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Service-Oriented Network Description and Discovery for High-Performance Grid Computing Qiang Duan Computer Science Department University of Central Arkansas Conway, AR 72035 Abstract Grid computing is an emerging computing paradigm that will have a significant impact on the next generation information infrastructure. Networking systems form the foundation of Grid computing and must be integrated into the Grid. Service-oriented architecture is the basis of Grid computing; therefore service-oriented network description and discovery are the keys to network-Grid integration. High-performance Grid computing must select appropriate network services that meet the Quality of Service (QoS) requirements of various Grid applications. However, current Grid service description and discovery approaches are based on functional criteria instead of performance criteria thus are insufficient to support network service discovery. In this paper, a new description approach is proposed for describing QoS capabilities of network services. Based on this new description approach, a performance-based service discovery technology is developed for selecting network services that meet the QoS requirements of Grid applications. The network service description and discovery technologies developed in this paper are highly flexible and applicable to various networking systems crossing multiple heterogeneous domains. The network service discovery system proposed in this paper is embedded in the serviceoriented Grid architecture and can be implemented within the current Grid service discovery framework.

1 Introduction The rapid growth of the Internet, along with the availability of powerful computers and high-speed networks as low-cost commodity components has enabled the utilization of a wide variety of geographically distributed computational resources, including computers, storage systems, data sources, and special devices, as a unified resource. This new paradigm that has evolved is popularly termed as “Grid”

computing. The federation of highly distributed heterogeneous resources to deliver better-than-best-effort services is a key feature of Grid computing. Therefore, networking systems with Quality of Service (QoS) provisioning capabilities form the foundation for high-performance Grid computing. The Service-Oriented Architecture (SOA) denotes a set of architectural principals that play the key role for realizing Grid computing. In the service-oriented Grid architecture, each Grid service publishes a service description. When an application needs to utilize the Grid infrastructure, a service broker discovers a service that meets the application requirement, and then binds the service with the application. In order to exactly define the core Grid services and facilitate their deployment on all scales, the Open Grid Forum (OGF) defines the Open Grid Service Architecture (OGSA) standard [11]. Since networking is the basis of any geographically distributed resource sharing, computer networks must be integrated into the Grid architecture as services for being utilized effectively by Grid applications. Toward this end, OGF recently formed the “Grid High-Performance Networking” research group and defined the Grid network service as a network service that has roles and/or interfaces that are deemed to be specific to a Grid infrastructure [14]. The description and discovery technologies for Grid network services play a key role in the integration of networking systems into the Grid architecture. Current Grid service description is based on the Web Service Description Language (WSDL). The WSDL-based service description only provides information about functions and invoking interfaces of a service. A WSDL file does not give any information about the QoS capability of a service, such as the minimum service rate and the maximum delay guaranteed by the service provider to a client. Therefore, current Grid service discovery mechanism is based on functional criteria instead of performance criteria; that is, the Grid service broker selects a service without considering the achievable QoS performance of the service. However, network QoS is

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significant for supporting high-performance Grid computing and the service broker must select the network services that can guarantee the QoS performance required by Grid applications. Although the research community is making progresses toward QoS-capable service description and discovery, current available research results are not sufficient yet for Grid network service discovery. Therefore, new approaches for describing the QoS capabilities of network services and discovering network services based on the achievable QoS performance are required by high-performance Grid computing. One of the main difficulties in performance-based network service discovery lies in the heterogeneity of Grid networking systems. A general approach for describing and discovering various networking systems that cross heterogeneous domains is required. In this paper, a new approach is proposed for describing the QoS capabilities of Grid network services. This approach is applicable not only to various network implementations but also to networking systems that consists of a set of heterogeneous domains. Based on this new description approach, a technology is developed for discovering Grid network services that guarantee the QoS performance required by Grid applications. The description and discovery technologies developed in this paper provide a complement rather than a replacement to the current Grid service discovery standard for enhancing Grid networking performance. The rest of this paper is organized as follows. Section 2 introduces service description and discovery for Grid computing and Grid network services. Section 3 proposes a new description approach for Grid network services. A technology for performance-based discovery of Grid network services is developed in section 4. Numerical examples are given in Section 6 to illustrate the applications of the developed techniques. Section 7 discusses a system architecture for Grid network service broker and Section 8 draws conclusions.

2 Grid Network Services and Network-Grid Integration In the service-oriented Grid architecture, a service is a self-contained implementation of some function(s) with a well-defined interface specifying the message exchange pattern used to interact with the function(s). A Grid service should provide descriptive information about its functions and the required interface for accessing the service. This descriptive information is called a service description, which is published at a service registry. When a computing application needs to utilize the Grid, the application submits a job request to a service broker and specifies the required functions. The service broker searches service descriptions published at the registry to discover a qualified service that

supports the functions required by the application. Then the service broker retrieves the necessary binding information of the selected service and binds it with the application accordingly. Discovering the appropriate service for each application is the key to high-performance Grid computing and service descriptions form the basis for successful service discovery. Therefore, service description and discovery play crucial roles in the service-oriented Grid architecture. Central role in interconnecting the distributed resources in Grids is given to high-performance networking. For effectively utilizing the underlying networking systems in Grids, it is essential to integrate networks into the Grid architecture as services. Toward this end, the OGF recently formed the “Grid High-Performance Networking” research group and developed the notion of Grid network service, which is defined as a network service that has roles and/or interfaces that are deemed to be specific to a Grid infrastructure [14]. Through Grid network services, networking resources are seen joining other computational resources, such as CPU and storage, as Grid-managed resources and fully integrated in the Grid architecture. Currently Grid service description is based on the Web Service Description Language (WSDL) [12]. A WSDL document typically contains two groups of definitions: an abstract interface and a concrete implementation interface. The abstract interface describes what the service does in terms of the messages it consumes and produces without considering how and where that service is offered. The implementation interface contains the implementation details needed to contact and invoke the service. Essentially a WSDL service description provides the information about what function(s) the service supports and how the function(s) are invoked. There is no information about how well the service can be offered by the provider, for example the minimum service rate and the maximum service delay. We refer to such QoS-related information about a service as the capability description of the service. Since service discovery is based on service descriptions, which currently only provide function and interface information, current Grid service discovery standard is function-based instead of performance-based; that is, the Grid service broker selects any service that supports the required function(s) without considering the achievable QoS performance. The network-Grid integration requires new mechanisms for describing and discovering Grid network services. Since network QoS has significant impact on Grid computing performance, it is extremely important to discover the Grid network services that can guarantee the performance required by Grid applications. Therefore, the description and discovery mechanisms for Grid network services should be performance-based instead of function-based. Recently some research results on performance-based Grid service discovery have been reported in literature.

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R. Al-Ali and his colleagues developed the G-QoSM framework for supporting QoS-based Grid service discovery by extending the UDDI service registry [1, 2]. Yu and Lin designed a QoS capable broker algorithm for web services discovery [10]. B. Ambros and his coauthors proposed a system in [3] that can predict web service run-time performance for dynamic service selection. B. Chan and his colleagues extended the WSDL with an optional attribute in order to describe the QoS levels of Grid services [5]. The WS-Policy specification [13] that is currently under development is an attempt of the web service community to standardize the description approach for nonfunctional service capabilities, such as QoS and security. Although the research community is making progresses toward QoSbased Web/Grid service discovery, the current results are mainly focused on the Grid services that provide data processing and/or storage functions instead of data communication functions. Networking systems have special features that distinguish them from regular Grid services. Therefore, the obtained techniques may not be applied directly to Grid network service description and discovery. One of the main challenges of describing QoS capabilities of Grid network services lies in the heterogeneity of Grid networking systems. Due to the wide geographical distribution of resource sharing in Grids, it is very likely that the underlying networking system for a Grid consists of multiple network domains with various implementations. Therefore, the description and discovery approach for Grid network services must meet the following requirements: first, the approach should be applicable to various networks without different implementations; second, the approach must support the composition of multiple heterogeneous network domains into one cross-domain network service. This paper will develop network service description and discovery techniques that meet these requirements. The author noticed that Grid network services can be classified into two categories: data delivery services and other services for network control and management purposes. This paper focuses on the first service category but the developed techniques are also applicable to the second category.

3 Capability Description for Grid Network Services The main function of a Grid network service is data delivery and the provisioning capability for data delivery includes two aspects: the destinations can be reached by the network and the achievable QoS performance for data delivery to each destination. In this paper, the former aspect is referred to as reachability and the latter aspect is called QoS capability of the service. Reachability can be described by giving all pairs of sources and destinations between which the network can transfer data. QoS capabilities

vary from network to network but typically include the minimum bandwidth and the maximum delay for data transfer. In order to provide a formal description for network service capabilities, we define the Capability Matrix C that describes both reachability and QoS capability for a network service. Given a network service S with m ingress ports and n egress ports, the capability matrix C for this service will be a m × n matrix whose elements are denoted as ci,j (i = 1, · · · , m; j = 1, · · · , n); that is,   c1,1 c1,2 · · · c1,n  c2,1 c2,2 · · · c2,n   C= (1)  . . . . . . . . . . . . . . . . . . . . . . cm,1 cm,2 · · · cm,n

Each element ci,j in C is defined as follows. Let Ri,j be the network route from the ingress i to the egress j provided by S, then ( 0 if Ri,j does not exist ci,j = (2) Si,j if Ri,j exists

where Si,j is the QoS descriptor for the route Ri,j , which will be developed in the rest of this subsection. According to the definitions given in (1) and (2), the capability matrix element ci,j = 0 if the network service cannot reach j from i. If the network service has a route from i to j, then the QoS capability of this route is given by the descriptor Si,j . In a Grid network service the routes between different ingress-egress pairs may have different implementations. Therefore, the key requirement for the QoS descriptor is to be applicable to various networking systems. To achieve this objective, the notion of service curve in the network calculus theory is adopted by this paper to define the QoS descriptor. The network calculus theory has been developed to be an effective tool for network modeling and analysis [4]. The service curve is defined as follows in network calculus. Let Rin (t) and Rout (t) respectively be the accumulated amount of traffic of a flow that arrives at and departs from a server by time t. Given a non-negative, non-decreasing function, S(·), where S(0) = 0, we say that the server guarantees a service curve S(·) for the flow, if for any t ≥ 0 in the busy period of the server, Rout (t) ≥ Rin (t) ⊗ S(t)

(3)

where ⊗ denotes the convolution operation in minplus algebra, which is defined as h(t) ⊗ x(t) = inf s:0≤s≤t {h(t − s) + x(s)}. Essentially a service curve gives the minimum amount of service offered by the server to a client in an arbitrary time interval within a busy period. Such a curve describes the lower bound of the service provisioning capability offered to a client.

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A typical server model for networking systems is the Latency-Rate (LR) server [9], which guarantees each flow a service curve βr,θ (t) = r(t − θ), where θ and r are respectively called the latency and service rate for the flow. LR server is a general server model for networks. Packet schedulers that are widely deployed in practical networking equipments, such as weighted fair queuing (WFQ) and weighted round-robin (WRR), belong to this server category. In our service description approach, we adopt the service curve guaranteed by the route Ri,j as the QoS descriptor Si,j used in the capability matrix C. Since a service curve is a general data structure that is independent with network implementations, it is flexible enough to describe heterogenous networking systems. In a typical networking system where a route Ri,j can be modeled by a LR server with a service curve ri,j (t − θi,j ), the matrix element ci,j can be represented by a data structure [ri,j , θi,j ] with two parameters: the service rate and the latency. The end-to-end Grid network service utilized directly by a Grid application is very likely a service consisting of a set of network domains, each of which is abstracted as a network service. Therefore, how to compose the QoS capabilities of a set of heterogenous network links into one descriptor for the end-to-end route is an important and challenging problem. The service curve-based description approach supports composition of QoS capabilities. Assume that a service system consists of a series of tandem servers G1 , G2 , · · · , Gn , which respectively guarantees the service curves S1 (t), S2 (t), · · · , Sn (t) to a flow, it is known from network calculus that the service curve guaranteed by the entire system to this flow, S(t), can be obtained through the convolution of the service curves guaranteed by each server; that is, (4)

S(t) = S1 (t) ⊗ S2 (t) · · · ⊗ Sn (t).

Since typical networking systems can be modeled as LR servers, we are particularly interested in composition of LR servers. Suppose each network server Si guarantees the flow a service curve βri ,θi (t) = ri (t − θi ), it can be proved that the convolution of these service curves is (5)

βr1 ,θ1 (t) ⊗ · · · , ⊗βrn ,θn (t) = βr,θΣ (t)

4 Performance-Based Discovery for Grid Network Services In this section we develop a technology that enables the service broker to discover Grid network services that meet performance requirements of Grid applications. This newly developed technology focus on network service selection while other components of the discovery mechanism, including publishing service descriptions, searching the registry for available services, and binding the selected service with the application, can be implemented based on the current Grid service discovery standards. A service broker needs three aspects of information in order to conduct performance-based network service discovery for a Grid application: (a) the provisioning capabilities of available network services; (b) the performance requirement of the application; and (c) the character of network traffic load generated by the application. The information (a) can be obtained from the capability matrix C published by the network service provider. The last two aspects of information, which specify the demand of a Grid application on the network service, should be provided to the service broker by the application as part of its request. This paper ¯ L, ¯a) as a general specifidefines a Demand Profile P (d, cation of Grid application demands. This profile consists of three elements: the pair of source-destination for data ¯ a traffic load descriptor L; and a transfer, denoted by d; performance requirement set a ¯. The source-destination pair d can be specified by two network addresses, one for the source and one for the destination. The set a ¯ consists of the performance parameters required by the Grid application. Different parameters may be included in a ¯ for different applications. The minimum bandwidth breq and the maximum delay dreq for data delivery are typical requirements, i.e. a ¯ = {breq , dreq }. The descriptor L is used to characterize the network traffic that the Grid application will load on a network service. In order to describe application traffic load in a general form, we employ the arrival curve as the traffic descriptor L in the demand profile. Let Rin (t) denote the accumulated amount of traffic generated from an application by time t. Given a non-decreasing, non-negative function, A(·), the application is said to have an arrival curve A(·) if Rin (t) − Rin (s) ≤ A(t − s) ∀ 0 < s < t.

where r = min {r1 , r2 , · · · , rn }

and θΣ =

n X

θi

(6)

i=1

The equations (5) and (6) imply that the total latency of an end-to-end network route is described by the summation of the latency parameters of all links on the route, and the bandwidth of the end-to-end route is limited by the link with the least transmission rate.

(7)

Essentially the arrival curve of an application gives the upper bound of the amount of traffic the application can load on the network service. Currently most QoS-capable networks apply traffic regulation mechanisms at network boundaries to shape arrival traffic from applications. The traffic regulators most commonly used in practice are leaky buckets with a peak rate enforcer. A traffic flow constrained by a leaky bucket has

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an arrival curve A(t) = min {P t, σ + ρt}, where P , ρ, and σ are respectively the peak rate, the sustained rate, and the maximal burst size of this flow. Now we develop a technique for the service broker to predict the QoS performance that can be guaranteed by a network service to an application, thus deciding if the network service meets the application requirement. Among various performance requirements, this paper focuses on the minimum bandwidth and the maximum delay for data delivery, which are the most important performance parameters of high-performance networking for Grid applications. Network calculus provides an effective approach for analyzing the minimum bandwidth and maximum delay guaranteed by a network service. A service curve itself is a description of the minimum service capacity offered by a network, which essentially gives the minimum bandwidth guaranteed by the network to an application. Therefore, given the QoS descriptor for a route Ri,j in a network service S, which is described by a service curve Si,j (t), the minimum bandwidth guaranteed on the route can be determined as bmin = lim [Si,j (τ )/τ ]. (8) τ →∞

Suppose the load descriptor L of a Grid application is given by the arrival curve A(t), then the maximum data transfer delay dmax guaranteed by the service S for this application can be determined from the maximal horizontal distance between the service curve and the arrival curve; that is, dmax = max {min {δ : δ ≥ 0 and A(t) ≤ S(t + δ)}} . t:t≥0

(9) Since the LR server is a typical network model and the leaky bucket is a typical traffic regulator, we specifically give performance analysis for a network route modeled by a LR server under traffic load constrained by a leaky bucket regulator. Suppose the arrival curve for application traffic is A(t) = min{P t, σ + ρt} and the QoS descriptor of the network route is S(t) = r(t−θ), then the minimum bandwidth can be guaranteed to the application is   rθ r(τ − θ) bmin = lim = lim r − =r (10) τ →∞ τ →∞ τ τ is

The maximum delay for any data bit of this application   P −r σ dmax = θ + for (r ≥ ρ). (11) P −ρ r

After determining the QoS performance guaranteed by a network service S to the Grid application, including the minimum bandwidth bmin and the maximum delay dmax , the service broker compares these parameters with the performance requirement given in the set a ¯ to decide if this network service can be selected for the application. Grid

applications can be classified into three categories according to their performance requirements: (a) applications with only bandwidth requirement; i.e. a ¯ = {breq }; (b) applications with only delay requirement; i.e. a ¯ = {dreq }; (c) applications with both bandwidth and delay performance requirements; i.e. a ¯ = {breq , dreq }. If the application belongs to category (a), then the network service S can be selected only when bmin ≥ breq . If the application belongs to category (b), then the network service S can be selected only when dmax ≤ dreq . If the application belongs to category (c), then the network service S can be selected only when bmin ≥ breq and dmax ≤ dreq . If there are multiple network services meet the performance requirements, selection among them may be based on other criteria such as service cost or load balance.

5 Resource Allocation for Network Service Provisioning In order to actually guarantee the performance required by a Grid application, the network service selected by the Grid service broker should allocate sufficient amount of resources. One of the main networking resources is bandwidth. Equation (11) gives the relation between the achievable delay performance and available bandwidth, which implies that the required delay upper bound can be guaranteed by allocating sufficient amount of bandwidth. Analysis of (11) shows that the minimum possible delay upper bound Dmin = θ is achieved when the allocated bandwidth ra = P . For any delay requirement dreq > Dmin , the network service must guarantee this application a delay upper bound that is no greater than dreq ; that is,   P −r σ ≤ dreq . d=θ+ (12) P −ρ r Therefore, the bandwidth that must be allocated for meeting (12) is Pσ . (13) ra = (P − ρ)(dreq − θ) + σ Equation (11) also implies that allocated bandwidth ra = ρ is sufficient for any delay requirement dreq ≥ Dmax = θ + σ/ρ. In summary, the bandwidth allocation for guaranteeing a delay requirement dreq can be determined as  dreq ≥ Dmax  ρ σ D ra = (P −ρ)(dPreq min ≤ dreq < Dmax −θ)+σ   cannot be guaranteed dreq < Dmin (14) where Dmax = θ + σ/ρ and Dmin = θ. If the application has both delay and bandwidth requirements; i.e. a ¯ = {breq , dreq }, then the minimum amount of

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dmax (ms)

bandwidth that must be allocated for this application will be

150

bmin = max{ra , breq }.

Specifically we analyze resource allocation for network services that can be modeled by the weighted fair queueing (WFQ) server [8], which is widely deployed in practical networking equipments. It is known from [9] that at  1 a WFQ, the latency for a traffic flow is θ = L R + 1r , where L is the maximum packet length of this flow, R is the total transmission capacity of the network route, and r is the bandwidth available to the flow on this route. For a Grid application that generates a leaky bucket constrained flow f with traffic parameters (P, ρ, σ), if the QoS descriptor of the route provided by a network service S for the application is S = [r, θ], then by using (11) we can predict that the maximum delay guaranteed by the service S for the flow f is     1 P −r σ 1 dmax = L + + . (16) R r P −ρ r The bandwidth allocation requirement can be determined from (14). Given a required delay dreq that is between Dmin and Dmax , ra =

P σ + L(P − ρ) . (P − ρ)(dreq − L/R) + σ

dsmax dlmax

(15)

(17)

6 Numerical Example This section gives numerical examples to illustrate the application of the performance prediction and bandwidth allocation techniques developed in this paper. Our examples assume that the route provided by a network service S is modeled by a WFQ server. We assume that the traffic flow f generated from the Grid application is a stream of video packets with the parameters given in [6]; that is, the peak rate P = 3.9 Mb/s, the sustained rate ρ = 1.1 Mb/s, and the maximum burst size σ = 143 kbits. Such a video flow is select in the example because real-time video delivery with a maximum delay constraint is a typical networking function for high-performance Grid computing. We predicted the delay performance that can be guaranteed for the flow f with various amounts of available bandwidth on the route assigned to the flow. The results are plotted in Figure 1. We noticed from (16) that given the traffic load parameters (P, ρ, σ), the relation between dmax and available bandwidth r is associated with the packet length L. Therefore, we considered two cases: the short packet case where L = 53 bytes and the long packet case where L = 1500 bytes. An example of the former case is the ATM network with a fixed 53 byte cell length; and an example for the latter case is Ethernet with a 1500 bytes maximum transmission unit (MTU). The delay upper bounds for

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r(Mb/s)

Figure 1. Available bandwidth and predicted maximum delay

this two cases are respectively denoted by dsmax and dlmax in Figure 1. From this figure we can see that both dsmax and dlmax are decreasing functions of the available bandwidth r, which means that the more bandwidth is available on the network route, the tighter is the guaranteed delay upper bound to the flow. We can also see from Figure 1 that dsmax is slightly less than dlmax for all values of available bandwidth. This implies that a shorter packet length may give better delay performance with the same amount of bandwidth, although the improvement in delay performance caused by a shorter packet length is not significant in this example. ra (Mb/s) 4

ras ral

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0 10

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dreq (ms)

Figure 2. Bandwidth allocation for delay guarantee We also calculated the required amounts of bandwidth for meeting various delay requirements for the flow f by using (17). The results are plotted in Figure 2. We still considered both the short packet (53 bytes) case and the long

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packet (1500 bytes) case. The required amount of bandwidth for delay guarantee in these two cases are denoted respectively by ras and ral in Figure 2. From this figure we can see that both ras and ral decrease when the delay requirement dreq increases, which means that more bandwidth must be allocated for guaranteeing a tighter delay requirement. We also found from this figure that ras is less than ral for all delay requirements, which implies that shorter packet length helps the network service to achieve the same delay performance with less amount of allocated bandwidth.

7 System Architecture for Grid Network Service Discovery This section discusses a system architecture for implementing the description and discovery technologies for Grid network services. This architecture is shown in Figure 3. The proposed system is embedded in the service-oriented Grid architecture. The major components of this system include the Grid service broker, the Grid service registry, and the network service provider. The procedure for discovering a network service that meets the performance requirement of a Grid application consists of the following steps, which are numbered in Figure 3. Application

2: application request ¯ L, a demand profile P [d, ¯] 4: service selection Grid Service dmax ≤ dreq Broker bmin ≥ breq 5: service confirmation Service Provider

3: service search

1: publish matrix C

7: update ci,j 6: resource allocation

Service Registry

Figure 3. Grid network service discovery Step 1: The network service provider publishes a service description, which includes a capability matrix C in addition to the service function and interface definitions provided by a WSDL file. The matrix C and its elements are defined according to (1) and (2). Step 2: When a Grid application needs to utilize the underlying networking platform, it submits a job request to ¯ L, a the Grid service broker with a demand profile P (d, ¯), where d is the desired destination, L is a load descriptor that characterizes the traffic load, and a ¯ gives all performance

requirements. Step 3: After receiving the application request, the service broker searches the service registry to find a list of available network services; and then retrieves and examines the capability matrix C of each candidate service to filter out all network services with no route between the source ¯ and destination specified by d. Step 4: For each network service that passes the reachability check at Step 3, the service broker predicts the achievable QoS performance for the application. The achievable minimum bandwidth bmin and the maximum delay dmax can be calculated by using (10) and (11) respectively. Then the service broker selects the service whose achievable performance satisfies the application requirement. Step 5: After selecting a network service, the Grid service broker contacts the service provider to confirm that the predicted performance can actually be guaranteed by the network. Service-Level-Agreement (SLA) negotiation and network admission control may be involved in this step. Step 6: If the network service provider accepts the Grid application request in Step 5 and setup a service-levelagreement with the application, then sufficient amount of network resources should be allocated for QoS provisioning. Bandwidth allocation for delay guarantee can be determined by using (14) and (15). Step 7: The network service provider should update the QoS capability description after resource allocation. This step includes recalculating and republishing the matrix C.

8 Conclusions Grid computing is an emerging computing paradigm that will have a significant impact on the next generation information infrastructure. Computer networking systems form the foundation for Grid computing and must be integrated into the Grid architecture as services. High-performance Grid computing must select the appropriate network service that meets the QoS requirement of each Grid application, therefore performance-based description and discovery for network services is the key of integrating networks into the Grid architecture. However, current Grid service description and discovery mechanisms are function-based instead of performance-based; thus are not sufficient to support the network QoS for high-performance Grid computing. This paper proposes a new approach for describing service provisioning capabilities of Grid network services. The key of this new description is a capability matrix that describes both reachability and QoS capability of each route provided by a network service. Based on this new description approach, we developed a performance-based discovery technology for Grid network services. Using the proposed description and discovery technologies, the Grid service broker can select a network service that not only supports the

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required functions but also meets the QoS requirement for each Grid application. The adoption of service curve, arrival curve, and other network calculus techniques in our approaches makes the developed technologies highly flexible and applicable to networking systems that consist of heterogeneous domains with various implementations. The service discovery approach developed in this paper can be realized within the framework of current Grid service discovery standard and may significantly improve networking performance for Grid computing.

References [1] R. J. Al-Ali, O. M. Rana, and D. W. Walker, “G-QoSM: grid service discovery using QoS properties,” J. of Computing and Informatics, vol. 21, no. 4, 2002. [2] R. J. Al-Ali, A. ShaikhAli, O. M. Rana, and D. W. Walker, “Supporting QoS-based discovery in service-oriented grids,” Proc. of the Intl. Parallel and Distributed Processing symposium, 2003. [3] E. Ambrosi, M. Bianchi, C. Gaibisso, G. Gambosi, and F. Lombardi, “A system for predicting the run-time behavior of Web Services,” Proc. of the 2005 International Conference on Services Systems and Services Management, June 2005. [4] J. L. Boudec and P. Thiran, “Network calculus: a theory of deterministic queueing systems for the Internet,” Springer Verlag LNCS 2050, June 2001. [5] B. Y-L. Chan, V. Ng, and S. Chan, “Quality guarantee for WSDL-based services,” Proc. of the 9th Intl. Conf. on Computer Supported Cooperative Work in Design Proceedings, 2005.

[6] P. Fizek and M. Reisslein, “MPEG-4 and H.263 video traces for network performance evaluation,” IEEE Network Magazine, vol. 15, no. 6, pp. 40–54, Nov. 2001. [7] M. Parashar and C. A. Lee, “Specail Issue on Grid Computing,” Proceedings of the IEEE, vol. 92, no. 3, pp. 479–484, March 2005. [8] A. K. Parekh and R. G. Gallager, “A generalized processor sharing approach to flow control in integrated services networks: the single-node case,” IEEE/ACM Trans. Networking, vol. 1, no. 3, pp. 344–357, March 1993. [9] D. Stiliadis and A. Varma, “Latency-rate servers: a general model for analysis of traffic scheduling algorithms,” IEEE/ACM Trans. Networking, vol. 6, no. 5, pp. 611–624, Oct. 1998. [10] T. Yu and K-J. Lin, “The design of QoS broker algorithms for QoS-capable Web Services,” Intl. J. of Web Services Research, vol. 1, no. 4, Oct. 2004. [11] Global Grid Forum OGSA-WG, “The Open Grid Service Architecture, version 1.0,” Jan. 2005. [12] World Wide Web Consortium, “Web Service Definition Language version 1.1,” March 2001. [13] Web Services Policy Framwork, published by IBM, BEA, and Microsoft online at http://www128.ibm.com/developerworks/webservices/ library/specification/ws-polfram/ [14] Global Grid Forum GHPN-RG, “Grid Network Services,” May 2005. [15] Global Grid Forum GHPN-RG, “Networking Issues for Grid Infrastructure,” GFD-I.037, Nov. 2004.

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