We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algorithms and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a restricted form of shared memory, based on coarse-grained transformations rather than fine-grained updates to shared state. However, we show that RDDs are expressive enough to capture a wide class of computations, including recent specialized programming models for iterative jobs, such as Pregel, and new applications that these models do not capture. We have implemented RDDs in a system called Spark, which we evaluate through a variety of user applications and benchmarks.

/pdf/resilient-distributed-datasets-a-fault-tolerant-abstraction-1jmy0nuhco.pdf

Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing

We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.

/pdf/mesos-a-platform-for-fine-grained-resource-sharing-in-the-1mr3h7jksr.pdf

Mesos: a platform for fine-grained resource sharing in the data center

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees.We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.

/pdf/distributed-graphlab-a-framework-for-machine-learning-and-3837teraz4.pdf

Distributed GraphLab: a framework for machine learning and data mining in the cloud

Many "big data" applications must act on data in real time. Running these applications at ever-larger scales requires parallel platforms that automatically handle faults and stragglers. Unfortunately, current distributed stream processing models provide fault recovery in an expensive manner, requiring hot replication or long recovery times, and do not handle stragglers. We propose a new processing model, discretized streams (D-Streams), that overcomes these challenges. D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers. We show that they support a rich set of operators while attaining high per-node throughput similar to single-node systems, linear scaling to 100 nodes, sub-second latency, and sub-second fault recovery. Finally, D-Streams can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes. We implement D-Streams in a system called Spark Streaming.

/pdf/discretized-streams-fault-tolerant-streaming-computation-at-59e0rdpnuz.pdf

Discretized streams: fault-tolerant streaming computation at scale

: Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel Parallel Sliding Windows algorithm, GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs, using just a single consumer-level computer. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. We build on the basis of Parallel Sliding Windows to propose a new data structure Partitioned Adjacency Lists, which we use to design an online graph database GraphChi-DB.We demonstrate that, on a single PC, GraphChi-DB can process over one hundred thousand graph updates per second, while simultaneously performing computation. GraphChi-DB compares favorably to existing graph databases, particularly on data that is much larger than the available memory. We evaluate our work both experimentally and theoretically. Based on the Parallel Sliding Windows algorithm, we propose new I/O efficient algorithms for solving fundamental graph problems. We also propose a novel algorithm for simulating billions of random walks in parallel on a single computer. By repeating experiments reported for existing distributed systems we show that with only fraction of the resources, GraphChi can solve the same problems in a very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC.

Large-scale Graph Computation on Just a PC

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.

pdf/time-series-anomaly-detection-service-at-microsoft-5o1r3o5q58.pdf

Time-Series Anomaly Detection Service at Microsoft

MODIST is the first model checker designed for transparently checking unmodified distributed systems running on unmodified operating systems It achieves this transparency via a novel architecture: a thin interposition layer exposes all actions in a distributed system and a centralized, OS-independent model checking engine explores these actions systematically We made MODIST practical through three techniques: an execution engine to simulate consistent, deterministic executions and failures; a virtual clock mechanism to avoid false positives and false negatives; and a state exploration framework to incorporate heuristics for efficient error detection

We implemented MODIST on Windows and applied it to three well-tested distributed systems: Berkeley DB, a widely used open source database; MPS, a deployed Paxos implementation; and PACIFICA, a primary-backup replication protocol implementation MODIST found 35 bugs in total Most importantly, it found protocol-level bugs (ie, flaws in the core distributed protocols) in every system checked: 10 in total, including 2 in Berkeley DB, 2 in MPS, and 6 in PACIFICA

/pdf/modist-transparent-model-checking-of-unmodified-distributed-2qxpmdze3r.pdf

MODIST: transparent model checking of unmodified distributed systems

Peer-to-peer networks often use incentive policies to encourage cooperation between nodes. Such systems are generally susceptible to collusion by groups of users in order to gain unfair advantages over others. While techniques have been proposed to combat Web spam collusion, there are few measurements of real collusion in deployed systems. In this paper, we report analysis and measurement results of user collusion in Maze, a large-scale peer-to-peer file sharing system with a non-net-zero point-based incentive policy. We search for colluding behavior by examining complete user logs, and incrementally refine a set of collusion detectors to identify common collusion patterns. We find collusion patterns similar to those found in Web spamming. We evaluate how proposed reputation systems would perform on the Maze system. Our results can help guide the design of more robust incentive schemes.

/pdf/an-empirical-study-of-collusion-behavior-in-the-maze-p2p-27np9w413i.pdf

An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System

Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired by our empirical study on a trace from a large-scale production data-processing cluster; it allows a set of effective query optimizations that are not possible in a traditional batch processing model.We have developed a query processing system called Comet that embraces batched stream processing and integrates with DryadLINQ. We used two complementary methods to evaluate the effectiveness of optimizations that Comet enables. First, a prototype system deployed on a 40-node cluster shows an I/O reduction of over 40% using our benchmark. Second, when applied to a real production trace covering over 19 million machine-hours, our simulator shows an estimated I/O saving of over 50%.

/pdf/comet-batched-stream-processing-for-data-intensive-3yqc0g43ij.pdf

Comet: batched stream processing for data intensive distributed computing

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Papers

Time-Series Anomaly Detection Service at Microsoft

MODIST: transparent model checking of unmodified distributed systems

Time-Series Anomaly Detection Service at Microsoft

An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System

Comet: batched stream processing for data intensive distributed computing