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Design and Implementation of a Multi-Grid Resource Broker for Grid Computing Chao-Tung Yang and Wen-Jen Hu Department of Computer Science, Tunghai University, Taiwan (ROC)

Abstract Grid computing integrates geographical computing resources across multiple virtual organizations to achieve high performance computing. A single grid often could not provide huge resources, because virtual organizations have no adequate of computing resources restriction on the scale of organizations. In this paper, we present a multi-grid, new grid architecture for integrating multiple computational grids from different virtual organizations. A resource broker is built on multiple grid environments; it integrates a number of single grids from different virtual organizations without the limitation of organizations. The multiple grid resource could be utilized efficiently and precisely. Keywords

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Grid computing, Multi-Grid, Resource Broker

INTRODUCTION

Grid technology plays a major role in tackling large-scale problems by integrating distributed resources to provide users with a supercomputer liked capacity for data sharing and computation [1, 2, 3, and 4]. Participating sites may be physically distributed, heterogeneous, and governed by different administrative domains. Many grid related studies and projects has been proposed for solving large scale scientific problems such as earthquake simulation, atmosphere and ocean simulation, high energy and nuclear physics, astronomy, bioinformatics [5], and medical image processing [6]. There are more and more proposed grid projects such as Globus [4, 7], Condor, LEGION, Grid PP, EGEE, P-Grid, DutchGrid, ESnet, and Grid Bus. We use the Ganglia and NWS tools to monitor resource status and networkrelated information [8, 9, and 10], respectively. Understanding influence of each parameter is not only crucial for an application to achieve good performance, but would also help to develop effective schedule heuristics and design high quality Grids. In Taiwan, some research institutes has devoted to build grid platforms for academic research use such as Tiger Grid [11] and Medical Grid [6] that integrate available computing resources in some universities and high schools. They respective use their grid resource, but some of virtual organizations have no the adequate of computing resources restriction on the scale of organizations [15]. For this reaS.C. Lin and E. Yen (eds.), Data Driven e-Science: Use Cases and Successful Applications of Distributed Computing Infrastructures (ISGC 2010), DOI 10.1007/978-1-4419-8014-4_20, © Springer Science+Business Media, LLC 2011

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son, we construct a multi-grid resource broker [13] integrate those single grid environment as a multiple grid environment so that resources can be used more effectively. In this paper, we propose a multi-grid resource broker architecture to solve the resource sharing problems in grids [14, 15, and 16]. When a new grid joins in the multi-grid environment by the proposed system, the user can use entire multi-grid resources, and get more computing resource. This approach could help these virtual organizations to possess more computing resources without any extra overhead. We chose the Globus Toolkit as the middleware of grids. Although Globus provides a monitor tool, which is called MDS, is not capable of providing the rich set of all the requisite information. Thus we used another monitor tool “Ganglia” [17, 18, 19, and 20] on the multi-grid system. The Ganglia is a scalable open source distributed system for monitoring status of nodes in wide-area systems based on clusters.

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MULTI-GRID RESOURCE ARCHITECTURE

The system architecture of the resource broker is shown in Fig. 1. Users could easily make use of our resource broker through a common Grid portal [21, 22, 23, and 24]. The primary task of Resource Broker is comparing requests of users and resource information provided by Information Service. After choosing the appropriate job assignment scheme, Grid resources are assigned and the Scheduler is responsible to submit the job. The results are collected and returned to Resource Broker. Then, Resource Broker records results of execution in the database of Information Center through the Agent of Information Service. Users can query the results from Grid portal. Our resource broker architecture includes four layers: web portal, resource brokering subsystem, multi-grid manager center, and multi-grid resource. The multi-grid resource consists of many single grid environments. When a new single grid joins our multi-grid environment, the grid administrator should register at the web portal and provide the personal file and the related information of the grid, such as the CA package, the grid-mapfile of Globus, and the machine lists. The multi-grid system deploys the environment according to the registered information, and then assigns a resource broker account to a single grid user. This account could access the resource which was supported by the resource broker on the multi-grid environment, Fig. 1 shows the concept. The multi-grid manager center has cross grid authentication service and cross grid information service, that is the important part on entire multi-grid environment, which controls the access of grid and the information gather, the details described in following section.

Design and Implementation of a Multi-Grid Resource Broker for Grid Computing

Fig. 1: Resource Broker system architecture

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Chao-Tung Yang and Wen-Jen Hu

SYSTEM DESIGN AND IMPLEMENTATION

Software Stack Diagram

The system software stack includes three layers which were constructed using a bottom-up methodology. The layers are described below:  Bottom Layer: principally consists of Nodes, as shown in Fig. 2. The layer contains two main blocks, the Information Provider, which uses Ganglia to gather machine information on Nodes, such as number of processors/cores, processor loading, total/free memory, and disk usage, and NWS, which gathers essential network information such as bandwidth and latency. The second block contains Grid Middleware, used to join Grid Nodes together, and the MPICH-G2 required for running parallel applications on the Grid.  Middle Layer: contains grids as shown in Fig. 3. Each grid consists of several nodes located in the same place or connected to the same switch/hub. All nodes in a site are connected to each other and to the Internet. Moreover, grids are usually built as clusters with each node having a real IP.  Top Layer: contains the Resource Broker, the Monitoring Service, and the multi-grid manager, as shown in Fig. 3. The Resource Broker coordinates Grid resources, dispatches jobs to resources, and monitors job statuses. One contribution of this work is proposing a resource allocation scheme that can handle user jobs requiring more Grid resources than one dedicated cluster can supply. To make strategic decisions in dispatching jobs, the Re-source Broker needs currently information on the Grid from the Monitoring Service, which also provides a web front-end for users to observe job progress. Monitoring Service, which also provides a web front-end for users to observe job progress. The task of multi-grid manager is integrating the grids into the multi-grid environment, which includes the cross grid authentication service and cross grid information service.

Fig. 2: The software stack of all nodes

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Fig. 3: The software stack of all sites and the service

3.2 Cross Grid Authentication Service The Globus Toolkit authentication is issued by the certificate of GSI. Each user and service is certificated to identify and authenticate the trust between users or services. If two parties have the certificate and both of them trust the CAs, then these two parties could trust each other. This is known as the mutual authentication. A subject name, which identifies the person or object that the certificate represents. The cross grid authentication service manages the several certificates and the subject of grid, and we know these messages from the registered information via web portal. All of nodes on multi-grid environment should setup the cross grid service tool, which written in the shell script program, show in Fig. 4. The cross grid service tool contains some procedures to regular automatic update with multi-grid manager center, such as the IP and domain of host list, the certificates, and the subject. Any single grid user on our multi-grid environment makes use of the multi-grid resource through the tool to update with multi-grid manager center. The primary task of cross grid information is gathering the IDL file, which contains the several attributes of hosts on grid, the detail describe in next section.

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Fig. 4: Multi-Grid Manager Center

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Cross Grid Information Service

The monitor tool is one of the most important components in a grid environment [17, 18, and 25]. A good monitor tool could help grid administrator to manage and monitor the machines. On the other hand, the middleware could gather the resource information to find the suitable resource via the monitor tool. There are common monitor tools, such as MDS, Ganglia, Cacti, and Condor. On a multi-grid environment, grids use different monitor tools with different information formats. Therefore, we define a new information format to integrate different formats. The proposed format is called IDL (Information Describing Language), as shown in Fig. 5. It is responsible to exchange and translate resource information among grids, and to perform the post-transfer filtering to ensure that only necessary information is passed to clients, end users, and software components.

Design and Implementation of a Multi-Grid Resource Broker for Grid Computing

Fig. 5: Information Describing Language.

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Fig. 6: Cross Grid Information Service architecture

The CGIS consists of three layers: the Core Service Layer, the Translator Layer, and the Resource Layer, as shown in Fig. 6. The Core Service Layer contains the Agent, Filter, Getter and Setter, and Gather. These components are installed in every grid environment for information gathering and maintenance. The Translator Layer supports a variety of format conversion monitor tools, such as Ganglia, Cacti, and MDS. The information in different monitor tools would be transformed into the IDL format. The Resource Layer describes resource information from different grid environments. In this study, it includes Tiger Grid, Medical Grid, and Bio Grid [15].

3.4 Web Portal We provides a registration page for a new single grid to join our multi-grid environment, the register service will automatic send mail to the multi-grid administrator, administrator adjustment and configure the setting of multi-grid environment as receive the registration page. Simultaneously, the register service informs the new single grid to download the cross grid service tool from multi-grid server for finishing the step of registration, as shown in Figure 7.

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Fig. 7: Registration page of Multi-Grid Resource Broker

We use IDL XML format to exchange of the information between multi-grid resources. The collected information not only is utilized while submitting job, but also be referenced while monitoring the status of grids. The Monitor Service could monitor the resources supported by the multi-grid resource broker. We provide the interface to observe the status of Hosts Info, Daemons, and Ganglia. Hosts Info show the status include the number of CPU, CPU speed, the average of load in one minute, the average of load in five minute, the size of free memory and the size of free swap , as shown in Figure 8. Daemon Status is another service that monitors the services’ status of machines by the NMAP, which parses the list of host by IDL file and scans the fix ports through the list, and then gets the status whether the service to be on or not, as shown in Figure 9. In this study, we rewrite the Ganglia codes and modify its setting to support the multiple grids. Our Ganglia page presents the multi-level architecture: Tiger Grid, Medical Grid, Bio Grid, and GCA Grid in the web portal, as shown in Figure 10.

Design and Implementation of a Multi-Grid Resource Broker for Grid Computing

Fig. 8: Hosts Info

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Fig. 9: Daemon Status

The Tiger Grid contains the network measure service; we use the NWS to achieve the point to point network bandwidth measure. However, the NWS tool lacks the database to conserve the statistics, so we stored the statistics into roundrobin database every five minutes, and then draw the graph through JRobin, as shown in Figure 10. JRobin is a 100% pure Java implementation of RRDTool's functionality. The function pages have a web page of help manual in our web portal, which describes the capability of page and the function of hyperlinks. With the friendly manipulated interface, details are abstracted from the users who no longer have need of expertise in multi-grid environment, as shown in Figure 11.

Fig. 10: NWS Information

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Fig. 11: Help Page

The user could execute a parallel program via the resource broker and describe the requirements for the execution of the hosts when submitting the job, as shown in Figure 11, and retrieve the result form the Job Monitor Service, which shows the many detail messages in execute time, such as scheduling log, machine list, result message, running log, debug message, and turnaround time.

Fig. 12: Job Monitor

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CONCLUSIONS AND FUTURE WORK

In this paper, we construct a resource broker for multi-grid computational environment which integrates the four single grids into our environment, and provides the cross grid service. The resource broker facilitates users to submit job via web portal. The user executes the workflow to achieve more high-performance computing by web portal without comprehend complicated instructions, and that can monitor status of multi-grid or grid itself. With the multi-grid platform, as more and more virtual organizations join into the multi-grid environment, a huge computing resource would be progressively grown up. In the future, we focus on efficiently integrating the different grid middleware, such as Globus and GLite. Although most organizations utilize the Globus as the grid middleware in Taiwan, the GLite still has plenty of users in global. We will continually invite other universities or research centers to join our architecture, and allocate the account to use resource broker.

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