Parallel Execution of Data-intensive Web Services Based on Data-flow Constructs and I/O Operation Ratio

<p>Web service composition has become a promising technique to build powerful enterprise applications by making use of distributed services with different functions. In the age of big data, more and more web services are created to deal with a large amount of data, which are called data-intensive services. Due to the explosion in the volume of data, providing efficient approaches to composing data-intensive services will become more and more important in the field of service-oriented computing. Meanwhile, as numerous web services have been emerging to offer identical or similar functionality on the Internet, web service composition is usually performed with end-to-end Quality of Service (QoS) properties which are adopted to describe the non-functional properties (e.g., response time, execution cost, reliability, etc.) of a web service. In addition, the executions of composite web services are typically coordinated by a centralized workflow engine. As a result, the centralized execution paradigm suffers from inefficient communication and a single point of failure. This is particularly problematic in the context of data-intensive processes. To that end, more decentralized and flexible execution paradigms are required for the execution of data-intensive applications. From a computational point of view, the problems of QoS-aware data-intensive web service composition and execution can be characterised as complex, large-scale, constrained and multi-objective optimization problems. Therefore, genetic programming (GP) based solutions are presented in this thesis to address the problems. A series of simulation experiments are provided to demonstrate the performance of the proposed approaches, and the empirical observations are also described in this thesis. Firstly, we propose a hybrid approach that integrates the local search procedure of tabu search into the global search process of GP to solving the problem of QoS-aware data-intensive web service composition. A mathematical model is developed for considering the mass data transmission across different component services in a data-intensive service composition. The experimental results show that our proposed approach can provide better performance than the standard GP approach and two traditional optimization methods. Next, a many-objective evolutionary approach is proposed for tackling the QoS-aware data-intensive service composition problem having more than three competing quality objectives. In this approach, the original search space of the problem is reduced before a recently developed many-objective optimization algorithm, NSGA-III, is adopted to solve the many-objective optimization problem. The experimental results demonstrate the effectiveness of our approach, as well as its superiority than existing single-objective and multi-objective approaches. Finally, a GP-based approach to partitioning a composite data-intensive service for decentralized execution is put forth in this thesis. Similar to the first problem, a mathematical model is developed for estimating the communication overhead inside a partition and across the partitions. The data and control dependencies in the original composite web service can be properly preserved in the deployment topology generated by our approach. Compared with two existing heuristic algorithms, the proposed approach exhibits better scalability and it is more suitable for large-scale partitioning problems.</p>

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Distributed Data-intensive Web Service Composition

10.26686/wgtn.14601741.v1 ◽

2021 ◽

Author(s):

Soheila Sadeghiram

Keyword(s):

Web Services ◽

Web Service ◽

Service Composition ◽

Problem Formulation ◽

Web Service Composition ◽

User Preferences ◽

Distributed Data ◽

Multi Objective ◽

Data Intensive ◽

The Impact

<p>Service-oriented architecture (SOA) encourages the creation of modular applications involving Web services as the reusable components. Data-intensive Web services have emerged to manipulate and deal with the massive data emerged from technological advances and their various applications. Distributed Data-intensive Web Service Composition (DWSC) is a core of SOA, which includes the selection of data-intensive Web services from diverse locations on the network and composes them to accomplish a complicated task. As a fundamental challenge for service developers, service compositions must fulfil functional requirements and optimise Quality of Service (QoS), simultaneously. The QoS of a distributed DWSC is not only impacted by the QoS of component services and how the compositions are generated, but also by the locations of services and data transformation between services. However, existing works often neglect the impact of locations and data on service composition. The distributed DWSC has not been sufficiently studied in the literature. In this thesis, we first define the single-objective distributed DWSC that includes communication (e.g. bandwidth), Web service (execution time) and data (data cost) attributes. To this aim, we consider bandwidth information of communication links obtained using the location information of services. Based on the problem formulation, we then address the distributed DWSC problem by developing EC-based approaches. Those EC-based approaches are designed to incorporate domain-knowledge for effectively solving the distributed DWSC problem. Afterwards, we study the multi-objective distributed DWSC to satisfy different QoS requirements. In particular, the QoS-constrained distributed DWSC problem and user preferences are considered. For finding trade-off solutions for those problems, new Multi-objective Evolutionary Algorithms (MOEAs) are proposed based on the current Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Furthermore, a new problem formulation for the dynamic distributed DWSC (D2−DWSC) problem with bandwidth fluctuations is proposed. An EC-based approach is developed to solve the D2-DWSC. Finally, extensive empirical evaluations are conducted that demonstrate the high performance of our proposed methods in finding composite services with good QoS.</p>

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Pipelined data‐flow delegated orchestration for data‐intensive eScience workflows

International Journal of Web Information Systems ◽

10.1108/ijwis-05-2013-0012 ◽

2013 ◽

Vol 9 (3) ◽

pp. 204-218 ◽

Cited By ~ 4

Author(s):

Sattanathan Subramanian ◽

Paweł Sztromwasser ◽

Pål Puntervoll ◽

Kjell Petersen

Keyword(s):

Data Flow ◽

Data Intensive

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PERANCANGAN APLIKASI NOTIFIKASI JADWAL PENERBANGAN BERBASIS MOBILE

AIRMAN: Jurnal Teknik dan Keselamatan Transportasi ◽

10.46509/ajtkt.v1i2.17 ◽

2020 ◽

Vol 1 (2) ◽

pp. 38-45

Author(s):

Ucok Sinaga ◽

Fatmawati Sabur

Keyword(s):

Web Services ◽

Real Time ◽

Data Flow ◽

Flow Diagram ◽

Data Flow Diagram

Penelitian ini bertujuan untuk merancang pengembangan sistem informasi jadwal penerbangan yang memberikan informasi waktu yang sesungguhnya berdasarkan keberadaan pesawat dalam penerbangan kepada pengguna, dan menghasilkan sebuah sistem informasi jadwal penerbangan yang real time sehingga dapat memberikan informasi waktu kadatangan pesawat secara tepat. Dalam perancangan pengembangan sistem informasi jadwal penerbangan menggunakan UML Univ (data flow diagram) dan perancangan aplikasinya menggunakan teknologi mobile dengan pemanfaatan web services/API untuk mengakses informasi jadwal penerbangan dan informasi cuaca. Jadwal pererbangan berupa file json yang diperoleh dari website FIDS bandara hasanuddin Makassar, sedangkan informasi cuaca bandara asal dan bandara tujuan penumpang diperoleh dari API Open Wheater.

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Composing Distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm

Lecture Notes in Computer Science - Database and Expert Systems Applications ◽

10.1007/978-3-030-27618-8_30 ◽

2019 ◽

pp. 411-422 ◽

Cited By ~ 1

Author(s):

Soheila Sadeghiram ◽

Hui Ma ◽

Gang Chen

Keyword(s):

Web Services ◽

Memetic Algorithm ◽

Distributed Data ◽

Data Intensive

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