluate AutoToken on SCOPE jobs from our production clusters and show that it outperforms state-of-the-art solutions for peak resource estimation. We also discuss our plans towards supporting repeatable and extensible research on resource prediction for SCOPE jobs, including describing a simulation methodology for generating arbitrary-sized datasets with similar characteristics as the production datasets.

Database administrators (DBAs) were traditionally responsible for optimizing the on-premise database workloads. However, with the rise of cloud data services, where cloud providers offer fully managed data processing capabilities, the role of a DBA is completely missing. At the same time, optimizing query workloads is becoming increasingly important for reducing the total costs of operation and making data processing economically viable in the cloud. This paper revisits workload optimization in the context of these emerging cloud-based data services. We observe that the missing DBA in these newer data services has affected both the end users and the system developers: users have workload optimization as a major pain point while the system developers are now tasked with supporting a large base of cloud users.

The past decade has seen a tremendous interest in large-scale data processing at Microsoft. Typical scenarios include building business-critical pipelines such as advertiser feedback loop, index builder, and relevance/ranking algorithms for Bing; analyzing user experience telemetry for Office, Windows or Xbox; and gathering recommendations for products like Windows and Xbox. To address these needs a first-party big data analytics platform, referred to as Cosmos, was developed in the early 2010s at Microsoft. Cosmos makes it possible to store data at exabyte scale and process in a serverless form factor, with SCOPE [4] being the query processing workhorse. Over time, however, several newer challenges have emerged, requiring major technical innovations in Cosmos to meet these newer demands. In this abstract, we describe three such challenges from the query processing viewpoint, and our approaches to handling them.

In distributed query processing, data shuffle is one of the most costly operations. We examined scaling limitations to data shuffle that current systems and the research literature do not solve. As the number of input and output partitions increases, naïve shuffling will result in high fan-out and fan-in. There are practical limits to fan-out, as a consequence of limits on memory buffers, network ports and I/O handles. There are practical limits to fan-in because it multiplies the communication errors due to faults in commodity clusters impeding progress. Existing solutions that limit fan-out and fan-in do so at the cost of scaling quadratically in the number of nodes in the data flow graph. This dominates the costs of shuffling large datasets. We propose a novel algorithm called Hyper Dimension Shuffle that we have introduced in production in SCOPE, Microsoft's internal big data analytics system. Hyper Dimension Shuffle is inspired by the divide and conquer concept, and utilizes a recursive partitioner with intermediate aggregations. It yields quasilinear complexity of the shuffling graph with tight guarantees on fan-out and fan-in. We demonstrate how it avoids the shuffling graph blow-up of previous algorithms to shuffle at petabyte-scale efficiently on both synthetic benchmarks and real applications.

Interactive data analytics is often inundated with common computations across multiple queries. These redundancies result in poor query performance and higher overall cost for the interactive query sessions. Obviously, reusing these common computations could lead to cost savings. However, it is difficult for the users to manually detect and reuse the common computations in their fast moving interactive sessions. In the paper, we propose to demonstrate SparkCruise, a computation reuse system that automatically selects the most useful common computations to materialize based on the past query workload. SparkCruise materializes these computations as part of query processing, so the users can continue with their query processing just as before and computation reuse is automatically applied in the background --- all without any modifications to the Spark code. We will invite the audience to play with several scenarios, such as workload redundancy insights and pay-as-you-go materialization, highlighting the utility of SparkCruise.

In this paper, we present our early findings on the utilization of a social robot during the formal interview process. We implemented a mechanism that enabled the robot to ask context-aware questions based on the data based on the resume or linked-in profile of the applicant. Later, we conducted an exploratory between-subject evaluation with 8 adult participants to find the difference in the duration of applicants responses given to the NAO robot and to the human interviewer. Our results didn't find the significant difference in terms of participant responses to human and robotic interviewers.

Analytics-as-a-service, or analytics job service, is emerging as a new paradigm for data analytics, be it in a cloud environment or within enterprises. In this setting, users are not required to manage or tune their hardware and software infrastructure, and they pay only for the processing resources consumed per job. However, the shared nature of these job services across several users and teams leads to significant overlaps in partial computations, i.e., parts of the processing are duplicated across multiple jobs, thus generating redundant costs. In this paper, we describe a computation reuse framework, coined CLOUDVIEWS, which we built to address the computation overlap problem in Microsoft's SCOPE job service. We present a detailed analysis from our production workloads to motivate the computation overlap problem and the possible gains from computation reuse. The key aspects of our system are the following: (i) we reuse computations by creating materialized views over recurring workloads, i.e., periodically executing jobs that have the same script templates but process new data each time, (ii) we select the views to materialize using a feedback loop that reconciles the compile-time and run-time statistics and gathers precise measures of the utility and cost of each overlapping computation, and (iii) we create materialized views in an online fashion, without requiring an offline phase to materialize the overlapping computations.

We observe significant overlaps in the computations performed by user jobs in modern shared analytics clusters. Naïvely computing the same subexpressions multiple times results in wasting cluster resources and longer execution times. Given that these shared cluster workloads consist of tens of thousands of jobs, identifying overlapping computations across jobs is of great interest to both cluster operators and users. Nevertheless, existing approaches support orders of magnitude smaller workloads or employ heuristics with limited effectiveness. In this paper, we focus on the problem of subexpression selection for large workloads, i.e., selecting common parts of job plans and materializing them to speed-up the evaluation of subsequent jobs. We provide an ILP-based formulation of our problem and map it to a bipartite graph labeling problem. Then, we introduce B ig S ubs , a vertex-centric graph algorithm to iteratively choose in parallel which subexpressions to materialize and which subexpressions to use for evaluating each job. We provide a distributed implementation of our approach using our internal SQL-like execution framework, SCOPE, and assess its effectiveness over production workloads. B ig S ubs supports workloads with tens of thousands of jobs, yielding savings of up to 40% in machine-hours. We are currently integrating our techniques with the SCOPE runtime in our production clusters.

Query optimizers are notorious for inaccurate cost estimates, leading to poor performance. The root of the problem lies in inaccurate cardinality estimates, i.e., the size of intermediate (and final) results in a query plan. These estimates also determine the resources consumed in modern shared cloud infrastructures. In this paper, we present C ARD L EARNER , a machine learning based approach to learn cardinality models from previous job executions and use them to predict the cardinalities in future jobs. The key intuition in our approach is that shared cloud workloads are often recurring and overlapping in nature, and so we could learn cardinality models for overlapping subgraph templates. We discuss various learning approaches and show how learning a large number of smaller models results in high accuracy and explainability. We further present an exploration technique to avoid learning bias by considering alternate join orders and learning cardinality models over them. We describe the feedback loop to apply the learned models back to future job executions. Finally, we show a detailed evaluation of our models (up to 5 orders of magnitude less error), query plans (60% applicability), performance (up to 100% faster, 3x fewer resources), and exploration (optimal in few 10s of executions).

As the Internet of Things becomes a reality, proliferation of wireless devices such as ZigBee nodes has accelerated. Their presence is now wide spread in sensitive areas such as home automation, industrial control systems, medical devices and security systems. As their popularity has increased, so has the urgency to protect and defend these networks. The decentralized nature of ZigBee ad-hoc networks creates unique security challenges for maintaining network security and intrusion detection. RF-fingerprinting provides a unique physical (PHY) layer defense against node counterfeiting by identifying rogue devices through their RF-emissions. In previous work, feature generation from received RF signals was accomplished with parametric variables such as standard deviation, variance, skewness, and kurtosis, all based on the assumption of a normal distribution of a particular region of interest. We show in this work that most collected signals are either multi-modal or non-parametric. Use of non-parametric methods for feature generation such as mean, median, mode, and trend represented by linear model coefficient estimates are shown to be much more applicable to the non-parametric distribution of the collected ZigBee preamble, resulting in improved classification performance of devices. Non-parametric classifier Random Forest is used with both parametric and non-parametric features to provide a classification performance comparison. Performance improvements of upto 9% in correct classification rates have been achieved and an effective gain of 4 dB SNR was realized.1

SUMMARY: 2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) AVAILABILITY: 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.