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Cloud- and Agent-Based Geospatial Service Chain: A Case Study of Submerged Crops Analysis During Flooding of the Yangtze River Basin Xicheng Tan, Liping Di, Senior Member, IEEE, Meixia Deng, Aijun Chen, Fang Huang, Chao Peng, Meng Gao, Yayu Yao, and Zongyao Sha
Abstract—More intelligent construction of geospatial service chains and more efficient execution of such service chains remain major challenges in distributed geospatial analysis. This study addresses these challenges using a Cloud- and agent-based approach for automatic and intelligent construction of a geospatial service chain in the Cloud environment. A spatial agent infrastructure comprising fundamental services and an agent interface is designed, implemented, and deployed. Our approach involves a strategy for selecting and aggregating appropriate agents and Web-processing services (WPS) by evaluating their availability. This strategy ensures successful construction of a geospatial service chain in the Cloud environment, even when there is a lack of requisite geospatial services in the system. Moreover, the method can significantly increase the speed of a service chain in distributed environments and retains high stability when more requests are submitted over various network conditions. This is because the computing mobility and intelligence of the agent help to avoid transfer of large volumes of spatial data and keep the load balanced during construction and execution of the service chain. A prototype system for analysis of submerged crops during flooding of the Yangtze River basin demonstrates the advantages of our approach over existing methods.
Manuscript received October 01, 2014; accepted October 27, 2014. Date of publication December 17, 2014; date of current version March 27, 2015. This work was supported in part by NSFC projects under Grant 51277167, Grant 41001221, Grant 41071249, and Grant 41371371; in part by Guangzhou Science and Technology Project on the Supercomputing Applications Research: 2012Y2-00035 and 2013Y2-00031; in part by “the Fundamental Research Funds for the Central Universities:” the Study of Artificial Immune-based Self-Adaptive Generalization Method in SpatioTemporal Association Rule Mining; in part by “CAST Innovation Fund:” the Study of Agent and Cloud-Based Spatial Big Data Service Chain; in part by Open Research Fund by Sichuan Emergency Mapping Support and Geological Disaster Monitoring Engineering Research Center under Grant K2014B003; and in part by the Postdoctoral Science Foundation of China under Grant 2011M501400. (Corresponding author: Xicheng Tan.) X. Tan is with the Department of Spatial information and Digital Technology, International School of Software, Wuhan University, Wuhan 430079, China (e-mail:
[email protected]). L. Di, M. Deng, and A. Chen are with the Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22032 USA. F. Huang is with the School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China. C. Peng is with the School of Software, Tsinghua University, Beijing 100084, China. M. Gao, Y. Yao, and Z. Sha are with the Department of Spatial information and Digital Technology, International School of Software, Wuhan University, Wuhan 430079, China. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2014.2376475
Index Terms—Agent, Cloud computing, geospatial service chain, Open Geospatial Consortium (OGC), service aggregation, service-oriented architecture (SOA).
I. I NTRODUCTION
D
URING RECENT decades, the Open Geospatial Consortium (OGC) has continuously developed a series of OGC Web services (OWS) specifications, including the Web Map Service Interface Standard (WMS) [1], Web coverage service (WCS) [2], Web feature service (WFS) [3], and Web-processing service (WPS) [4]. WMS provides a simple HTTP interface for requesting geo-registered map images from one or more distributed geospatial servers. WFS and WCS allow users all over the world to access geospatial data in a standard manner. The WPS defines an interface, which facilitates the publishing and discovery of geospatial processes. In addition, numerous geospatial Web services are built on OWS, such as the Web services available in GeoBrain [5]. At present, many popular geospatial platforms, such as ArcGIS and Mapinfo, also offer components to support OWS, which facilitate interoperation. The distributed geospatial services built on OWS can be aggregated as a geospatial service chain (i.e., service-oriented architecture [SOA]-based geospatial service chain), which is similar to a common-purpose service chain. A geospatial service chain provides a feasible method for the discovery and use of numerous spatial services on the Web. This method is focusing on aggregating services based on possible business logic and semantic constraints to fulfill the requirements of users. Recent advances in geospatial Web service and service chain technologies have facilitated the automatic discovery of requisite services from a distributed network, their aggregation into a new service called a composite service, and the provision of heterogeneous data and composite services from distributed centers to users worldwide [6]–[9]. However, challenges still remain in the construction of geospatial service chains. For example, when the size of a geospatial network expands, it may be very difficult to find the requisite services among the thousands of nodes that offer geospatial services. As another example, the development of the earth observation system (EOS) means that geospatial data and services accessible over distributed environments are increasing continuously and large amounts of real-time data are provided, which require more effective processing capabilities and techniques, thereby
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posing great challenges in the construction of geospatial service chains. The major challenges are listed as follows. 1) The low efficiency of large-volume spatial data transfer. The existing data interoperation methods (e.g., SOA-based methods) between the distributed services in a chain are based mainly on WFS or WCS [10], which are feasible only when the volume of spatial data involved is not very large, concurrent requests are not many, and network bandwidth is not low. When these conditions are not met, current data interoperation methods may have very low efficiency, or even lead to occasional service chain failures, which occurs very often in real-world applications. 2) The lack of geospatial services. The amount of geospatial data, especially EOS data, is continually increasing, so it is impossible for one server to offer all the geospatial services required for various dynamic applications. 3) The lack of requisite data on the servers of the services. Popular geoprocessing services are constructed mainly on a server based on the geospatial Web service, e.g., WPS. However, if the server lacks the data required for the WPS, the WPS request will fail or have to wait for the transfer of a large volume of data through WCS or WFS from other data servers, which is also affected by challenge (1). Thus, a new geospatial service chaining method is needed to address these challenges. In this study, we proposed a Cloudand agent-based geospatial service chain approach [11]–[14]. This approach allows self-adaptive aggregation of geospatial services based on the theories and technologies of both Cloud computing and agent. It considers an agent infrastructure that allows a spatial agent to migrate and execute geospatial tasks, as well as a strategy for agent-based construction of a geospatial service chain in a Cloud environment. The feasibility and effectiveness of this approach are evaluated in experiments on real-time analysis of submerged crops during a flood disaster in the Yangtze River basin. II. R ELATED W ORK A. Geospatial Service Chain Previous studies have contributed to the theory and methodology of service chains (i.e., service aggregation) [15]–[20]. Geoscience researchers have conducted many studies of the chaining or aggregation of geospatial services based on the OGC standards [21]–[24]. Service and service-chaining mechanisms based on OGC data services (e.g., WMS, WCS, and WFS) and geoprocessing services (e.g., WPS) have been employed to solve many practical problems, mainly using an SOA [25]–[28]. Semantic and syntactic service descriptions have been used to generate workflows that can integrate service discovery and link remote geographic services to help expert users to produce complex geoprocessing services and perform the timely analysis of geodata [29]. The research areas of distributed geographic information processing (DGIP) have been defined and research issues, such as SOA, federal enterprise architecture (FEA)-based DGIP architecture, spatial service chains, models, and spatial interoperability, have been
addressed [30]. A DGIP-oriented architecture was designed and DGIP applications were built by combining OGC services into service chains, which was illustrated by an example from the domain of risk management [31]. Using an approach based on semantics and ISO geo-ontologies, a mechanism for discovery, accessing, and chaining geospatial Web services was designed [6], [32]. As part of the NASA Sensor Web Project, a service geoprocessing workflow was proposed and tested based on the retrieval and processing of sensor data during wildfire hot pixel detection [33]. B. Cloud Computing Researchers around the world have also proposed new methodologies for massive spatial data transferring, processing, and application, such as geospatial information processing based on the Grid [34]–[36]. With the development of Cloud computing technology, geocomputing methods based on the Cloud have attracted much attention in recent studies [37], [38]. Cloud computing involves the use of resources (hardware and software) that are delivered as a service over a network [39]. Cloud computing technology is derived from the development of grid computing, high performance computing (HPC) [40], and distributed computing. Cloud computing is becoming the next-generation computing platform and it differs from others. In the Cloud, the infrastructure can act as a service (IaaS), the platform can act as a service (PaaS), and the software can also act as a service (SaaS) [37], where all of the resources, such as computing equipment, storage, and data, can all be used as services. Thus, the Cloud is scalable and highly reliable, and can be used on demand. The Cloud has become a new HPC platform that offers powerful virtual clusters [41]. For example, the AWS EC2 Cloud gives customers access to clusters with very high CPU capacities, high bandwidth, and low latency for HPC. Cloud computing has achieved great success in the commercial arena because of these properties. The Cloud has also been introduced into geosciences, and some experimental projects are being implemented by FGDC, NOAA, and NASA. Business corporations, such as Microsoft, Amazon, and ESRI, are also investigating how to operate geospatial applications in the Cloud environment and how geoscience might fit into the new computing model. Previous studies demonstrate that spatial Cloud computing provides unprecedented new capabilities to enable Digital Earth and geosciences in the twenty-first century [42]. To provide useful flood and water-borne disease forecasting tools to decision makers in the Southern African region, an overall architecture was developed based on the Sensor Web, grid, and Cloud, and completed within the GEO 2009–2011 Work Plan [43]. The feasibility and performance of geoprocessing chains have been analyzed and compared on different Cloud platforms [44]. A Hadoop Cloud-enabled WPS framework for Earth Observation data processing was proposed and the experimental processing of MODIS data showed that WPS can be enabled in a Cloud computing environment [45]. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation, thus a Cloud implementation of a full hyperspectral unmixing chain was constructed for EO-1, which will be made available online as part of the NASA SensorWeb suite of Web services [46].
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C. Agent-Based Methods Agent-based methodologies have been explored in the past decade [47], [48], and have become very popular in geoscience research areas such as land use and land cover change simulations [49], fighting forest fires [50], prediction of urban traffic [51], and movements of pedestrians and shoppers [52]. There are two primary modes for agent-based methods, the instrumental mode and the representational mode [53], both of which have attracted attention in many studies [54]–[57]. Moreover, some studies have considered the use of agent-based combinations of Web services. For example, a multiagent-based coalition formation approach for service composition was used to achieve emergent behavior analysis and decentralized decisionmaking [58]. A distributed agent coalition algorithm called DACA was proposed for autonomic Web service composition based on distributed decision making by autonomous agents [59]. Another formal service agent model called DPAWSC was proposed to allow distributed Web service composition, and experiments showed that the model was effective in producing high-quality solutions at a low communication cost [60]. In addition, some other studies have focused on agent-based spatial services. A service-oriented simulation framework was presented to support spatially explicit agent-based modeling within a spatial cyberinfrastructure (CI) environment [61]. An agent-based methodology was introduced for the aggregation of flexible Web map services on the Internet [62]. Agents and the OWL-S services model were used to aggregate OGC-based map services [63]. III. M ETHODOLOGY Our proposed Cloud- and agent-based geospatial service chain approach employs the theories and technologies of both Cloud computing and agents to address the three challenges mentioned in Section I. For a better understanding of the methodology, we first present an example scenario. Disaster prevention and reduction in the Yangtze River basin of China is a systematic project that requires many resources. For example, it involves many government departments, including the Flood Control and Drought Relief Office (FCDRO), Ministry of Land and Resources (MLR), Ministry of Agriculture (MA), and academic and research organizations (ARO). During a flood disaster, the FCDRO is responsible for collecting real-time flood depth data, which are monitored by the sensors in hydrologic stations, as well as organizing all the useful resources to facilitate highly effective disaster prevention and reduction. The FCDRO decision-maker must take many factors into consideration, and agricultural losses are an important factor. The departments involved have their own professional resources. For example, the FCDRO obtains real-time flood depth data from hydrologic stations, MLR acquires high accuracy geographic data about the flooded region, MA manages numerous agricultural remote sensing data such as multispectral and InSAR remote sensing data, and the AROs employ spatial analysis and remote sensing image interpretation algorithms, such as flood submergence analysis, overlap analysis, slope analysis, and remote sensing image classification and crop extraction algorithms.
Fig. 1. SAI. (a) Fundamental services. (b) Agent software interface.
Normally, when the FCDRO needs to analyze a submerged crop area, they first obtain the latest fundamental geographic data including DEM and DOM from the MLR, and they also need to acquire the latest remote sensing data from the MA. It is time consuming (e.g., days) to complete the procedures to filter and transfer the data. Next, the FCDRO brings together experts from the areas of remote sensing, mapping, agriculture, hydrology, meteorology, etc., to reach a decision. This way has very low efficiency and is not a real-time decision-making solution. Occasionally, it may cause huge losses during emergencies such as floods. Recently, SOA-based applications [27], [28], including SOAbased service chains, have considerably improved the disaster response scenario mentioned above. Based on the SOA model, the departments involved exist as distributed nodes that provide their resources through Web services. The distributed spatial resources can be integrated to make decisions based on the Web services. Therefore, the SOA-based methodology has become popular for disaster prevention, reduction, and assessment [64]– [66]. However, the SOA-based framework still faces the same challenges mentioned in Section I. To address these challenges, our Cloud- and agent-based geospatial service chain approach exploits the capability of the agent to search for dynamic resources and to facilitate computing mobility in a Cloud environment. It considers three major aspects: 1) the spatial agent infrastructure (SAI); 2) the strategy used for agent-based service chain construction; and 3) the service chain construction and execution procedures. A. SAI In this study, a single Cloud can be assigned three basic roles: data Cloud role (DC), computation Cloud role (CC), and algorithm Cloud role (AC). If a single Cloud is DC, it can store geospatial data and it will release the metadata for its retained data via CSW [67], [68], and share the data with OGC data services such as WFS and WCS. However, if the data are exclusive and sensitive, the DC will only release the metadata of the data. CC offers computing resources including hardware and software. AC plays the role of an algorithm manager where the algorithms exist as registered agents. In this design, a single Cloud may have more than one role in a network, e.g., a CC can be DC and AC, and a DC can be AC and CC, which simply depends on the type of services the single Cloud wants to offer.
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Based on the above concept, we designed an SAI (Fig. 1), which mainly comprises the fundamental Cloud services and the agent software interfaces. The SAI has the following required services. 1) The TaskPlanning service: after a task request is submitted by a user, the TaskPlanning service will be invoked, which will find the requisite resources (data, services, and agents) by calling the ResourcesFinding service, before it produces a rational task script so the agents can migrate and the services identified can be chained. 2) The ResourcesFinding service aims to search for the requisite spatial data, services, and agents to execute a requested task. 3) The AgentMigrating service: this service is used for migrating and running the agents. After the task has been planned, this service packages the agent, the task script, and the requisite data into a file, before moving the file to the target Cloud. 4) The ResultAccepting service: when the task of an agent is completed, this service is requested by the agent to return the results. 5) The AgentPulse service aims to detect whether an agent is “alive.” 6) The ResultWFS and ResultWCS services return the results. The software structure of an agent is designed based on a uniform interface. All of the agents implement a common interface, but they have private methods. The Run method is the unique public method of the IMobileAgent interface and all agents must implement this interface. It is triggered to start running and executing geospatial task according to the task script when an agent arrives at the target Cloud. When SAI is deployed on Clouds, the agents can move among all of the single Clouds and execute their geospatial tasks. Using the SAI, the agents also have the ability to interact with geospatial services (WFS, WCS, WPS, and CSW), which allows the agents to act as part of the entire chain. B. Strategy for Cloud- and Agent-Based Service Chain Construction To understand the strategy for Cloud- and agent-based service chain construction considered in the proposed approach, we consider its application to submerged crops analysis in the disaster prevention and reduction project for the Yangtze River basin of China. Fig. 2 shows the Geospatial Model for the application. After the submerged crops analysis task is submitted from the portal running on the Cloud of the FCDRO, the TaskPlanning service of SAI will search for the requisite resources, including data, services, computing resources, and algorithm agents on the Clouds, by calling the ResourcesFinding service according to the model. After the requisite resources have been found, the task script is created. During the service chain aggregation procedure, the agents migrate to the specified Clouds and search for the resources required for geospatial analysis utilizing the existing OGC geospatial services (WFS/WCS/WPS). In this manner, a comprehensive service chain can be constructed that contains both
Fig. 2. Geospatial model of the application.
geospatial services and agents. As a result, a service chain can still be constructed successfully even if the requisite services are missing. Moreover, the large volume distributed geospatial data required by a service chain do not need to be transferred across the network because the agents can move to the Cloud that holds the required data. After the task is submitted from the portal, the TaskPlanning service will plan the completion of three analyses according to the Geospatial Model: submergence analysis based on flood depth data and DEM, crop extraction based on remote sensing images, and overlap analysis. To enable the dynamic construction of a service chain, we define the following two action rules. 1) TaskPlanning Service Action Rule: This rule defines the actions of the TaskPlanning service. The TaskPlanning service identifies the requisite resources among the distributed Clouds using the ResourcesFinding service, which is created via the OGC CSW. The CSW specifies interfaces, HTTP protocol bindings, and the access to digital catalogues of metadata for geographic data and services. If the required spatial data and WPS are on a DC at the same time, the WPS will be invoked by the TaskPlanning service and acquire the results in the form of GML or URL. If the requisite WPS is not on the DC, but the DC is also a CC, the TaskPlanning service will invoke the ResourcesFinding service to search the AC for the requisite algorithm agent and migrate the agent to the DC to complete the geospatial task. Finally, the agent will invoke the ResultAccepting service to return the URL of the resulting data, which is added to the ResultWFS or ResultWCS of the SAI. When there are multiple requests, many geospatial tasks will be sent to the CC node, which might be overloaded. To keep the load balanced, when the CPU or RAM usage rate of a CC node exceeds a given threshold (defined as 80% here), other agents will migrate to other CC nodes with sufficient idle resources. 2) Agent Action Rule: This rule defines the principles for agent actions. After the agent has migrated to the target DC that is also a CC role, it starts to execute the geospatial task. It also sends pulse signals to its parent Cloud via the AgentPulse service to inform the parent Cloud that it is alive. If not all of the requisite data are stored on the target DC, the agent will search other Clouds for the requisite WPS and data. When a DC has the requisite spatial data and WPS at the same time,
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Fig. 3. Typical service chains of submerged crops analysis. (a) Only based on WPS. (b) Based on WPS and agent. (c) Entirely based on agent.
the WPS will be invoked by the agent and obtain the results in the form of GML or URL. If the requisite WPS is absent from the DC, but the DC is also a CC, the agent will send a clone of itself to the DC to complete the analysis. Finally, the agent will invoke the ResultAccepting service to return the URL of the resulting data, which is added to the ResultWFS or ResultWCS. According to these two rules, if the statuses of the resources (e.g., geospatial data, services, and agents) differ in the Cloud, the service chain form of the application will be changed. It is impossible to list all the forms of the service chain, thus only three typical service chains are listed (Fig. 3). At present, the type “a” service chain is the most common form of service chain. In the type “a” service chain, only the OGC services such as WPS, WFS, and WCS are involved, but chain construction will fail if only one service is absent (e.g., the submergence analysis WPS is missing). In the present study, however, the submergence analysis agent can substitute the WPS such as type “b.” Even if all the WPS are missing, the service chain can still be constructed successfully based entirely
on the agents in the form of type “c” if the requisite agents exist in the AC. C. Service Chain Construction and Execution Procedures To illustrate the procedures used to construct and execute a Cloud- and agent-based geospatial service chain based on the strategy described above, we use submerged crops analysis during flooding as an example. There are four distributed single Clouds in the application we consider, but there is no WPS in these Clouds (detailed information is provided in Table I). Table I shows the information related to the CloudID, Cloud owner, role, retained algorithm agents, and data. The simulated real-time flood depth data in Cloud 1 can be obtained by Web services. Cloud 2 holds 4.8 GB of 5-m DEM data and topographic maps, which cover the Hubei province of China. The multispectral remote sensing datasets in Cloud 3 are MSS and TM data with a volume of around 16 GB, which cover the research area. In addition to the Clouds of the FCDRO, MLR, and MA, a volunteer, Cloud 4, is owned by an ARO and it has the
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TABLE I D ETAILED I NFORMATION A BOUT THE C LOUDS
algorithm agents. Three algorithm agents are retained in Cloud 4: flood analysis, overlay analysis, and crop extraction. No WPS is deployed on all of these Cloud nodes, thus a service chain that is only dependent on the geospatial services for the application cannot be constructed. The roles of Clouds 2 and 3 are both DC and CC. The role of Cloud 4 is only AC. According to the rules defined in Section III-B, the procedures used for the construction and execution of the submerged crops analysis service chain are illustrated in Fig. 4. n (n is a positive integer) is the position of a The symbol step in the overall service chain. After the portal invokes the TaskPlanning service to submit the Geospatial Model in step 1 , the TaskPlanning service invokes the ResourcesFinding service of the Clouds to search for the requisite geospatial data, 2 During this proceservices, and algorithm agents in step . dure, it finds that Cloud 2 holds the DEM data required for submergence analysis, Cloud 3 stores TM data for the studied area, and Cloud 4 has the requisite geospatial algorithm agents. Clouds 2 and 3 have CC roles, whereas Cloud 4 does not, thus the crops extraction agent and submergence analysis 3 and agent migrate to Clouds 2 and 3, respectively, in step 4 After the agents return the they begin to execute in step . 5 the TaskPlanning service continues to search results in step , 6 before the overlap for the requisite services or agents in step , 7 depending analysis agent migrates to Clouds 3 or 2 in step , 8 the overlap on their performance. After its execution in step , analysis agent returns the results to the TaskPlanning service in 9 and the results are displayed on the portal in step . 10 step IV. I MPLEMENTATION AND D EMONSTRATION To evaluate the feasibility of the proposed approach using a Cloud- and agent-based geospatial service chain, a prototype system was implemented on the Alibaba Aliyun Cloud [69], one of the biggest commercial Cloud platforms in China. The system performance of the proposed approach was evaluated by comparison with other two approaches in experimental tests.
A. Prototype System Aliyun provides worldwide Cloud services, such as IaaS, PaaS, and SaaS. We purchased four Aliyun Cloud virtual machines (VMs) and deployed the nodes listed in Table I in different centers of the Aliyun Cloud, as shown in Fig. 5. Four Windows Server 2008 Cloud VMs were created from one Cloud instance image. Each VM has two virtual CPUs of 2.20 GHz, with 4 GB of RAM, a 100-GB disk, and bandwidth of 2 Mb/s. The SAI environment was installed in the image so that every Cloud VM had the SAI environment, but there were no WPS in these VMs. The prototype system portal is shown in Fig. 6. In the system, Openlayers 3.0 is utilized to display the images and analysis results, a Microsoft Bing map is used as the background map, and the geographic algorithms of Geospatial Model are built based on Grass GIS 6.44. After a request is submitted by pressing the “START ANALYSIS” button, the service chain generates real-time results for submerged crops and displays them on the portal. The results area shows crops that are submerged; the accuracy of the result depends on the precision of the DEM data, remote sensing data, and the analysis model utilized. The results indicate possible flooded regions with submerged crops after the FCDRO decides to open a specific sluice in the dike to reduce the flood pressure from the Yangtze River. Many factors need to be considered, but the analysis results for submerged crops could be important in helping the FCDRO in decision-making to prevent flood disasters. For example, according to the results, the FCDRO can select the most rational region to submerge to reduce or minimize agricultural losses. To address challenge 2 mentioned in Section I, no WPS were deployed on the distributed Cloud nodes, but the prototype execution results show that the service chain could still be constructed successfully because of the capability of the agents. Using this method, even if the requisite geospatial services are missing or if one Cloud node that has WPS services becomes invalid, agents can act as substitutes to construct the service chain. The prototype also shows that challenge 3 is well addressed by moving the agent to the data server to avoid large-volume data transfer and failure of the service chain construction. B. Performance Evaluation Evaluation of system performance was based on comparison tests between our proposed approach and two other approaches. 1) Approach 1: SOA-based service chain on physical machines. This approach uses OWS to construct a service chain. All the requisite WPS are built on one physical machine, and required spatial data are acquired through WCS and WFS from the data machines. All the machines are in the same local area network (LAN) with bandwidth of 1000 Mb/s, but NetLimiter 3.0 was used to set the bandwidth to specific values to simulate the network for a distributed environment. 2) Approach 2: SOA-based service chain on the Cloud. This approach is the same as Approach 1 but run on the Cloud. All the requisite WPS are built on one Cloud VM, and required spatial data are acquired through WCS and
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Fig. 4. Procedures used in the submerged crops analysis service chain. TABLE II S YSTEM C ONFIGURATIONS FOR THE T ESTS
Fig. 5. Distribution of the cloud nodes on Aliyun.
WFS from the data nodes, which are connected via the Internet. 3) Approach 3: the Cloud- and agent-based method proposed in this study. The configurations of the machines and Cloud VMs in the tests are listed in Table II. In the experiments, NetLimiter 3.0 was used to set the Internet bandwidth for the Cloud VMs to three different values (0.5, 1.0, and 2.0 Mb/s) to simulate various network conditions. To evaluate the performance and confirm that our approach addresses challenge (1), three comparisons were conducted
under different conditions. Fig. 7(a)–(c) shows test results for the execution time using the three approaches under different bandwidths, numbers of requests, and study area sizes.
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Fig. 6. Prototype system.
Fig. 7(a) shows the test results for network bandwidth of 2 Mb/s. The execution time for Approaches 1 and 2 increases dramatically with the number of concurrent requests and the size of the study area, whereas the performance of Approach 3 remains quite stable. For example, the execution time for Approaches 1 and 2 increases from seconds to approximately 1 h when the number of requests increases from 1 to 10 and the study areas increase from 1000 to 7000 km2 . By contrast, the execution time for Approach 3 only increases from seconds to approximately 5 min. This clearly demonstrates that Approach 3 has superior performance and that the performance of the SOA-based approaches may not meet requirements in an emergency situation. It is easy to understand the test results. Both SOA-based approaches need to transfer all of the spatial data online through geospatial data services (e.g., WFS and WCS). By contrast, our Cloud- and agent-based service chain only needs to transfer a very small volume of spatial data (e.g., GML data or single-color raster data) and algorithm agents. In fact, the data transfer time of the agent-based method can almost be neglected. In addition, a greater number of concurrent requests mean that more computing tasks need to be executed on the WPS server, which is usually a single server that is easily overloaded. By contrast, geospatial tasks can be distributed onto different Cloud VMs to keep the load balanced via agent computing in Approach 3. Hence, the execution time for Approach 3
does not increase greatly despite an increase in the number of requests and a sevenfold increase in study area size (Fig. 7). The results also show that the performance of Approach 1 is slightly better than that of Approach 2, because Approach 1 runs directly on physical machines with higher CPU capacity and a simulated distributed environment on the same LAN, whereas Approach 2 runs on virtual machines (VM) of the Aliyun Cloud connected via the Internet. To evaluate the effect of bandwidth on performance, we tested the three approaches using different bandwidths [Fig. 7(b)]. The results show that the SOA-based approaches are more severely affected by network conditions than our proposed approach because of the time required to transfer the data. This disadvantage of SOA-based approaches will largely restrict their use when the network bandwidth is limited, which is a typical situation in most developing countries. The difference in performance between the approaches becomes more obvious when varying network conditions are combined with multiple requests, as shown in Fig. 7(c). When the bandwidth decreases from 2 to 1 and 0.5 Mb/s for the same number of requests, the execution time for Approach 3 increases, but to a much lesser extent than for Approaches 1 and 2. Moreover, when the number of request increases and the bandwidth decreases, differences in execution time between our Cloud- and agent-based approach and the two SOA-based
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Fig. 7. Performance analysis. (a) Test for bandwidth of 2 Mb/s with different numbers of request and study areas. (b) Test for a single request with different bandwidth and study areas. (c) Test for an area of 7000 km2 with different bandwidths and multiple requests.
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approaches significantly increase. For example, for 10 requests and bandwidth of 0.5 Mb/s, the task execution time was >4.5 h for Approach 2, which is approximately tenfold the time for Approach 3. The execution time for Approach 1 was better than for Approach 2, but still was much worse than for Approach 3. In conclusion, the performance of our Cloud- and agentbased method is much better than that of the other two approaches when the study area is large and more data transfer is needed, especially when there are multiple requests and worse network conditions. These characteristics are very important for applications that use large-volume distributed spatial datasets, run under various network conditions, and have many concurrent users.
in a distributed environment based on the real-time distribution of geospatial data, computing resources, algorithm agents, and services. This capacity will be critical for effective and efficient execution of complex geospatial tasks, particularly when the scale of the Cloud is large and dynamic. In the future, efforts will be made to improve the capacity of the mobile agent in terms of movement and communication. The security of agent-based computing will also be considered. The security of spatial data and services on the Cloud will need to be improved when agent-based computing is adopted. Furthermore, the HPC capacity of Cloud computing can be utilized to develop high performance agent-based geospatial service chains. ACKNOWLEDGMENT
C. Role of Cloud Computing The rapid and automatic deployment of the agent environment SAI is critical for the computing mobility of the agent. However, it was difficult for us to deploy the SAI in the traditional distributed environment or the Grid. For example, the SAI needs to be installed on the nodes manually one by one in the grid. If many nodes are involved, it can take a long time to deploy the environment for agent computing in the grid. By using Cloud computing in the present study, the SAI could be deployed quickly by automatically launching the instance image, which is a typical IaaS service in Cloud computing [37]. Furthermore, although HPC was not considered in this study, it is easy to see that a high performance geospatial service chain could be implemented in the Cloud because of the excellent scalability of the Cloud. It is convenient to extend the scale of Cloud clusters, e.g., AWS EC2, which can start or stop HPC cluster nodes with the deployment environment in approximately 1 min, whereas a traditional HPC cluster would require several weeks to set up the deployment environment for the first time [38]. V. C ONCLUSION AND F UTURE W ORK We proposed an approach involving a Cloud- and agentbased geospatial service chain to address the major problems in constructing and executing a geospatial service chain, including the low efficiency of large-volume transfer of spatial data, the lack of requisite data on service servers, and the lack of geospatial services, which will tended to be more acute in the future as the volume, types and applications of EOS data increases continuously. Experiments on the analysis of submerged crops during a simulated flood in the Yangtze River basin demonstrated the feasibility, efficiency, and effectiveness of the proposed method and its better fulfillment of geospatial tasks during emergencies such as floods. Given the capacity of agent-based computing and the capabilities of Cloud computing, this method can prevent service chain failure, avoid large-volume transfer of geospatial data, and yield acceptable performance and robustness, even for multiple concurrent requests under various network conditions. Furthermore, the method can dynamically construct a geospatial service chain
The authors would to like to thank the anonymous reviewers, Prof. X. Cui for his detailed comments, which helped to improve the quality of the paper, M. F. Goodchild for his encouragement, and J. Poupart and G. Esworthy for their help in revising the language of the paper. The authors also thank the editors and the reviewers for their outstanding comments and suggestions, which greatly helped to improve the technical quality of the paper. R EFERENCES [1] OGC Web Map Server Implementation Specification (Version 1.3.0), OGC Standard, 21–38, 2006. [2] OGC Web Coverage Service Specification (Version 2.0.1), OGC Standard, OGC 09-110r4, 12–31, 2012. [3] OGC Web Feature Service Implementation Specification (Version 1.1.0), OGC Standard, 8–112, 2005. [4] OGC Web Processing Service Specification (Version 1.0.0), OGC Standard, 87, 2007. [5] M. Deng and L. Di, “GeoBrain online resources for supporting collegelevel data-intensive geospatial science and engineering education,” in Proc. IEEE Geosci. Remote Sens. Symp. (IGARSS), 2008, pp. IV-443– IV-446. [6] P. Yue, L. Di, W. Yang, G. Yu, and P. Zhao, “Semantics-based automatic composition of geospatial web service chains,” Comput. Geosci., vol. 33, no. 5, pp. 649–665, May 2007. [7] M. Deng and L. Di, “Building an online learning and research environment to enhance use of geospatial data,” Int. J. Spat. Data Infrastruct. Res., vol. 4, pp. 77–95, 2009. [8] M. Deng and L. Di, Facilitating Data-intensive Research and Education in Earth Science: A Geospatial Web Service Approach. Saarbrücken, Germany: LAMBERT Academic Publishing, 2010, pp. 97–102. [9] M. Deng et al., “Web-service-based monitoring and analysis of global agricultural drought,” Photogramm. Eng. Remote Sens. (PE&RS), vol. 79, no. 10, pp. 929–943, Oct. 2013. [10] P. Yue, J. Gong, and L. Di, “Augmenting geospatial data provenance through metadata tracking in geospatial service chaining,” Comput. Geosci., vol. 36, no. 3, pp. 270–281, Mar. 2010. [11] P. Bellavista, A. Corradi, and C. Stefanelli, “Mobile agent middleware for mobile computing,” Computer, vol. 34, no. 3, pp. 73–81, Mar. 2001. [12] C. Bryce and J. Vitek, “The Javaseal mobile agent kernel,” Auton. Agents Multi-Agent Syst., vol. 4, no. 4, pp. 359–384, 2001. [13] R. S. Gray et al., “D’Agents: Applications and performance of a mobileagent system,” Softw. Pract. Exper., vol. 32, no. 6, pp. 543–573, May 2002. [14] X. Tan and F. Bian, “P2P and agent service based on-line 3DGIS,” in Proc. Web Wireless Geogr. Inf. Syst., vol. 4295, pp. 170–179, 2006. [15] K. Sivashanmugam et al., “Framework for semantic web process composition,” J. Electron. Commerce, vol. 9, no. 2, pp. 71–106, Dec. 2004. [16] I. B. Arpinar, R. Zhang, and B. Aleman-Meza, “Ontology driven web services composition platform,” Inf. Syst. e-Bus. Manage., vol. 3, no. 2, pp. 175–199, Jul. 2005.
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Xicheng Tan received the B.Sc. degree in surveying engineering from Taiyuan University of Science and Technology, Taiyuan, China, in 2002, the M.S. degree in geographical information systems from Wuhan University, Wuhan, China, in 2004, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, in 2007. From 2013 to 2014, he was a Visiting Scholar with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA. Currently, he is an Associate Professor of Geographic Information Science with the International School of Software, Wuhan University. His research interests include geospatial web service, distributed computing, cloud computing, high performance computing, and 3-D GIS.
Liping Di (M’01–SM’06) received the B.Sc. degree in remote sensing from Zhejiang University, Hangzhou, China, in 1982, the M.S. degree in remote sensing/computer applications from the Chinese Academy of Sciences, Beijing, China, in 1985, and the Ph.D. degree in geography from the University of Nebraska–Lincoln, Lincoln, NE, USA, in 1991. He was a Research Scientist with the Chinese Academy of Sciences, from 1985 to 1986, and the NOAA National Geophysical Data Center, Boulder, CO, USA, from 1991 to 1994. He served as a Principal Scientist from 1994 to 1997, and a Chief Scientist from 1997 to 2000 with Raytheon ITSS, Lanham, MD, USA. Currently, he is a Professor of Geographic Information Science and the Director with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA. His research interests include remote sensing, geographic information science and standards, spatial data infrastructure, global climate and environment changes, and advanced earth observation technology.
Meixia Deng received the B.S. degree in engineering mechanics from Huazhong University of Science and Technology, Wuhan, China in 1990, the M.S. degree in computer science from University of Missouri– Columbia, Columbia, MO, USA, in 2000, and the Ph.D. degree in computational science and informatics from George Mason University, Fairfax, VA, USA, in 2009. She is a Research Associate Professor and Associate Director with the Center for Spatial Information Science and Systems (CSISS), George Mason University. She was elected as an International Representative (IR) Officer for the International Committee for Information Technology Standards (INCITS), Geographic Information Systems, in May 2013 for a 3-year term. Her research interests include data and computational science, information technology and standards, geoinformation science, and Web-service-based geospatial data, as well as information and knowledge systems.
Aijun Chen received the Ph.D. degree in cartography and geographic information system from Peking University, Beijing, China, in 2000. He did the 2-year postdoctoral research at Tsinghua University, Beijing, China, after he received the Ph.D. degree. He had been a Research Associate Professor with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA, from 2009 to 2014. He joined NOAA/NGS, Silver Spring, MD, USA, in April, 2014 as a Senior GIS Specialist. His research interests include standards-based remote sensing data and information sharing, geospatial web services, geospatial grid computing, workflow-based geospatial modeling and simulation, virtual globes-based visualization of remote sensing data, and geospatial cloud computing.
Fang Huang received the B.Sc. degree in surveying and mapping engineering from Taiyuan University of Science and Technology, Taiyuan, China, in 2002, the M.S. degree in photogrammetry and remote sensing from Beijing Jiaotong University, Beijing, China, in 2005, and the Ph.D. degree from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, in 2008. Currently, he is an Associate Professor with the University of Electronic Science and Technology of China (UESTC), Chengdu, China. His research interests include putting the cut-edge computing techniques, e.g., the parallel computing, grid computing, cloud computing, and even heterogeneous computing into RS/GIS applications.
Chao Peng was born in Chongqing, China, in 1991. He received the B.Sc. degree in spatial information and digital engineering from Wuhan University, Wuhan, China, in 2014, and he is a Graduate Student at Software Engineering of School of Software, Tsinghua University, Beijing, China. His research interests include data mining, machine learning, and cloud computing.
Meng Gao was born in Hubei, China, in 1993. He is a Student in spatial information and digital technology of International School of Software, Wuhan University, Wuhan, China. His research interests include cloud computing, high-performance computing, and geospatial Web service.
Yayu Yao was born in Hubei, China, in 1993. He is a Student in spatial information and digital technology of International School of Software, Wuhan University, Wuhan, China. His research interests include cloud computing, geospatial web service, and spatial big data analysis.
Zongyao Sha received the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2003. He is a Professor with the Department of Spatial Information and Digital Engineering, International School of Software, Wuhan University. He comes with interdisciplinary GIS, remote sensing, and software engineering. His research interests include service-oriented programming for GIS applications and geospatial modeling via remote sensing and GIS technology.
