Zhuofeng Zhao

CAFISE: an approach to enabling adaptive configuration of service grid applications

Aiming at building up more powerful, open-standard-based and generic infrastructures for application integration, service grids address the challenges in large-scale coordinated sharing and on-demand composition of network-based application services. The related endeavors have opened up new ways of application development, deployment and integration. In connection with the new level of scale, openness and dynamism brought forward by service grids, adaptive service configuration is of essential importance to applications. This paper proposes an approach called CAFISE, which tries to better facilitate on-demand configuration and dynamic reconfiguration of service grid applications. In CAFISE, a business design and its supporting software system are considered in a coherent way, and a convergent relation, which helps to map business-level configurations to software-level configurations, is highlighted. The paper is particularly devoted to presenting and discussing the principles, reference model, modeling language and supporting application framework of CAFISE. Since practical usefulness is highly valued in the development of CAFISE, the application of the approach to a real-world scenario is also presented in the paper.

Facilitating Dynamic Service Compositions by Adaptable Service Connectors

Dynamic changes of services and requirements require service connection relationships adaptable in service compositions. This paper presents an adaptable service connector model and related language and tools. The model presents service connection relationships as an explicit component with which service connections can be reconfigured and changes of services involved in the interaction can be handled; this makes the service connection relationships more adaptive. With the language and tools supporting the model, services can be dynamically chained, which make service-oriented applications adapt to volatile business requirements and dynamic changeable services. The related case study and evaluation are also presented in this paper.

Feature-based high-availability mechanism for quantile tasks in real-time data stream processing

Under distributed Cloud environment, the real‐time and continuous data stream makes the availability during processing essential but expensive. For aggregation tasks of data stream processing systems, traditional replica‐based high‐availability mechanisms require large overheads at run‐time and long recovery latency at fail‐time, because of specific nature of aggregations. In this paper, we focus on the typical quantile tasks and propose a feature‐based high‐availability mechanism to reduce related overhead and the latency. With the help of monitor module, quantile feature is maintained incrementally through histogram synopsis over time‐based sliding window, and the failed quantile tasks can be recovered precisely with high probability in an efficient way. The effectiveness has been analyzed theoretically, and meanwhile, the acceptable tradeoff between overheads and performance has been demonstrated by comprehensive experiments on both synthetic and real data. Copyright

A Consistency-Preserving Mechanism for Web Services Response Caching

Web services are rapidly emerging as a popular standard technology for sharing data and functionality among heterogeneous systems. Service providers and consumers are loosely coupled and distributed across the network, either within an organization or across organizational boundaries, and therefore, performance becomes a major concern in such a distributed environment. Furthermore, XML is widely used as message format for service providers and consumers in Web services environment. XML message packaging and parsing brings extra overhead to both ends. Web services response latency, as well as throughput, is becoming a bottleneck problem. In this paper, We propose a consistency-preserving mechanism for Web services response caching, which reduces the volume of data transmitted without semantic interpretation of service requests or responses, and accelerates the services response finally. It achieves this reduction through the use of cryptographic hashing to detect similarities with previous results. Experiments with an initial prototype called SigsitAcclerator indicate that our mechanism can lead to significant performance improvement over more straightforward techniques.

A pattern-based approach to facilitating service composition

This paper presents a pattern-based approach to facilitating the composition of Web services, which enables business users to use composite services more effectively. With the support of patterns, business users can construct applications with larger-granularity components, amend and customize their own patterns to meet personalized requirements. The approach is illustrated with a case study. We suggest the patterns be used during the orchestration stage in a service composition process. By doing so, the composition logic built into the pattern can be made available to other users.

A Hybrid Processing System for Large-Scale Traffic Sensor Data

In recent years, with the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development. Traffic sensor data gathered by large amounts of sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing traffic sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical traffic sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of traffic sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.

An Integrated Processing Platform for Traffic Sensor Data and Its Applications in Intelligent Transportation Systems

With the continuous expansion of the scope of traffic sensor networks, traffic sensor data becomes widely available and large in amount. Traffic sensor data gathered by large amounts of sensors shows the massive, continuous, streaming and spatio-temporal characteristics compared to traditional traffic data. In order to satisfy the requirements of different applications in Intelligent Transportation Systems (ITS), we need to have the capability of real-time processing over both streaming and historical traffic sensor data. In this paper, we present DeCloud4SD, an integrated processing platform for traffic sensor data, which is designed to provide services for receiving, storing, acquiring and computing traffic sensor data in a scalable architecture with real-time guarantee. Three types of applications using DeCloud4SD in a real ITS project are also described in detail. Through the analysis of these applications, we can see that DeCloud4SD can ensure: 1) scalable and customizable traffic sensor data gathering and computing, 2) rapid application development and deployment using a MapReduce-like model, 3) seamless integration with existing relational data sources and applications.

A Spatio-temporal Parallel Processing System for Traffic Sensory Data

With the continuous expansion of the scope of traffic sensor networks, traffic sensory data becomes widely available and is continuously being produced. Traffic sensory data gathered by large amounts of sensors show the massive, continuous, streaming and spatio-temporal characteristics compared to traditional traffic data. In order to satisfy the requirements of different applications with these data, we need to have the capability of processing both real-time traffic sensory data in streaming way and historical traffic sensory data in large amount. In this paper, we present an approach and corresponding system for traffic sensory data processing, which is designed to combine spatio-temporal data partition, parallel pipeline processing and stream computing to support traffic sensory data processing in a scalable architecture with real-time guarantee. Three types of applications in real project are also described in detail to show the significant effect gains of the proposed approach and system. Numerical evaluations according to experiment results also show that the system can gain high performance in terms of the processing time of traffic sensory data stream.

Feature-Based High Availability Mechanism for Extreme Aggregation Tasks in Real-Time Data Stream Processing

Data stream under Cloud or IoT (Internet of Things) implies real-time and continuous processing, during which availability guarantee is essential but expensive. For extreme aggregation tasks, traditional HA mechanisms require vast space at run-time and much longer recovery latency at fail-time especially under worse input. In this paper, feature-based high availability mechanism is proposed for extreme aggregation tasks, in which space-bounded feature is maintained through random sampling over time-based sliding window and failed tasks can be recovered precisely with high probability in an efficient way. The probabilistic effectiveness has been proved theoretically, and meanwhile the acceptable tradeoff between related overheads and performance has been demonstrated by comprehensive experiments on both synthetic and real data.

MapReduce-Based data stream processing over large history data

With the development of Internet of Things applications based on sensor data, how to process high speed data stream over large scale history data brings a new challenge. This paper proposes a new programming model RTMR, which improves the real-time capability of traditional batch processing based MapReduce by preprocessing and caching, along with pipelining and localizing. Furthermore, to adapt the topologies to application characteristics and cluster environments, a model analysis based RTMR cluster constructing method is proposed. The benchmark built on the urban vehicle monitoring system shows RTMR can provide the real-time capability and scalability for data stream processing over large scale data.