226,841 research outputs found

    MorphStream: Scalable Processing of Transactions over Streams on Multicores

    Full text link
    Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream applications handling shared mutable states. Yet, the full potential of these systems, specifically in exploiting parallelism and implementing dynamic scheduling strategies, is largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables dynamic scheduling and parallel processing in TSPEs. Our experiment showcased MorphStream outperforms current TSPEs across various scenarios and offers support for windowed state transactions and non-deterministic state access, demonstrating its potential for broad applicability

    Stream Processing in the Context of CTS

    Get PDF
    The recent development of innovative technologies related to mobile computing combined with smart city infrastructures is generating massive, heterogeneous data and creating opportunities for novel applications in transportational computation science. The heterogeneous data sources provide streams of information that can be used to create smart cities. The knowledge on stream analysis is thus crucial and requires collaboration of people working in logistics, city planning, transportation engineering and data science. We provide a list of materials for a course on stream processing for computational transportation science. The objectives of the course are: Motivate data stream and event processing, its model and challenges. Acquire basic knowledge about data stream processing systems. Understand and analyze their application in the transportation domain..

    A Survey of Distributed Data Stream Processing Frameworks

    Get PDF
    Big data processing systems are evolving to be more stream oriented where each data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis. As the stream processing technology matures and more organizations invest in digital transformations, new applications of stream analytics will be identified and implemented across a wide spectrum of industries. One of the challenges in developing a streaming analytics infrastructure is the difficulty in selecting the right stream processing framework for the different use cases. With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed data stream processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed data stream processing frameworks. The study also reports our ongoing study on a multilevel streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time data stream processing and analytics framework

    Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

    Get PDF
    Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

    SQPR: Stream Query Planning with Reuse

    Get PDF
    When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions. In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads

    JEERP: Energy Aware Enterprise Resource Planning

    Get PDF
    Ever increasing energy costs, and saving requirements, especially in enterprise contexts, are pushing the limits of Enterprise Resource Planning to better account energy, with component-level asset granularity. Using an application-oriented approach we discuss the different aspects involved in designing Energy Aware ERPs and we show a prototypical open source implementation based on the Dog Domotic Gateway and the Oratio ER

    Network-Aware Stream Query Processing in Mobile Ad-Hoc Networks

    Get PDF
    • …
    corecore