Stream-based Applications Research Articles

A novel temporal and spatial panorama stream processing engine on IoT applications

Abstract Nowadays, more and more streaming data are generated with the development of Internet of Things. Although, streaming data show great application values in practical scenarios, raw streams from terminal sensors are quite massive, heterogeneous and complex, and those features make it difficult for applications to deal with them. In order to simplify streaming data processing for applications, a novel temporal and spatial panorama stream processing engine is proposed. This stream processing engine forms an effective link between bottom sensors and upper stream-based applications, and provides configurable, flexible, available and usable stream services for various upper applications. With support of Internet plus and domain data, raw streaming data are formatted thorough data fusion and are represented in temporal, spatial, logic and storage views. Fusion data are encapsulated with multiple strategies and methods of clustering models on every dimension. According to configurable strategies, encapsulated data are extracted, partitioned and distributed as the form of dynamical variable gratitude data blocks into stream channels. Our engine is applied to a practical application scenario. Case study of dynamic power distribution application based on people crowd streaming data proves the customized service capabilities of our engine. And the outstanding performance of the engine is shown further by experiments evaluation in this case.

Efficient and flexible algorithms for monitoring distance-based outliers over data streams

Anomaly detection is considered an important data mining task, aiming at the discovery of elements (known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria leads to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility in the sense that they assume a single configuration of the k and R parameters. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, are more efficient than previously proposed techniques and offer significant flexibility with regard to the input parameters. Experiments performed on real-life and synthetic data sets verify our theoretical study.

Computing end-to-end delays in stream query processing

Real-time data processing is essential in many stream-based applications including disaster area monitoring, health monitoring, and intrusion detection. In this work, we propose an approach that measures time delays in stream query processing. We represent a stream query as a graph consisting of operators that process data and channels that transport data tokens between operators. Our model establishes a causality relationship between consumed and produced data tokens at each operator and their corresponding occurrence times. The total time taken for the computation from the input to the output of a query, i.e., end-to-end delay, is computed by the causality relationships and periodic schedules for stream queries. Experiments are conducted to validate the proposed technique.

A Design of Pipelined Architecture for on-the-Fly Processing of Big Data Streams

Conventional processing infrastructures have been challenged by huge demand of stream-based applications. The industry responded by introducing traditional stream processing engines along-with emerged technologies. The ongoing paradigm embraces parallel computing as the most-suitable proposition. Pipelining and Parallelism have been intensively studied in recent years, yet parallel programming on multiprocessor architectures stands as one of the biggest challenges to the software industry. Parallel computing relies on parallel programs that may encounter internal memory constrains. In addition, parallel computing needs special skillset of programming as well as software conversions. This paper presents reconfigurable pipelined architecture. The design is especially aimed at Big Data clustering, and it adopts Symmetric multiprocessing (SMP) along with crossbar switch and forced interrupt. The main goal of this promising architecture is to efficiently process big data streams on-the-fly, while it can process sequential programs on parallel-pipelined model. The system overpasses internal memory constrains of multicore architectures by applying forced interrupts and crossbar switching. It reduces complexity, data dependency, high-latency, and cost overhead of parallel computing.

Reconfiguration for Sensitivity Technique: A QoS-aware Co-Design approach for stream-based applications

Reconfiguration for Sensitivity Technique (RST) is presented as an approach for maintaining QoS in stream-based applications running on embedded systems. It is a co-design approach that uses the reconfiguration and sensitivity metrics for QoS awareness; the former for run-time reconfiguration in response to stimuli from the latter. The effectiveness of RST was tested after we used our CHARMS algorithm to screen out 95.17% of the mapping-cases for a H.263 encoder. Our tests showed a 99.25% QoS provisioning-up-time-level at any given instant, based on available battery power, using RST as compared to 80.62% with Performance Aware Reconfiguration of Software Systems(PARSY), 58.83% with Reconfigurable Service Composition(RSC) and 41.67% without RST.

Query scheme and its optimization for distributed data stream

Stream database system is a new type of database system designed to facilitate the execution of queries to continuous streams of data.Many stream-based applications are inherently distributed.Since input stream rates and other system parameters such as the amount of available computing resources can fluctuate significantly,a stream query plan must be able to adapt to these changes.This paper proposed a distributed stream query plan and optimized,and defined performance metrics for response time and system throughput.The experimental result proves that this plan is optimal compared with other plans.

SmartCell: An Energy Efficient Coarse-Grained Reconfigurable Architecture for Stream-Based Applications

This paper presents SmartCell, a novel coarse-grained reconfigurable architecture, which tiles a large number of processor elements with reconfigurable interconnection fabrics on a single chip. SmartCell is able to provide high performance and energy efficient processing for stream-based applications. It can be configured to operate in various modes, such as SIMD, MIMD, and systolic array. This paper describes the SmartCell architecture design, including processing element, reconfigurable interconnection fabrics, instruction and control process, and configuration scheme. The SmartCell prototype with 64 PEs is implemented using 0.13 µm CMOS standard cell technology. The core area is about 8.5mm2, and the power consumption is about 1.6mW/MHz. The performance is evaluated through a set of benchmark applications, and then compared with FPGA, ASIC, and two well-known reconfigurable architectures including RaPiD and Montium. The results show that the SmartCell can bridge the performance and flexibility gap between ASIC and FPGA. It is also about 8% and 69% more energy efficient than Montium and RaPiD systems for evaluated benchmarks. Meanwhile, SmartCell can achieve 4 and 2 times more throughput gains when comparing with Montium and RaPiD, respectively. It is concluded that SmartCell system is a promising reconfigurable and energy efficient architecture for stream processing.

Finding the Next Computational Model: Experience with the UCSC Kestrel

Architects and industry have been searching for the next durable computational model, the next step beyond the standard CPU. Graphics co-processors, though ubiquitous and powerful, can only be effectively used on a limited range of stream-based applications. The UCSC Kestrel parallel processor is part of a continuum of parallel processing architectures, stretching from the application-specific through the application-specialized to the application-unspecific. Kestrel combines an ALU, multiplier, and local memory, with Systolic Shared Registers for seamless merging of communication and computation, and an innovative condition stack for rapid conditionals. The result has been a readily programmable and efficient co-processor for a wide range of applications, including biological sequence analysis, image processing, and irregular problems. Experience with Kestrel indicates that programmable systolic processing, and its natural combination with the Single Instruction-Multiple Data (SIMD) parallel architecture, is the most powerful, flexible, and power-efficient computational model available for a large group of applications. Unlike other approaches that try to displace or replace the standard serial processor, our model recognizes that the expansion in the application landscape and performance requirements simply imply that the most efficient solution is the combination of more than one type of processor. We propose a model in which the CPU and the GPU are complemented by "the third big chip," a massively-parallel SIMD processor.

ZELESSA: an enabler for real-time sensing, analysing and acting on continuous event streams

Our research project aims to build an infrastructure that can efficiently and seamlessly meet the demanding requirements of business event stream-based analytical applications. We focus on the complex event analysis on event streams, QoS-aware event processing services, and a novel event stream processing model that seamlessly and efficiently combines event processing services. That model is built upon the Sense & Respond loops that support a complete Business Intelligence process to sense, interpret, predict, automate and respond to business processes and aim to decrease the time it takes to make the business decisions. We have developed ZELESSA, an event stream management system as a proof of concept.

Load Balancing in a Cluster-Based Web Server for Multimedia Applications

We consider a cluster-based multimedia Web server that dynamically generates video units to satisfy the bit rate and bandwidth requirements of a variety of clients. The media server partitions the job into several tasks and schedules them on the backend computing nodes for processing. For stream-based applications, the main design criteria of the scheduling are to minimize the total processing time and maintain the order of media units for each outgoing stream. In this paper, we first design, implement, and evaluate three scheduling algorithms, First Fit (FF), Stream-based Mapping (SM), and Adaptive Load Sharing (ALS), for multimedia transcoding in a cluster environment. We determined that it is necessary to predict the CPU load for each multimedia task and schedule them accordingly due to the variability of the individual jobs/tasks. We, therefore, propose an online prediction algorithm that can dynamically predict the processing time per individual task (media unit). We then propose two new load scheduling algorithms, namely, Prediction-based Least Load First (P-LLF) and Prediction-based Adaptive Partitioning (P-AP), which can use prediction to improve the performance. The performance of the system is evaluated in terms of system throughput, out-of-order rate of outgoing media streams, and load balancing overhead through real measurements using a cluster of computers. The performance of the new load balancing algorithms is compared with all other load balancing schemes to show that P-AP greatly reduces the delay jitter and achieves high throughput for a variety of workloads in a heterogeneous cluster. It strikes a good balance between the throughput and output order of the processed media units.

The 8 requirements of real-time stream processing

Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments. Furthermore, as the "sea change" caused by cheap micro-sensor technology takes hold, we expect to see everything of material significance on the planet get "sensor-tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements.Recently, several technologies have emerged---including off-the-shelf stream processing engines---specifically to address the challenges of processing high-volume, real-time data without requiring the use of custom code. At the same time, some existing software technologies, such as main memory DBMSs and rule engines, are also being "repurposed" by marketing departments to address these applications.In this paper, we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such, this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements.The paper attempts to be vendor neutral, so no specific commercial products are mentioned.

Experiences with optimizing two stream-based applications for cluster execution

We explore optimization strategies and resulting performance of two stream-based video applications, video texture and color tracker, on a cluster of SMPs. The two applications are representative of a class of emerging applications, which we call “stream-based applications”, that are sensitive to both latency of individual results and overall throughput. Such applications require non-trivial parallelization techniques in order to improve both latency and throughput, given that the stream data emanates from a limited set of sources (exactly one in the two applications studied) and that the distribution of the data cannot be done a priori. We suggest techniques that address in a coordinated fashion the problems of data distribution and work partitioning. We believe the two problems are related and need to be addressed together. We have parallelized two applications using the Stampede cluster programming system that provides abstractions for implementing time- and throughput-sensitive applications elegantly and efficiently. For the Video Textures application we show that we can achieve a speedup of 24.26 on a 112 processor cluster. For the Color Tracker application, where latency is more crucial, we identify the extent of data parallelism that ensures that the slowest member of the pipeline is no longer the bottleneck for achieving a decent frame rate.

Retrospective on Aurora

This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream-processing domain.

Modeling stream-based applications using the SBF model of computation

Modeling applications and architectures at various levels of abstraction is becoming more and more an accepted approach in embedded system design. When looking at the modeling of applications in the domain of video, audio, and graphics applications, we notice that they exhibit a high degree of task parallelism and operate on streams of data. Models that we can use to specify such stream-based applications on a high level of abstraction are the dataflow models and process network models. Each of these models has its own merits. Therefore, an alternative approach is to introduce a model of computation that combines the semantics of both models of computation. We introduce such a model of computation, which we call the stream-based functions (SBF) model of computation and show an example. Furthermore, we discuss the composition and decomposition of SBF objects and put the SBF model of computation in the context of relevant dataflow models and process network models.