Geo-information processing service composition for concurrent tasks: A QoS-aware game theory approach

Li, Haifeng; Zhu, Qingsheng; Yang, Xiaoxia; Xu, Lei

doi:10.1016/j.cageo.2011.10.007

Cited by 21 publications

(22 citation statements)

References 26 publications

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“…In the geospatial domain, some progress has been made towards implementing an operational big geo-data computing architecture. Part of the research has focused on web service-based geoprocessing models for distributed spatial data sharing and computing [1][2][3][4], and some studies have investigated CyberGIS-based [5,6] methods to address computationally intensive and collaborative geographic problems by exploiting HPC infrastructure, such as computational grids and parallel clusters [7][8][9], particularly focused upon the integration of particular CyberGIS components, spatial middleware and high-performance computing and communication resources for geospatial research [10,11].…”

Section: Cloud-based Big Geo-data Processingmentioning

confidence: 99%

An Effective NoSQL-Based Vector Map Tile Management Approach

Wan

Huang

Xia

2016

IJGI

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Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.

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Section: Cloud-based Big Geo-data Processingmentioning

confidence: 99%

An Effective NoSQL-Based Vector Map Tile Management Approach

Wan

Huang

Xia

2016

IJGI

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“…Whereas, the work in [16], proposes an approach based on skyline method, which reduces the number of candidate services to be considered, in order to effectively select the optimal services for the composition. Whilst, [23] proposes a non-cooperative gamebased mathematical model to analyze the competitive relationship between tasks, and an iterative algorithm that converges to Nash-equilibrium is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Shuffled frog leaping algorithm (SFLA), is introduced by Eusuff and Lansey [15], is a meta-heuristic evolutionary algorithm, it is inspired from mimicking the behavior of frogs searching for food placed on separate stones haphazardly positioned in a pond that has a maximum quantity of food [22], [23]. SFLA is designed to seek a global optimal solution, which combines the benefits of a gene-based Memetic Algorithm (MA) and social behavior-based particle swarm optimization (PSO).…”

Section: Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

SFL Algorithm for QoS-based Cloud Service Composition

Younes¹,

Essaaidi²,

Moussaoui³

2014

IJCA

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With the advent of cloud service-based applications and Software as a Service (SaaS), new applications have recently known an increasing use of service-oriented architecture (SOA). This model has allowed computer science and associated industries to build new customized applications, by using the available and the existing cloud services bridged together dynamically to form a complex workflow process with more functionalities. However cloud services with similar and compatible functionalities may be offered by multiple providers but may also be offered at different QoS levels. Hence, to build a composite service with a high QoS, a decision should be made based on end-to-end QoS. This work proposes a new approach, for QoS-aware cloud service composition, which addresses a universal model, with end-toend QoS. It also proposes an effective evolutionary method based on Shuffled Frog Leaping Algorithm (SFLA), which is satisfying global and local constraints. Therefore, in order to evaluate the robustness of the proposed approach, we have evaluated the impact of several parameters that are highly significant in evolutionary methods, such as the impact of the population size, number of candidate services per task and number of criteria. The experimental results show that the chosen algorithm performs better than the ones based on Genetic Algorithm (GA).

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“…With regard to the problem mentioned above, this paper presents a service-oriented architecture for Proactive Geospatial Information Service (PGIS) based on our previous works [9][10][11][12]. The PGIS is an aggregation and cooperation mechanism to integrate all the sensor, data, processing, and social resources to satisfy complicated user requirements.…”

Section: Introductionmentioning

confidence: 99%

A Service-Oriented Architecture for Proactive Geospatial Information Services

2011

Future Internet

Self Cite

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Abstract:The advances in sensor network, linked data, and service-oriented computing has indicated a trend of information technology, i.e., toward an open, flexible, and distributed architecture. However, the existing information technologies show a lack of effective sharing, aggregation, and cooperation services to handle the sensors, data, and processing resources to fulfill user's complicated tasks in near real-time. This paper presents a service-orientated architecture for proactive geospatial information services (PGIS), which integrates the sensors, data, processing, and human services. PGIS is designed to organize, aggregate, and co-operate services by composing small scale services into service chains to meet the complicated user requirements. It is a platform to provide real-time or near real-time data collection, storage, and processing capabilities. It is a flexible, reusable, and scalable system to share and interoperate geospatial data, information, and services. The developed PGIS framework has been implemented and preliminary experiments have been performed to verify its performance. The results show that the basic functions such as task analysis, managing sensors for data acquisition, service composition, service chain construction and execution are validated, and the important properties of PGIS, including interoperability, flexibility, and reusability, are achieved.

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Geo-information processing service composition for concurrent tasks: A QoS-aware game theory approach

Cited by 21 publications

References 26 publications

An Effective NoSQL-Based Vector Map Tile Management Approach

An Effective NoSQL-Based Vector Map Tile Management Approach

SFL Algorithm for QoS-based Cloud Service Composition

A Service-Oriented Architecture for Proactive Geospatial Information Services

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