Modeling and Performance Analysis of an OGSA-based Resource Sharing Environment in NGN

Transcription

1 Modeling and Performance Analysis of an OGSA-based Resource Sharing Environment in NGN Li Li, Fangchun Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications P.O. Box 187, BUPT, Beijing, P.R.CHINA Abstract: - This paper researches the resource sharing problem in Next Generation Network (NGN). GRSENan OGSA-based Resource Sharing Environment in NGN is proposed which is divided into four layers: resource layer, service layer, virtual service and virtual organization layer (VSVOL) and the user/application layer. GRSEN wraps all the resources in NGN including network capability resources, service resources into grid services. With the help of VSVOL, GRSEN realizes the transparency of the access to grid service in NGN. A multiserver multiqueue (MSMQ) based scheduling system of GRSEN and a priority-based scheduling algorithm are proposed. The proposed scheduling system is modeled by Stochastic Petri Net and simulation experiments are done. The results of experiments verify that the scheduling system and algorithm can achieve the goal that scheduling service requests of GRSEN according to the priority and getting large throughput. Key-Words: - OGSA, Resource sharing, Next Generation Network, Scheduling scheme, Multiserver multiqueue, Stochastic Petri Net 1 Introduction With the convergence of the telecommunication networks and packet-switch data networks, the socalled Next-Generation Networks (NGN) [1] comes into being. How to share these resources in NGN is an important and meaningful work. There are lots of equipments and resources in NGN, including network resource, service resource, etc. With the help of open API such as Parlay/OSA API [2], the network capability in NGN can be opened to the outside third party. But there are still a lot of other kinds of resources need to be open and shared. For instance, there are many Application Servers, SCPs, and on these service nodes, running a lot of valueadded services. All these services and service nodes are important resources to be shared. Grid computing [3] is concerned with coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. With the evolving of Open Grid Services Architecture (OGSA), Web Services emerges as a standard interoperable technology for grid systems. According to [4], the service abstraction may be used to specify access to computational resources, storage resources, and networks, in a unified way. How the actual service is implemented is hidden from the user through the service interface. So, with the help of OGSA, we can solve the resource sharing problem in NGN. All the resources in NGN can be wrapped into the grid service. The service virtualization enables consistent NGN resource access across multiple heterogeneous platforms, and many services with the same function set are classified into one type of virtual service. Virtualization strengthens the ability to map common service semantics seamlessly onto diverse specific service implementations. When the user requests are scheduled, virtualization helps the grid system locate the most suitable actual service and resource. This paper proposes an OGSA-enabled Resource Sharing Environment in NGN (GRSEN). We put forward a multiserver multiqueue (MSMQ) based scheduling system to describe the service request selecting and dispatching on GRSEN, and propose a modeling and analysis method based on Stochastic Petri Net (SPN). We also propose a priority-based Advanced Shortest Expected Delay First scheduling algorithm (P-ASED) which makes scheduling decision according to the priorities of requests and the weighted average of the expected delay and relative queue length of service nodes. The rest of the paper is organized as follows: Section 2 describes the related work. Section 3 presents GRSEN and its scheduling system. Section 4 models the scheduling system of GRSEN using SPN and describes the scheduling policy of P- ASED algorithm. The simulation experiments are

2 discussed in Section 5. Section 6 concludes the paper. 2 Related Work Based on Grid and Web Services, Open Grid Services Architecture (OGSA) is an important progress. A basic premise of OGSA is that everything is represented by a Grid service. It defines standard mechanism for creating, naming and discovering Grid Service instances. OGSA defines the semantics of Grid Service. Its service model emphasizes the service virtualization, service integration ability and message based communication structure [4, 5]. All these have the same view with NGN service characteristics. [6] proposes a Parlay and OGSA based NGN service convergence architecture. [7] proposes a new Parlay/OSA service architecture based on OGSA, named Parlay Grid Services. These researches show that it is an interesting research direction by applying Grid related technologies to telecom. But they are mainly focuses on the service convergence study in NGN. These researches do not solve the resource sharing problem in NGN. 3 GRSEN- an OGSA based Resource Sharing Environment in NGN 3.1 Overview of GRSEN GRSEN is an OGSA-based service grid system. As shown in Fig.1, the whole architecture of GRSEN is divided into four layers: resource layer, service layer, virtual service and virtual organization layer and the user/application layer. Fig. 1. The Architecture of GRSEN Parlay/OSA gateway, Parlay Application Server (Parlay AS), SCP of Intelligent network, SIP Application Server etc. The network capability resource is the abstraction of the network capability in NGN such as the generic call control capability, multi-party call control, etc. The service resources are the value-added services running on the service node, such as Parlay AS, SCP, etc. There are other servers in the Resource Layer contains resources, such as computing resource, data resource, etc. The service layer of GRSEN wrapped the NGN resources in resource layer into grid service, and provides the NGN grid service execution environment. There are two kinds of entities in this layer: Grid Enabling Gateway (GEGW) and Gridenabled Open Service Supporting Environment (GOSSEN). GEGW is gateway converting the value-added services running on existing service nodes (Parlay AS, SCP, etc.) into grid service, so as to protect the service resource in the current network. GOSSEN is a newly proposed system in which the services in NGN are created and hosting directly in the form of grid service. GOSSEN provides the environment for the creation, running and management of the NGN grid service. Users and application access NGN grid services by the virtual service and virtual organization layer (VSVOL) which is implemented upon special portal. In GRSEN, the virtual service is a set of grid service with the same functions. Before real services are published, the corresponding virtual service is published first with the WSDL schema. In additional, the service-scheduling function is also provided at VSVOL. Now a priority-based scheduling algorithm is provided at the portal which will described in the latter parts of this paper. With VSVOL, GRSEN realizes the transparency of the access to grid service. VSVOL helps the users to search the suitable services and create a VO for its request based on policies and agreements. When the portal, which represents the user, negotiates successfully with the corresponding service node, the user request will be scheduled to it. The VO management is provided to create, manage and destroy VO for user request. Service coordination function can compose many services from various service domains for a complex user request. 3.2 Scheduling System of GRSEN In this section, we propose a MSMQ-based scheduling system in GRSEN as Fig. 2 shown. The resource layer contains the sharing resources in NGN. The resources layer entities include

3 4 SPN Modeling Fig. 2. MSMQ-based scheduling system in GRSEN. The MSMQ-based scheduling system in GRSEN is composed of multiple grid service nodes (GOSSENs or GEGWs) and Grid Scheduler. The service nodes locate possibly in different administrative domains, and each node contains a grid service server. In the scheduling system of GRSEN, service requests are classified into multiple categories and assign different priorities. In this way, GRSEN can provide differentiated QoS to different users or service requests. Specifically, the Grid Scheduler receives the incoming requests and classifies them into several categories based on their importance, for example, some requests may have more stringent real-time requirements, or some users may have paid more money for their grid services. Different categories of requests are assigned different priorities and are processed accordingly. The Grid Scheduler located in the portal acts as a first-level scheduler of GRSEN and has control on the scheduling of all incoming service requests. The Grid Scheduler provides a set of request waiting queues, one for each priority level. Service requests of the same priority level join the same waiting queue and are scheduled in a first-come-first-served (FCFS) manner. A threshold is set for each waiting queue to limit the maximum number of waiting tasks. The Grid Scheduler is firstly responsible for selecting a waiting request from the multiple queues according to a request-selecting policy. It can then dispatch requests to any node according to a request-dispatching policy. The Grid Scheduler can consider many factors in the request dispatching, such as the requirements and preferences of the users or tasks, the status of the resources, the current load of the server, the location of the resource server, etc. Each service node has a local scheduler. One of the primary differences between a Grid scheduler and a local resource scheduler is that the former does not own the local resources and does not have control over them. So there are no assumptions to the behavior of the service node scheduler. For the Grid Scheduler, a service node is just like a singlequeue singleserver system. 4.1 System Model In this section, we will model the scheduling system of GRSEN described in Section 3 using SPN [8, 9]. The following assumptions are made in our SPN modeling: 1. There are n service nodes, the i-th service node is denoted by GN i ( 1 i n). Its mean service rate is at µ i ; 2. Each service node has an internal request queue to buffer the request. There are n internal queues, the i-th queue in the i-th service node is denoted by q i. The capacity of q i is nbuffer i ; 3. The service requests are classified into m categories and assigned m priority levels accordingly. Requests of priority j are denoted by t j. Service requests t j are submitted according to the Poisson process with mean rate λ j. These requests can be processed by service node. The Grid Scheduler contains m requests queues, one for each priority level. Requests in the same waiting queue have the same priority and are executed in a FCFS manner; 4. The execution time of service requests in different waiting queues is assumed to be exponentially distributed. Fig. 3 shows the SPN model of the scheduling system of GRSEN. In this model, the rectangles denote timed transitions and the black bars denote immediate transitions. The circles denote places, and they contain tokens denoted by black dots. Tokens in the model can represent either service requests or server resources. Request queues are represented by places, and the numbers of waiting requests in the queues are represented by the marking of those places. c1 p1 d1 q1 s1 r 1 so 1 c2 p2 r2 cm pm so 2 r m f o 1 d q 2 2 s2 so m o n Fig. 3. SPN model of scheduling system in GRSEN. dn qn o 2 sn

4 The meanings of the transitions and places in Fig. 3 are described in the following ( 1 j m, 1 i n): Transitions: c j : models the arrival of requests t j at rate λ j. r j : models selecting the request t j to be dispatched. It can be associated with enabling predicate and firing probabilities. so j : models the requests filtering of the waiting queue of t j when the queue is overload. It can be associated with the enabling predicate and firing probability. d i : models dispatching requests to node GN i. It can be associated with the enabling predicate and firing probability. s i : models executing requests by service node GN i. Its mean service rate is at µ i. o i : models the requests filtering of service node GN i when overload occurs. It can be associated with the enabling predicate and firing probability. Places: p j : models the request waiting queue of requests t j in the Grid Scheduler with capacity sbuffer j. f : models the judgment function of the Grid Scheduler to determine which service node should the request be dispatched to. f makes its decision according to the enabling predicate and firing probability of d i. q i : models the request waiting queue of service node GN i with capacity nbuffer i. 4.2 Scheduling Policy Based on the scheduling system shown in Fig. 2, here we propose a priority-based Advanced Shortest Expected Delay First scheduling algorithm (P- ASED) in GRSEN. The P-ASED algorithm is composed of two steps: the first step is the priority based request selecting; the second step is request dispatching according to the weighted average of the relative queue length and the expected delay Request-selecting Policy In the request selecting step of P-ASED, requests with higher priority will be firstly selected than the requests with lower priority. As for the MSMQbased scheduling system in GRSEN, the requests in the request queue with higher priority will be selected firstly than the requests in the request queue with lower priority. The strategy of the priority-based requestselecting policy can be described in the enabling predicate and firing probability of transition r j and so j in Fig. 3. The enabling predicate of r j is y j : ( M( pj) > 0) ( k,1 k < j, M( pk) = 0). Specially, the enabling predicate of r 1 is simply M( p 1) > 0. The firing probability of r j is 1, if j R1 ( M ) Pr ( j ) = Where 0, else R1( M) = { l ( M( p l ) > 0) ( M( p k ) = 0), k,1 k < l m} Specially, the firing probability of r 1 is 1, if M ( p1 ) > 0 Pr ( 1) = 0, if M ( p1 ) = 0 The enabling predicate of so j is y j : M(p j ) sbuffer j. The firing probability of so j is 0, if Μ( p j ) < sbufferj Pso ( j ) = 1, else Request-dispatching Policy In the request dispatching step, the selected request in the request selecting step will be dispatched to the service node according to the dispatching policy. In order to promote the throughput of GRSEN, we propose a new request dispatching algorithm Advanced Shortest Expected Delay First algorithm (ASED) which is used in the request dispatching step of P-ASED. ASED makes its dispatching decision according to the Node Selection Factor (NSF) which is a weighted average of the Relative Queue Length (RQL) and the Expected Delay (ED). Based on the SPN model in Fig. 3, we give the following definitions. Definition 1. The relative queue length (RQL) of a service node is the quotient of the exact length of the waiting request queue and the capacity of the queue. The RQL of the i-th service node is M ( qi ) RQL() i =. nbufferi Definition 2. The expected delay (ED) of a service node is the exact length of the waiting request queue and the service time of one request. The ED of the i-th service node is M ( qi ) ED() i =. µ i Definition3. The Node Selection Factor (NSF) of a service node is the weighted average of the RQL and ED. Given the weight of RQL and ED is w 1 and w 2 separately, and w 1 + w 2 = 1, the NSF of the i-th service node is NSF() i = w1 * RQL() i + w2 * ED() i. The strategy of the ASED request-dispatching policy can be described in the enabling predicate and firing probability of transition d i and o i in Fig.

5 3. The enabling predicate of d i is: y :( M ( q ) < nbuffer ) ( k i, NSF( i) NSF( k)) i i i ( k i, M ( qk) = nbufferk) The firing probability of d i is 1, if i ASED( M ); pd ( ) = ASED( M ) Where i 0, else. ASED( M ) = { k NSF( k) = min( NSF(1), NSF(2),..., NSF( n)) and( M ( q ) < nbuffer )} k Definition 4. overload i nbuffer i is the upper limit of the service node GN i is not overloaded. The enabling predicate of o i is y i : M(q i ) overload i The firing probability of o i is 0, if Μ( qi) < overloadi Po ( i ) =. 1, else 5 Simulation Experiment We use the software package SPNP [10] to analyze the SPN model in Fig. 3 and the proposed P-ASED algorithm. Two experiments are designed. Experiment 1 is designed to verify the proposed model and algorithm can schedule the service requests based on the priority. Experiment 2 is designed to verify the proposed P-ASED algorithm can promote the throughput of GRSEN. We only consider a GRSEN consisting of three service nodes and the requests are assigned two priority levels. Specifically, class 1 requests are attached with a high priority and class 2 requests are attached with a low priority. The arrival rates of class1 and class 2 requests monotonically increase from 5.0 to 27.5 reqs/s, and λ 1 = λ 2. This results in a maximum combined requests rate λ = λ1+ λ2 =55 reqs/s. The selecting rate of Grid scheduler and the request filtering rate when overloaded are large enough compared to the request arrival rate. We also assume the three service nodes are heterogeneous. The parameters of the experiments are listed in Table 1. The unit of place in SPN is reqs; the unit of transition is reqs/s. Table 1. Experiments parameters table Parameter sbuffer 1 sbuffer 2 nbuffer 1 w 1 Value Parameter nbuffer 2 nbuffer 3 µ 1 µ 2 µ 3 w 2 Value k Fig. 4 plots the throughput of service requests in reqs/s as a function of the total request arrival rate. TH1 is the throughput of the class 1 requests with high priority. TH2 is the throughput of the class 2 requests with low priority. TH is the throughput of the entire system including class 1 and class 2 requests. Throughput(reqs/s) TH TH1 TH Request arrival rate(reqs/s) Fig. 4. Throughput of the P-ASED algorithm As shown in Fig. 4, the performance difference in throughput is conspicuous between the high-priority and low-priority requests. At low request arrival rates (the total arrival rate is less than 30 reqs/s), the throughput TH1, TH2 and TH are all equal to the arrival rates. As the arrival rates increases, the servers capacity is almost exhausted at around 35 reqs/s. At this point the low-priority requests start to be rejected while the high-priority requests do not experience any rejections until the very end of the experiment at 55 reqs/s. Fig. 4 shows the throughput TH2 reaches a maximum at 16 reqs/s and then continue to drop despite the increasing request rate, whereas the throughput TH1 is very well protected all the time as it appears as a straight line and always equals to the request arrival rate. From experiment 1, we can see that the proposed scheduling system and P-ASED algorithm can do schedule the service request according to the priorities. 5.2 Numerical results of Experiment 2 In order to verify the throughput promotion of P- ASED, we select the other three algorithms to make the comparison: P-SEDR, P-SQR and P-RR. The request-selecting policies of these four algorithms are all priority-based policy. And the requestdispatching policies are ASED, SEDR, SQR and RR separately [11]. Fig. 5 plots the throughput comparison of service requests in reqs/s as a function of the total request arrival rate. 5.1 Numerical results of Experiment 1

6 Throughput(reqs/s) P-ASED P-SEDR P-SQR P-RR Request arrival rate(reqs/s) Fig. 5. Throughput comparison As shown in Fig. 5, the throughput of P-ASED is always larger than any other three algorithms. At low request rates, the difference between P-ASED and P-SEDR is not much. As the requests arrival rates increases, the throughput of P-ASED gets larger and larger than that of P-SEDR. From experiment 2, we can see that the proposed P-ASED algorithm can do promote the throughput of GRSEN. 6 Conclusion In this paper, we have proposed an OGSA-enabled resource sharing environment in Next Generation Network-GRSEN. GRSEN is divided into four layers: Resource Layer, Service Layer, Virtual Service and Virtual Organization Layer and User/Application Layer. Resources in the Resource Layer are wrapped into grid services, and services can be deployed on some resources. VSVOL helps users to search the suitable services and create a VO for its request based on policies and agreements. We also propose a MSMQ based scheduling system to describe the service request selecting and dispatching of GRSEN, and propose a modeling and analysis method based on SPN. Due to the cost and resources consumed are different for difference Grid Service requests, the multiple priority queues is designed in the grid scheduler of the proposed scheduling system. We also propose a priority-based Advanced Shortest Expected Delay First scheduling algorithm (P-ASED) which is consists of two steps: the first step is request-selecting according to the priority of requests; and the second step is requestdispatching according to the weighted average of the expected delay and relative queue length of the service node. Based on the proposed SPN model, the simulation experiments verify that the proposed MSMQ-based scheduling system and the P-ASED algorithm can schedule service request according to request priority and promote the throughput of GRSEN. References: [1] A. R. Seshadri and S. Mohan, Control and management in next generation networks: challenges and opportunities, IEEE Communications Magazine, Vol.38, No.10, 2000, pp [2] A.-J.Moerdijk and L. Klostermann, Opening the networks with Parlay/OSA: standards and aspects behind the APIs, IEEE Network, Vol. 17, No.3, 2003, pp [3] I. Foster, C. Kesselman, and S. Tuecke, The Anatomy of the Grid, International Journal of Supercomputer Applications, Vol. 15, No.3, 2001, pp [4] I. Foster, C. Kesselman, J. M. Nick, and S. Tuecke, The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration, Downloadable at: pdf, [5] I Foster, C. Kesselman, J. M. Nick, and S. Tuecke, Grid services for distributed system integration, IEEE Computer, Vol. 35, No.6,2002, pp [6] Zhen Liu, Jing Yang and Guo-Qing Zhang, Towards a Parlay-Grid Communication Model for NGN Service Convergence. Proceedings of IEEE GLOBECOM 2005, Nov pp [7] Shao-yang RAO and Fang-chun YANG, Parlay Grid Services: new generation service architecture based on OGSA. Journal of Beijing University of Posts and Telecommunications, Vol. 28, No.4, 2005, pp [8] T. Murata, Petri Nets: Properties, Analysis and Applications, Proceedings of the IEEE, 1989, Vol. 77, No. 4, pp [9] G. Ciardo, and K. S. Trivedi, A Decomposition Approach for Stochastic Reward Net Models, Performance Evaluation, Vol. 18, No.1, 1993, pp [10] G. Ciardo, J. Muppala, K.S. Trivedi, SPNP: stochastic Petri net package, Proceedings of the Petri nets and performance models, pp [11] Chuang Lin, Shiqiang Yang, Performance Analysis of Scheduling Schemes in Multiserver Muiltqueue Systems, ACTA ELECTRONICA SINICA, Vol. 28, No.5, 2000, pp