Elastic Stream Processing Research Articles

PA-SPS: A predictive adaptive approach for an elastic stream processing system

Stream Processing Systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm-based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs.

STRETCH: Virtual Shared-Nothing Parallelism for Scalable and Elastic Stream Processing

Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale stream processing applications. Given an application, SN parallelism ins9tantiates several copies of each analysis task, making each instance responsible for a dedicated portion of the overall analysis, and relies on dedicated queues to exchange data among connected instances. On the one hand, SN parallelism can scale the execution of applications both up and out since threads can run task instances within and across processes/nodes. On the other hand, its lack of sharing can cause unnecessary overheads and hinder the scaling up when threads operate on data that could be jointly accessed in shared memory. This trade-off motivated us in studying a way for stream processing applications to leverage shared memory and boost the scale up (before the scale out) while adhering to the widely-adopted and SN-based APIs for stream processing applications. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STRETCH</i> , a framework that maximizes the scale up and offers instantaneous elastic reconfigurations (without state transfer) for stream processing applications. We propose the concept of Virtual Shared-Nothing (VSN) parallelism and elasticity and provide formal definitions and correctness proofs for the semantics of the analysis tasks supported by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STRETCH</i> , showing they extend the ones found in common Stream Processing Engines. We also provide a fully implemented prototype and show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STRETCH</i> 's performance exceeds that of state-of-the-art frameworks such as Apache Flink and offers, to the best of our knowledge, unprecedented ultra-fast reconfigurations, taking less than 40 ms even when provisioning tens of new task instances.

Joker: Elastic stream processing with organic adaptation

This paper addresses the problem of auto-parallelization of streaming applications. We propose an online parallelization optimization algorithm that adjusts the degree of pipeline and data parallelism in a joint manner. We define an operator development API and a flexible parallel execution model to form a basis for the optimization algorithm. The operator interface unifies the development of different types of operators and makes operator properties visible in order to enable safe optimizations. The parallel execution model splits a data flow graph into regions. A region contains the longest sequence of compatible operators that are amenable to data parallelism as a whole and can be further parallelized with pipeline parallelism. We also develop a stream processing run-time, named Joker, to scale the execution of streaming applications in a safe, transparent, dynamic, and automatic manner. This ability is called organic adaptation. Joker implements the runtime machinery to execute a data flow graph with any parallelization configuration and most importantly change this configuration at run-time with low cost in the presence of partitioned stateful operators, in a way that is transparent to the application developers. Joker continuously monitors the run-time performance, and runs the optimization algorithm to resolve bottlenecks and scale the application by adjusting the degree of pipeline and data parallelism. The experimental evaluation based on micro-benchmarks and real-world applications showcase that our solution accomplishes elasticity by finding an effective parallelization configuration.

Latency-Aware Secure Elastic Stream Processing with Homomorphic Encryption

Increasingly organizations are elastically scaling their stream processing applications into the infrastructure as a service clouds. However, state-of-the-art approaches for elastic stream processing do not consider the potential threats of exposing their data to third parties in cloud environments. We present the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on homomorphic encryption (HomoESM). The HomoESM not only elastically scales data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real-world test setup, which includes an E-mail Filter benchmark and a Web server access log processor benchmark (EDGAR), we demonstrate the effectiveness of our approach. Experiments on Amazon EC2 indicate that the proposed approach for homomorphic encryption provides a significant result which is 10–17% improvement in average latency in the case of E-mail Filter benchmark and EDGAR benchmark, respectively. Furthermore, EDGAR add/subtract operations, multiplication, and comparison operations showed up to 6.13%, 7.81%, and 26.17% average latency improvements, respectively. Finally, we evaluate the potential of scaling the homomorphic stream processor in the public cloud. These results indicate the potential for real-world deployments of secure elastic data stream processing applications.

Cost-efficient enactment of stream processing topologies

The continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume. These data volume pose different loads to stream processing systems whose resource provisioning needs to be continuously updated at runtime. First approaches already allow for resource provisioning on the level of virtual machines (VMs), but this only allows for coarse resource provisioning strategies. Based on current advances and benefits for containerized software systems, we have designed a cost-efficient resource provisioning approach and integrated it into the runtime of the Vienna ecosystem for elastic stream processing. Our resource provisioning approach aims to maximize the resource usage for VMs obtained from cloud providers. This strategy only releases processing capabilities at the end of the VMs minimal leasing duration instead of releasing them eagerly as soon as possible as it is the case for threshold-based approaches. This strategy allows us to improve the service level agreement compliance by up to 25% and a reduction for the operational cost of up to 36%.

C-Stream

Stream processing is a computational paradigm for on-the-fly processing of live data. This paradigm lends itself to implementations that can provide high throughput and low latency by taking advantage of various forms of parallelism that are naturally captured by the stream processing model of computation, such as pipeline, task, and data parallelism. In this article, we describe the design and implementation of C-Stream , which is an elastic stream processing engine. C-Stream encompasses three unique properties. First, in contrast to the widely adopted event-based interface for developing streaming operators, C-Stream provides an interface wherein each operator has its own driver loop and relies on data availability application programming interfaces (APIs) to decide when to perform its computations. This self-control-based model significantly simplifies the development of operators that require multiport synchronization. Second, C-Stream contains a dynamic scheduler that manages the multithreaded execution of the operators. The scheduler, which is customizable via plug-ins, enables the execution of the operators as co-routines, using any number of threads. The base scheduler implements back-pressure, provides data availability APIs, and manages preemption and termination handling. Last, C-Stream varies the degree of parallelism to resolve bottlenecks by both dynamically changing the number of threads used to execute an application and adjusting the number of replicas of data-parallel operators. We provide an experimental evaluation of C-Stream. The results show that C-Stream is scalable, highly customizable, and can resolve bottlenecks by dynamically adjusting the level of data parallelism used.

Self-adaptive processing graph with operator fission for elastic stream processing

Self adaptive mechanism for scaling stream processing systems.Automatic scaling by increasing/decreasing the number of processing operators.Model that changes graph topology based on a reactive and predictive algorithms.Results show that both algorithms enable online self-adaptation of the graph. Nowadays, information generated by the Internet interactions is growing exponentially, creating massive and continuous flows of events from the most diverse sources. These interactions contain valuable information for domains such as government, commerce, and banks, among others. Extracting information in near real-time from such data requires powerful processing tools to cope with the high-velocity and the high-volume stream of events. Specially designed distributed processing engines build a graph-based topology of a static number of processing operators creating bottlenecks and load balance problems when processing dynamic flows of events. In this work we propose a self-adaptive processing graph that provides elasticity and scalability by automatically increasing or decreasing the number of processing operators to improve performance and resource utilization of the system. Our solution uses a model that monitors, analyzes and changes the graph topology with a control algorithm that is both reactive and proactive to the flow of events. We have evaluated our solution with three stream processing applications and results show that our model can adapt the graph topology when receiving events at high rate with sudden peaks, producing very low costs of memory and CPU usage.

Elastic Stream Processing for Distributed Environments

The current development towards the Internet of Things introduces the need for more flexibility in stream processing. To counter these challenges, the authors propose elastic stream processing for distributed environments, building on top of cloud computing and allowing a scalable and more flexible solution compared to traditional approaches.

Elastic stream processing in the Cloud

Stream processing is a computing paradigm that has emerged from the necessity of handling high volumes of data in real time. In contrast to traditional databases, stream‐processing systems perform continuous queries and handle data on‐the‐fly. Today, a wide range of application areas relies on efficient pattern detection and queries over streams. The advent of Cloud computing fosters the development of elastic stream‐processing platforms, which are able to dynamically adapt based on different cost–benefit trade‐offs. This article provides an overview of the historical evolution and the key concepts of stream processing, with special focus on adaptivity and Cloud‐based elasticity.This article is categorized under: Application Areas &gt; Data Mining Software Tools Technologies &gt; Computer Architectures for Data Mining

StreamCloud: An Elastic and Scalable Data Streaming System

Many applications in several domains such as telecommunications, network security, large-scale sensor networks, require online processing of continuous data flows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static configurations that lead to either under or overprovisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation, and a thorough evaluation of the scalability and elasticity of the fully implemented system.