Network Resource Scheduling and Management of Optical Grids

(1)

TANWIR, SAVERA. Network Resource Scheduling and Managemenet of Optical Grids. (Under the direction of Professor Harry G. Perros).

(2)

by

Savera Tanwir

A thesis submitted to the Graduate Faculty of North Carolina State University

in partial fulfillment of the requirements for the Degree of

Master of Science

Computer Science

Raleigh, NC 2007

Approved By:

Dr. Michael Devetsikiotis

Dr. Harry G. Perros Dr. Rudra Dutta

(3)

Dedication

(4)

Biography

(5)

Acknowledgments

First of all, I would like to thank my parents Dr. Tanwir Ahmed and Zamored Tanwir for their unconditional love and support through out my life. I would have never been able to accomplish this work without their unwavering faith in my abilities. I want to thank my husband Ahmed Mujtaba Hamdani for encouraging me all these years and for always being there when I needed him. He has always given me strength and hope in the worst of times.

I would like to especially thank Dr. Harry Perros for his guidance and encourage-ment throughout the two years of masters degree. He has always been very supportive and motivating. I am grateful to him for his valuable advice not only on the thesis but also on several other matters of life. I acknowledge Dr. Rudra Dutta and Dr. Michael Devetsikiotis for agreeing to be on my thesis committee and for their valuable advise and suggestions.

I would like to thank Dr. Lina Battestilli for sparing her time and helping me so much with the thesis. She has been an inspiration to me and kept me going all this time. I want to thank Gigi-Karmous Edwards for trusting in me and for her enormous help and financial support. I would also like to thank Dr. Yufeng Xin for his valuable discussions and guidance.

I want to thank my room mate Ani for bearing me and listening to me all the time. I also want to thank my brothers Mansoor and Sarmad for always being very supportive.

I am grateful to my advisor Dr. Arshad Ali from NUST for introducing me to research and encouraging me always. I would not be here without his help and support.

(6)

List of Figures

1.1 EnLIGHTened Computing Testbed . . . 3

1.2 EnLIGHTened Middleware Architecture . . . 4

3.1 Scheduling Window . . . 12

3.2 Example Topology . . . 12

3.3 Reservation table at t0 . . . 13

3.4 Reservation table at t2 . . . 15

3.5 Wavelength Assignment using Min-Gap . . . 19

3.6 Network Topologies . . . 22

3.7 Discrete Probability Distribution for the Intermediate Time between the Re-quest Arrival Time and Reservation Time . . . 23

3.8 Flexibility in Scheduling Window . . . 24

3.9 Arrival Rate vs Blocking Probability . . . 25

3.10 NSF-NET Link Utilization : SWF vs LB-SWF . . . 26

3.11 10 Node Fully-Connected Network Link Utilization : SWF vs LB-SWF . . . 27

3.12 k vs Blocking Probability . . . 28

3.13 k vs Blocking Probability . . . 29

3.14 dmax vs Blocking Probability . . . 30

3.15 Reservation Delay vsdmax . . . 31

3.16 Non-Blocking Scheduler . . . 33

3.17 Wavelength Assignment . . . 34

3.18 Requests for long and short durations simultaneously . . . 35

3.19 IPP Arrival Process . . . 36

3.20 Failure Recovery . . . 38

3.21 Periodic Reconfiguration . . . 40

4.1 Monitoring Architecture . . . 50

4.2 MonALISA client GUI : current monitoring information from the EnLIGHT-ened testbed can be viewed . . . 51

(9)

List of Tables

(10)

Chapter 1

Introduction

This chapter introduces the concepts and theory related to network resource scheduling and management of Optical Grids. Section 1.1 introduces the background con-cepts related to resource management in Optical Grids, Section 1.3 gives the motivation for this line of research and Section 1.4 summarizes our contributions to the field.

1.1 Background

The Grid is widely regarded as the next stage for the Internet after the World Wide Web. According to IBM [32], ”Grid computing is an approach to distributed computing that spans not only locations but also organizations, machine architectures and software boundaries to provide unlimited power, collaboration and information access to everyone connected to a grid”. The grid allows resource discovery, resource sharing and collaboration in distributed environment, thus it can provide people from different locations and countries to work together to solve a specific problem such as a design collaboration.

(11)

high speed transfer of high definition video streams with multicast capabilities. We have also recently seen great progress in the optical networks technology in terms of transmission capacity and dynamic reconfigurability. With Wavelength Division Multiplexing (WDM), a single wavelength can now reach OC-192 (10 Gbps) and hundreds of wavelengths can be supported on each fiber. So the Optical Grids have emerged to interconnect massive compute clusters and large storage resources through multi-gigabit lightpath connections. Although much progress has been made towards developing Grid technologies, the area that is still underdeveloped is the link between Grid applications and underlying network technologies which make Grids truly effective. The abstraction and encapsulation of these optical network resources into manageable and dynamically provisioned Grid entities is nec-essary in order to meet the complex demand patterns of Grid applications and to optimize the overall network utilization.

Various projects around the world consider the problem of building reconfigurable, dynamic, adaptable Optical Grids [8, 9, 12, 10]. These projects consider the network re-sources as key Grid rere-sources that can be managed and controlled like any other Grid resource. They have developed Grid middleware that is responsible for the resource man-aging, coordinating, allocating and scheduling advance resource reservations for all Grid resources: network, cluster, storage, scientific instruments, etc. The middleware establishes the optical connections with the necessary bandwidth and it interfaces with the cluster schedulers (e.g. Moab, OpenPBS/Torgue,etc) to make advance reservations. From a Grid application perspective, all types of resources may be required to meet its computational needs.

The EnLIGHTened Computing project [8] is one such effort towards the adaptive and optimized use of network as a virtual coordinated resource along with the other Grid resources. The EnLIGHTened Computing Project has designed an architectural framework that allows Grid applications to dynamically request in advance or on demand any type of Grid resource - not only high-performance computers, storage, scientific instruments but also deterministic, high-bandwidth, network paths, including lightpaths. The proposed framework discovers and monitors the performance and reliability of the Grid resources for adaptive resource meta-scheduling and coordination. The EnLIGHTened testbed is shown is figure 1.1.

(12)

Figure 1.1: EnLIGHTened Computing Testbed

(13)

Figure 1.2: EnLIGHTened Middleware Architecture

1.2 Motivation

The DNRM is the middleware component that controls the optical network. It is responsible for path computation and wavelength assignment of the end-to-end lightpaths. In a shared-lambda environment, the enforcement of policies is crucial for successful oper-ation. Thus, each DNRM has a policy engine that gives Grid administrators control over how networking resources are shared.

The current DNRM maintains a reservation table of fixed paths. When a request arrives, it checks if the fixed path between the required source and destination is free or not. The DNRM then reserves the resources by interfacing with the specific control plane, i.e., each network domain may have a dynamic control mechanism such as DRAGON’s GMPLS [2], CHEETAH [1], UCLP [13], etc. Currently, the DNRM does not support dynamic path computation based on the most recent network topology.

(14)

• Network Topology Discovery: Determine the latest state of the network resources by

interacting with the control plane e.g. OSPF-TE and management plane e.g. SNMP, TL-1 etc.

• Dynamic Path Computation and Wavelength Assignment: Dynamically compute the

paths using the up-to-date network topology. Assign wavelength using algorithms that improve network utilization.

• Failure Recovery: Employ network monitoring tools to get latest information about the

network resources. Implement mechanisms for restoration and recovery of lightpaths in case of failures.

• Network Re-Optimization: Implement offline re-optimization mechanisms to improve

network utilization.

1.3 Contribution

In this thesis, we have evaluated and compared various algorithms for advance lightpath scheduling that can be implemented in a DNRM. The main aim is to find the best scheduling policy for a Grid network resource manager that improves network utilization and minimizes blocking.

We have also analyzed several approaches for failure recovery and resource op-timization. In order to provide fast restoration, there is a need for a robust monitoring architecture. We also need up to date information about the resources to support the resource discovery process. We have identified the appropriate discovery and monitoring parameters which are needed to determine the availability and performance of the network as well as the compute resources in a Grid. We have surveyed a variety of monitoring tools and techniques and have proposed a monitoring framework to collect these parameters for the EnLIGHTENed middleware.

1.4 Thesis Layout

(15)

(16)

Chapter 2

Related Work

To place this thesis in perspective, we sketch a broad outline of the field of network scheduling by listing key features of other research efforts in this area. We first discuss advance reservations in networks followed by the routing and wavelength assignment of lightpaths. After that we discuss the existing work in the area of failure recovery of optical networks with advance reservations. We also briefly describe some network optimization techniques.

2.1 Advance Reservation of Lightpaths

(17)

reservations of network resources.

The concept of advance reservations in an optical network has attracted some attention in recent years. In [18], the author discussed the properties of advance reser-vation and proposed an architecture for a bandwidth broker to improve the performance of a network based on the additional knowledge of these future reservations. The Globus Architecture for Reservation and Allocation (GARA), presented in [24], supports advance reservations for various types of resources for the Grid. In [23], the authors also discussed advance reservation of heterogeneous network paths in the context of Grid computing and proposed a network resource hierarchy to integrate the path management with Grid infor-mation and authentication services.

In comparison to the immediate reservations, advance reservations generally de-grade the resource utilization and the acceptance rate due to resource fragmentation [19]. This can be improved by introducing some flexibility in defining the advance reservations. In [41] the authors proposed a sliding scheduled traffic model and a demand time conflict resolution algorithm to maximize resource usage in a network. In [26] the authors pro-posed a Flexible Advance Reservation Model (FARM) and described how to implement this model in the meta-scheduling problem. The results indicate that using this approach the acceptance rate and resource utilization can be improved dramatically.

2.2 Routing and Wavelength Assignment of Advance

Reser-vation Requests

The Routing and Wavelength Assignment (RWA) [45, 44] problem deals with find-ing a route on the network topology and assignfind-ing a wavelength on each link of the selected route for every lightpath established over the network. Typically a connection request can be of three types [45] static, incremental, and dynamic.

(18)

NP-complete [21]. To make the problem more tractable, the SLE problem can be partitioned into two subproblems, namely routing and wavelength assignment, and each subproblem can be solved separately. A review of these approaches is given in [45].

In case ofincremental traffic, connection requests arrive sequentially and the light-path established for each connection remains in the network indefinitely. Lastly fordynamic traffic, a lightpath is set up for each connection request as it arrives, and the lightpath is released after some finite amount of time. The objective in the incremental and dynamic traffic cases is to set up lightpaths and assign wavelengths in a manner that minimizes the blocking probability of a connection, or equivalently maximizes the number of connections that are established in the network at any time. This problem is referred to as the Dynamic Lightpath Establishment (DLE) problem. It is more complex to solve and usually heuristics are used to solve the routing and wavelength assignment subproblems separately [44].

There is another characterization of a traffic model where the setup and tear-down times of the demands areknown in advance. This is known as the Scheduled Traffic Model [29]. There can be afixed window or a flexible window with this model. The start and end time of the connection cannot be altered in the fixed window model, but with the flexible approach the start and end times can slide within a larger window. Integer Linear Program formulations and algorithms have been proposed to solve these problems in [28, 29]. In [46] the design of effective RWA algorithms for different types of advance reservations is discussed and algorithms are presented for requests having specific start time and specific duration (STSD), specific start time and unspecified duration (STUD) and Unspecified Start Time and Specified Duration (UTSD).

2.3 Failure Recovery

Accepted advance reservations can be affected by network failures. In view of this, a strategy is required in order to restore the existing network connections and re-schedule future reservations.

(19)

using different methods. From [20] it is clear that substituting the exact downtime by vague estimations is dangerous, since this leads to a significantly worse performance. Also in [20] two techniques were presented that are independent of the actual downtime. These are load-based and feedback based. In the case of load-based approach the current load and the booking profile during the next time slot should be less than a threshold value η

which depends on the network topology. While in the feedback based method, the initial re-routing interval is one slot and it is increased based on the percentage of successfully re-routed reservations during the last slot. The interval duration is increased until the percentage of successfully re-routed reservations is sufficiently high. The results show that the feedback approach gives better results than the load-based approach while avoiding the need to adjust the threshold parameterη.

2.4 Network Re-optimization

The network re-optimization can be used to increase the network utilization. The lightpath re-optimization techniques have been discussed in several papers. Most of the authors have proposed solutions for static traffic demand and heuristics for long-term on-demand traffic flows [17, 16]. In [43] the authors have proposed an optimization scheme for advance reservations by re-configuring them without changing their reservation times. Using this scheme, if a request,x, is blocked, all the existing connections that time-overlap with this request are unscheduled and then re-scheduled after sorting them according to their start times. This can result in provisioning of all the requests including the request

(20)

Chapter 3

Advance Reservation Scheduling

Strategies

In this chapter the various advance scheduling algorithms considered in this thesis are described. In Section 3.1 we describe the advance scheduling problem. In Section 3.2 we present the scheduler functions and algorithms. In Section 3.3, some wavelength assignment techniques are discussed. Section 3.4 and 3.5 describe the failure recovery and resource optimization. Section 3.6 gives the simulation model and the numerical results.

3.1 Problem Description

Consider a Network Topology Graph G = (N,L,W) where N is the set of nodes, L is the set of links and W is the set of wavelengths supported by each link. A user submits an advance reservation request for a lightpath between any two nodes on G to the DNRM. Each request R is defined by the following parameters:

R = [source node, destination node, s,e,d, bandwidth]

(21)

would like to make a resource reservation. The scheduling window must be bigger than the reservation durationd. Thus the scheduler must check if a path is available during interval [s+t, s+t+d] where t= 0,1,2, ...., e−s−d.

Figure 3.1: Scheduling Window

This is an online scheduling problem because the requests arrive dynamically and for each request R, the DNRM must compute a path and then check if a wavelength on each link of this path can be reserved for duration d within the scheduling window [s, e]. The DNRM allocates a wavelength on each link along a path from the source to the destination nodes. If a wavelength along the path for the specified period of time is not available, another path has to be determined. In order to do this, the DNRM maintains a schedule of the reservations called theReservation Table. It contains all current and future reservations and it is used to search for available resources for new advance reservations.

Figure 3.2: Example Topology

(22)

Figure 3.3: Reservation table at t0

R1 = [n1,n7,t1,t8,4,1] and R2 = [n1,n8,t2,t9,4,1]. We assume that each request requires a bandwidth equal to a wavelength. As there are no other reservations at this time, the links are reserved starting at the beginning of the scheduling window of each request. A pictorial representation is given in figure 3.3

Table 3.1: Reservation Table at t0

Node1 Node2 Wavelength Start Slot End Slot Req ID

n1 n3 λ1 t1 t4 R1

n1 n3 λ2 t2 t5 R2

n3 n6 λ1 t1 t4 R1

n3 n6 λ2 t2 t5 R2

n6 n7 λ1 t1 t4 R1

n6 n8 λ1 t2 t5 R2

Let us assume that a third request arrives at time t1 for a path between n1 and n8 with R3 = [n1,n8,t3,t8,3,1]. Since all the wavelengths along links n1→n3 and n3→n6 are busy till time t4, the shortest path n1→n3→n6→n8 is not available for slots t3 and t4. But due to the large scheduling window, the request can be still accepted for slots t5, t6 and t7 for the same path.

(23)

n1→n3→n6→n7 is not available for all slots in the scheduling window. So in this case, an-other path has to be determined. This new path can be a 4-link path i.e. n1→n2→n5→n6→n7 and the wavelengths that are reserved are λ1, λ1, λ1 and λ2. The state of the reservation table at time t2 is given in Table 2. A pictorial representation is given in figure 3.4.

Table 3.2: Reservation Table at t2

Node1 Node2 Wavelength Start Slot End Slot Req ID

n1 n2 λ1 t3 t4 R4

n1 n3 λ1 t1 t4 R1

n1 n3 λ2 t2 t5 R2

n1 n3 λ1 t5 t7 R3

n2 n5 λ1 t3 t4 R4

n3 n6 λ1 t1 t4 R1

n3 n6 λ2 t2 t5 R2

n3 n6 λ1 t5 t7 R3

n5 n6 λ1 t3 t4 R4

n6 n7 λ1 t1 t4 R1

n6 n7 λ2 t3 t4 R4

n6 n8 λ1 t2 t5 R2

n6 n8 λ2 t5 t7 R3

The objective in this thesis is to determine a scheduling policy to route each incoming lightpath connection request dynamically while minimizing the probability that a connection request will be refused due to lack of available lightpaths and maximizing the overall network throughput.

3.2 Scheduling Algorithms

(24)

Figure 3.4: Reservation table at t2

We consider two categories of advance scheduling. In the first, a call is blocked if it cannot be scheduled within its requested flexible window. In the second, calls are not blocked, but rather delayed and scheduled at the first available time instance which maybe outside the requested window.

As will be seen below, we always determine the shortest path from a source to a destination using Dijkstra’s algorithm. Different costs associated with the links can be used. The most common link cost is the propagation delay. However, in order to balance the load in the network one can use a link cost that is determined based on the current and future usage. In this case we start with all the links having a cost of 1. As lightpaths are reserved, the link cost is incremented by the number of slots reserved on that link. The cost is decreased after the request is serviced. Hence the weights only reflect the current and future utilization of the link.

(25)

• Switch Path First(SPF): In this scheme we start from the beginning of the scheduling window and check k different paths before sliding the window to the next time slot.

• Slide Window First(SWF): In this scheme we check one path at a time for all of the scheduling window slots. If a path cannot be reserved during the scheduling window, the next shortest path is checked.

We also propose a variant of these strategies by allowing the weights of the links to vary based on current and future utilization of the link. So the weight of a link increases as it gets more and more advance reservations. Opposite, if the link does not have too many advance reservations then its cost will decrease. This provides load-balancing in the network. We call these variant scheduling strategies:

• Load Balancing - Switch Path First(LB-SPF): In this scheme the weights of the links are based on current and future utilization of the link, but the search algorithm is the same as SPF.

• Load Balancing - Slide Window First(LB-SWF): In this scheme the weights of the links are based on current and future utilization of the link, but the search algorithm is the same as SWF.

All the four strategies can be used with both the wavelength assignment schemes First-Fit andMin-Gapdescribed below in section 3.3. We now proceed to describe in detail the two algorithms SPF and SWF.

3.2.1 Switch Path First (SPF) Algorithm

(26)

Algorithm 1 The SPF algorithm

1: function FindPath(Request r, topology t)

2: i= 1

3: start time = s

4: end time =s+d

5: while (end time≤ e) do

6: while(i≤ k) do

7: find shortest path with Dijkstra’s algorithm with propagation delay link cost 8: if a path is foundthen

9: if wavelengths are available on all links during start and end time then

10: assign wavelengths, update all tables

11: return

12: else

13: delete busy link from the topology

14: i++

15: end if

16: end if

17: end while

18: start time = start time +t0

19: end time = start time +d

20: end while

21: end function

3.2.2 Slide Window First (SWF) Algorithm

In this algorithm, we try to find a free period d starting at s+t, where t = 0,1,2, ...e−s−d. If the first shortest path is not free for the required duration during the window, the busiest link defined as the one that uses the maximum number of slots during the scheduling window, is removed from the network topology and the procedure is repeated until either an available path is found or a maximum of k paths is considered.

3.3 Wavelength Assignment

It is possible that multiple wavelengths on a particular link along the source-destination path, are available for the advance reservation. In this case, the scheduling algorithm must determine which wavelength to allocate from the pool of available wave-lengths. We use the following two wavelength allocation schemes:

(27)

Algorithm 2 The SWF algorithm

1: function FindPath(Request r, topology t)

2: i= 1

3: while (i≤ k)do

4: start time =s 5: end time =s+d

6: find shortest path with Dijkstra’s algorithm with propagation delay link cost 7: if A path is foundthen

8: while (end time ≤e) do

9: if wavelengths are available on all links during start and end time then

10: assign wavelengths, update all tables

11: return

12: else

13: start time = start time +t0

14: end time = start time +d

15: end if

16: end while

17: else

18: remove the busiest link during the window from topology 19: end if

20: i++ 21: end while

22: end function

numbered. When searching for available wavelengths, we always start from wavelength 1 and proceed sequentially to the last wavelength until a free wavelength is found. By searching the wavelengths in this manner, connections are packed into a smaller number of wavelengths thus freeing other wavelengths for reservations. First-fit does not require global knowledge of the network and no storage is needed to keep the network state. This scheme performs well in terms of blocking probability and fairness, and is preferred in practice because of its small computational overhead and low complexity [45].

Min-Gap : This scheme aims at reducing the fragmentation in wavelength us-age. With advance reservations we have the information of the wavelength usage with the associated times, and hence we can use this information when assigning the wavelength in order to reduce the gaps between reservations. The main drawback of this scheme is the additional computational overhead when searching for the least gap throughout the existing reservations.

(28)

Figure 3.5: Wavelength Assignment using Min-Gap

• Min-Leading-Gap: This wavelength assignment scheme minimizes the unused leading gaps on a wavelength.

• Min-Trailing-Gap: This scheme minimizes the unused trailing gaps on a wavelength.

• Best-Fit: This scheme minimizes the sum of the leading and trailing gaps.

An example of these wavelength allocation schemes is shown in figure 3.5. We can see that the new lightpath (LP) reservation can be accommodated by all the four wavelengths. First-Fit will select λ1. λ2 minimizes the leading gap between the new and previous reservations therefore it will be selected by Min-Leading-Gap scheme, while λ3 minimizes the trailing gaps so it will be selected by Min-Trailing-Gap. The Best-Fit will selectλ4, as it minimizes the sum of leading and trailing gaps. A comparison of these three schemes is presented in section 3.6.

3.4 Network Failures

Accepted advance reservations can be affected by network failures. In view of this, a strategy is required in order to restore the existing network connections and re-schedule future reservations.

(29)

a concern because most nodes have built in redundancy. Because of the high data rate on these networks, it is necessary to develop appropriate path protection and restoration schemes to prevent or reduce data loss. In case of protection, a path or a link is protected against failures by preassigning resources for a backup path, while in restoration schemes an alternate route is discovered dynamically for each failed connection after the failure occurs. Protection schemes have faster recovery time and provide guaranteed recovery but they require more network resources. Therefore due to lower costs involved, we use the restoration mechanism. Given a failure, the question of interest is how far into the future should we reschedule the existing reservations.

In order to deal with network failures the DNRM must be aware of the current network state. This information can be obtained from the network devices in the form of a link state database using SNMP or other management protocols. Routing protocols, such as OSPF react slowly to network failures. In view of this, monitoring tools that talk directly to the network devices to get the latest state of interfaces and ports should be used as well. As soon as a link failure is observed, all the in-service connections using this link have to be re-scheduled. Also, it is useful to reroute the requests that are scheduled but not yet in service that use the failed link. These are likely to be affected if the failed link is not restored on time. The straight forward approach is to re-schedule all the future connections that use the failed link. However this may not be good solution because if the link is repaired early, the connections will be using sub-optimal paths. Thus, a re-routing interval has to be determined to re-schedule future reservations so that to minimize the number of dropped reservations. We note that the reservations can be dropped when re-scheduling them, if a free path cannot be found for the start time that a reservation has already been made.

(30)

3.5 Network Re-Optimization

A re-optimization of the network can be performed by re-scheduling the lightpath requests. This can be done in two ways: either by changing paths only or by changing both the path and the start time. The latter is not very desirable as it involves re-negotiating the time with the users, and in view of this we will not be considering it in this study.

In [43] the authors have proposed a re-optimization scheme for advance reserva-tions by re-configuring them without changing their reservation times. Using this scheme, if a request is blocked, all the existing connections that time-overlap with this request are un-scheduled and then re-un-scheduled after sorting them according to their start times. This may result in provisioning of all the requests including the one which was blocked earlier. If this does not work, all connections are restored to their original state, i.e., no re-configuration takes place.

We use the same technique but instead of doing the re-configuration after every blocked request, we do it periodically by re-scheduling all the requests for a specific number of slots in future.

3.6 Performance Evaluation

3.6.1 The Simulation Model

We have conducted our simulation experiments on a 14 node NSF-NET topol-ogy with 42 directional links and a 33 node GEANT network topoltopol-ogy with 94 uni-directional links as shown in figure 3.6. We have also considered a 10 node fully connected network topology. We assume 10 wavelengths on each link and full wavelength conversion at each node. The time is slotted with the duration of each slot being 30 minutes.

(31)

(a) NSF-NET

(b) GEANT

(c) 10-node Fully Connected

(32)

nodes for the requested connection are selected randomly using a uniform distribution.

Figure 3.7: Discrete Probability Distribution for the Intermediate Time between the Re-quest Arrival Time and Reservation Time

We assume that the scheduling window is twice the reservation duration, i.e., (e−s) = 2d, based on the results in [26] where the authors have shown that using just 1 or 2 units of flexibility improves the performance significantly. Our results agree with this observation. Figure 3.8 gives the blocking probability for different window sizes. The solid curve marked as 1d corresponds to the case e−s=d. The dotted line curves correspond to the cases when e−s= 2dand e−s= 3d. We note that the scheduling window with a width of twice the duration has much lower blocking probability than without any flexibility. Also increasing it further does not improve the performance to a large extent but the delay between the start time of the actual reservation and the start of the window increases. We ran the simulations under different network loads, where network load is determined by the request arrival rate and the reservation durations. The parameter of interest is the blocking probability Bp.

(33)

Figure 3.8: Flexibility in Scheduling Window

distributed with a mean of 48 slots. We assume that when a link fails all wavelengths on that link fail.

The simulations were run for long time durations with large number of arrivals such that a sufficiently small confidence interval within 1% of the mean with 95% confidence is reached. The confidence intervals are not given in the graphs as they are not discernible. Finally, the simulation model was written in Java.

3.6.2 Numerical Results

Scheduling Algorithms

In this section we will present the simulation results and comparison of our schedul-ing algorithms for advance reservation of lightpaths.

(34)

(a) NSF-NET

(b) GEANT

(c) Fully-Connected

(35)

not improve the blocking probability.

The link utilization comparison between SWF and LB-SWF for NSF-NET is shown in figure 3.10. These results are for an arrival rate of 60 requests/slot. The slope of the curves show how the load is balanced among the links with and without load balancing. The links are sorted in the order of utilization. We also note that for most of the links, LB-SWF achieves lower utilization than SWF. This is because the load is balanced among the links.

Figure 3.10: NSF-NET Link Utilization : SWF vs LB-SWF

The link utilization comparison between SWF and LB-SWF for 10-node fully-connected network is shown in figure 3.11. These results are for an arrival rate of 75 requests/slot. We can see that load-balancing does not improve the network utilization in this case.

(36)

Figure 3.11: 10 Node Fully-Connected Network Link Utilization : SWF vs LB-SWF

value of k for each scheme for the rest of the experiments e.g. it is 3 for SPF and 4 for SWF for NSF-NET and for GEANT it is 4 for SPF and 5 for SWF. But for the fully connected network it is 1 for all schemes because all nodes have direct shortest path to all the other node and having longer paths increases the overall blocking probability.

We have also tested the sensitivity of optimum value of k to the link costs. We observed that optimum value of k depends on the topology and not on the link costs. In figure 3.13a and b, blocking probability is plotted versus different values of k for NSF-NET with reversed link costs and NSF-NET with cost of all links being 1. We can still see that optimal value of k for SWF is 4 and SPF is 3.

The graphs in figures 3.14a and 3.14b show the effect of the connection duration

d on the blocking probability. The duration is uniformly distributed with a minimum of one slot and a maximum dmax. We can see that the blocking probability increases as dmax

increases. SWF with load balancing still performs the best. We also observe that the blocking probability significantly increases fordmax>5 for the considered topologies. Here

again we see that for the fully-connected network all schemes give the same results. Figures 3.15a and 3.15b show the reservation delay, i.e., the time elapsed from the requested start time sto the time s+t where the reservation was actually made, as a function ofdmax for both SPF and SWF. We see that SPF always tries to schedule as close

(37)

(a) NSF-NET

(b) GEANT

(c) Fully-Connected

(38)

(a) NSF-NET Link Cost = 1

(b) NSF-NET Reversed Link Costs

(39)

(a) NSF-NET

(b) GEANT

(c) Fully-Connected

(40)

(a) NSF-NET

(b) GEANT

(c) Fully-Connected

(41)

We have also implemented a non-blocking version of the scheduler. In this case the requests are never blocked and scheduled at the first available time which can be outside the scheduling window. In this case, both the SPF and SWF algorithms keep on sliding the window until they find an available path. The difference between the two approaches is that SPF checks k different paths before sliding the window by one slot, while SWF slides the window by 2dslots before checking another path. The results are given for NSF-NET in figure 3.16. In figure 3.16a, the average reservation delay is plotted versus the arrival rate for all four scheduling schemes. In figure 3.16b, the percentage of reservations outside the scheduling window are plotted versus the arrival rate for all the four schemes. We can see that at high arrival rates the network becomes unstable and around 90% [figure 3.16b] of the requests are reserved at a time outside the scheduling window. Also the delay increases tremendously to an average of 500 slots which is not desirable [figure 3.16a]. But at lower rates there is a low percentage of connections scheduled outside the window and also with a low reservation delay. We can see the SPF and LB-SPF schemes give better results because they tend to reserve the request at a time close to the start of the scheduling window. Obviously in case of a non-blocking scheduler, a stability condition needs to be developed in order to protect the network from becoming unstable, i.e., the reservation delay becomes very large. Such a condition is not necessary for a blocking scheduler, where a request will get blocked if it cannot be scheduled within its requested window [s,e].

For wavelength assignment, we have implemented two approaches First-Fit and Min-Gap as discussed earlier. We can see [figure 3.17] that the Min-Gap scheme with minimum leading and trailing gap give the lowest blocking. The First Fit scheme also performs well. The Best-Fit Min-Gap scheme has the worst performance because it leaves small unused gaps before and after a reservation. These small gaps cannot be used and this results in more fragmentation.

We have studied how the requests with long and short durations affect each other. For this we generated requests which have a maximum duration dmax=2 with 50%

proba-bility and dmax=20 with 50% probability. We compared the results of blocking probability

of both classes of requests with the results when they are not in the presence of each other. We ran the simulation experiment for NSF-NET. The requests arrive in a Poisson fashion. The advance reservation scheme used is LB-SWF with First-Fit. In figure 3.18, the blocking probability curves are plotted against the arrival rate for a) all requests have dmax=20. b)

(42)

(a) Average Reservation Delay

(b) Percentage of Reservations outside the scheduling window

(43)

o

(a) NSF-NET with 50 wavelengths/link

(b) NSF-NET with 100 wavelengths/link

(44)

Figure 3.18: Requests for long and short durations simultaneously

dmax=20. We have two curves for the last case, one for each class of request. These are

labeled as ”Mixed d max(2)” and ”Mixed d max(20)”. We see that both classes of requests affect each other and the blocking probability increases for both. The blocking probability of requests with short duration, i.e., dmax=2 only was zero before and it increases with the

presence of requests with dmax=20. Same is the case with the long requests.

We have also conducted simulation experiments with requests arriving in a bursty manner using the Interrupted Poisson Process (IPP). The IPP arrival process is an ON/OFF process where both the ON and OFF periods are exponentially distributed. The transition rates for the ON and OFF states areφand ψ. During the ON state, the process generates Poisson arrivals with rate λon whereas in the OFF state there are no arrivals. An IPP

is bursty because the arrivals are batched together during the ON period and no arrivals occur during the OFF period and it becomes burstier when long OFF periods are followed by short ON periods with a large arrival rateλon. An IPP is uniquely characterized through

the parametersφ,ψ andλon. We use the squared coefficient of variation c2 to characterize

the burstiness of the arrival process. This is the ratio of the variance to the squared mean of the interarrival time. A small c2 represents interarrival times that concentrate mostly around the mean. For a Poisson process the c2 is equal to 1. For an IPP the c2 is always greater than 1 [15].

(45)

Figure 3.19: IPP Arrival Process

parameters φ, ψ and λon to characterize and IPP. Given c2 and λavg, we can determine

these with the following equations [15].

λon= λavg

r (3.1)

φ= 2λavg(1−r) 2

r(c2₋₁₎ (3.2)

ψ= 2λavg(1−r)

c2₋₁ (3.3)

where,

λavg is the IPP average arrival rate

and r is the coefficient of burstiness and is defined by

r= ψ

φ+ψ (3.4)

We have set the value of c2 _{= 10 and r = 2 for burstier arrivals. In the graph the} blocking probabilities are plotted against various values for λavg for NSF-NET and we can

(46)

higher compared to the Poisson arrivals [see figure 3.9]. Load Balancing schemes still give better results and LB-SWF performs the best.

Failure Recovery

We used a failure model with link failures only. We assume that when a link goes down all wavelengths on that link fail. Only one link can fail at a time and this link is randomly selected from all the links. The mean time to failure is exponentially distributed with a mean of 80 slots. The recovery time is also exponentially distributed with a mean of 48 slots.

The parameters of interest are the percentage termination and the overhead. Per-centage termination is the perPer-centage of reserved connections that cannot be re-routed and are dropped. The overhead is defined as the percentage of reserved connections that are unnecessarily re-routed as they were un-affected by the failure because the reservation start time turns out to be after the instant that link is repaired. Also we are interested in how the overall blocking probability is affected by the failure recovery process.

We calculate the re-routing interval using the following three policies

1. The Fixed re-routing interval with feedback: The length of the re-routing is fixed. When the link goes down the connections are re-routed for this fixed interval. If the link does not come up at the end of the interval, the re-routing interval is increased by an amount equal to the fixed value.

2. Adaptive re-routing interval with feedback: In this case the re-routing interval is calculated using a moving average of the historical recovery times. If the link does not come up at the end of the interval, the re-routing interval is increased by an amount equal to the last re-routing interval length.

3. All future reservations are re-routed: In this case there is no re-routing interval, all the reservations on the failed link are re-routed.

We compare all the three approaches with the ideal scenario where the failure duration is known and only the flows affected are re-routed.

(47)

(a) Percentage Termination and Overhead

(b) Blocking Probability

Figure 3.20: Failure Recovery

(48)

in the network due to a large number of sub-optimal paths [see figure 3.20b].

Periodic Re-configuration

In order to improve the network performance, we have implemented periodic re-configuration of existing reservations. The network topology considered is NSF-NET. The re-configuration process occurs every 24 slots and re-configures the existing reservations for the next 24 slots without changing the reservation times. This is done by sorting the requests in an ascending order of their start times and then re-scheduling them one at a time starting from the request with the earliest start time. This process can result in terminations, in which case we restore the reservation table to its last state (i.e. no re-configuration). We observed that the re-configuration process failed most of the time, i.e., the reservation table was restored to its last state due to at least one termination, especially at high rates. We also observed that if we allowed for a small percentage of terminations, the process succeeded most of the time.

Figure 3.21a shows the blocking probability as a function of the arrival rate with and without re-configuration process. We can see that the overall blocking probability is slightly improved with the re-configuration. Also re-configuration which allows 2% or 5% blocking of the reserved connections, lying in the re-configuration period, further improved the overall blocking probability in the network. The overall blocking probability also in-cludes the terminations during the re-configuration. In figure 3.21b, the percentage of time the re-configuration process fails is plotted against the arrival rate. The results show that at higher arrival rates the re-configuration fails more frequently, but this improves signif-icantly if some amount of termination is permitted. With 5% allowed termination the re-configuration succeeds all the time even at higher arrival rates.

(49)

(a) Arrival Rate vs Blocking Probability - NSF-NET

(b) Failure in Reconfiguration

(50)

Chapter 4

Monitoring and Discovery for Grid

Middleware

Resource Monitoring and Discovery of large computational data Grids is essential to guarantee high performance and reliability. In this chapter, we identify the various resource monitoring and discovery issues and challenges that need to be solved in a large adaptive Grid environment. We survey the existing Grid monitoring solutions and discuss the monitoring and discovery framework that we have developed for the EnLIGHTened Computing Project.

(51)

4.1 Existing Monitoring and Discovery Frameworks for Grid

When Grid computing gained popularity, many groups developed Grid monitoring systems [35, 39, 40, 42]. An overview of these different monitoring tools and frameworks is given in this section.

4.1.1 Grid Monitoring Architecture (GMA)

As the Grids are highly distributed and large, the Grid monitoring systems need to be scalable and interoperable. A general purpose information management system can not meet these requirements since the required characteristics of performance information are different. The Global Grid Forum (GGF) [6] defined a Grid Monitoring Architecture (GMA) to meet these requirements [38]. This architecture consists of three components: Consumers, Producers and a Registry/Directory Service. Producers collect information from sensors, register themselves with the Registry and describe the type and structure of information they want to make available to the Grid. Consumers can query the Registry to find out what type of information is available and locate Producers that provide such infor-mation. Once this information is known the Consumer can contact the Producer directly to obtain the relevant data. Three kinds of interactions are supported for transferring data between producers and consumers: publish/subscribe, query/response, and notification. In the first kind of interaction the initiator specifies interest in some kind of information and subscribes to it which it later terminates while in case of query/response, all the transfer of information as a result of a query is done in a single response. Notification is a one stage interaction done by the producer to the consumer. Apart from the describing basic archi-tecture the GGF also specified certain guidelines for the implementation like fault tolerance, adaptability, scalability, manageability, non-intrusiveness, distribution, efficiency and secu-rity. As a result, many monitoring frameworks were designed to meet the requirements of GMA. These include the R-GMA, Globus MDS, NWS etc.

4.1.2 MonALISA

(52)

variety of Grids. This system consists of autonomous multi-threaded, self-describing agent-based subsystems which are registered as dynamic services, and are able to collaborate and cooperate in performing a wide range of monitoring tasks. MonALISA is designed to easily integrate existing monitoring tools and procedures and to provide this information in a dynamic, self describing way to any other services or clients. These tools and procedures include SNMP, Ganglia, PBS, LSF, Condor, Abing, Iperf etc and modules for monitor-ing and controllmonitor-ing optical switches. In a Monalisa Service, the received values are stored locally into a relational database. This information can also be used to create WEB repos-itories components with selected information from specific groups of MonALISA services. The WSDL/SOAP interface is available in both the services and the repositories so that clients can access data from a specific Grid farm or, through a repository, they can access information received from several Grid farms.

MonALISA is a robust and flexible monitoring system which can be used to create higher level services to perform scheduling decisions that adapt themselves dynamically to respond to changing load patterns in large Grids.

4.1.3 Globus MDS

(53)

this information using Web service interfaces. Furthermore, the index services can register to each other in a hierarchy in order to aggregate data at several levels.

4.1.4 Network Weather Service

The NWS (Network Weather Service) [42, 4] is a distributed system that moni-tors and forecasts the performance of various network and compute resources that form the Grid. The service consists of a set of performance sensors from which it gathers readings of the instantaneous conditions at regular time intervals. It then uses numerical models to predict near-term of performance of the system. It has been developed to be used by dynamic schedulers and to provide QoS in a networked computational environment. The NWS is made up of four components. The first being the sensors, measure bandwidth, latency CPU, memory and disk usage. Next, a ’Memory Host’ is used to store the moni-tored data temporarily. The sensors and memory host register themselves with the ’Name Server’. Lastly the ’Forecaster’ is used to make predictions on the monitored performance information. Presently, mean-based, median-based and autoregressive methods are used for forecasting. NWS identifies the best forecasting technique for a give resource by applying all of them and choosing the one with the lowest cumulative error, thus providing greater accuracy.

4.1.5 PerfSONAR

perfSONAR (PERFormance Service Oriented Network monitoring ARchitecture) [22, 5] is an infrastructure for network performance monitoring concentrating on the end-to-end performance. The perfSonar architecture is developed through a collaboration between ESNet, GEANT2 and Internet2. It contains a set of services delivering performance mea-surements in a multi-domain environment. The project greatly focuses on standardization of interfaces, usability and security. The design goals for this project include flexibility, extensibility, openness, and decentralization. Hence, perfSONAR is an effort to automate monitoring data exchange between networks by simplifying troubleshooting performance problems occurring between sites connected through several networks.

(54)

retrieved from the Measurement Point Services, a Lookup Service to registers all services, an Authentication Service for managing domain-level access to services via tokens, a Trans-formation Service that offers custom data manipulation of existing archived measurements. The Resource Protector Service manages granular details regarding system resource con-sumption and lastly the Topology Service offers topological information on networks.

4.2 EnLIGHtened Computing Monitoring Framework

In this section we discuss the various performance measures that are required by the ERB to select and allocate Grid resources. We discuss the monitoring characteristics of the network and the compute resources separately.

4.2.1 Network Performance Measures

Optical Path Characteristics

The EnLIGHTened testbed consists of four GMPLS-enabled optical switches, see Figure 1.1. We currently monitor the connections and cross-connects at the optical switches. The presence of light determines the establishment of a connection and thus we monitor the power level at the ports. The link status can be determined by the presence of light between the two ports. The active cross-connects at the optical switches are also monitored in the same way.

Bandwidth

Bandwidth is defined as the data transferred in a unit time. Bandwidth can be measured at both path and link level. We currently measure the incoming and outgoing traffic at the interfaces of the network elements for each link as well as the end-to-end bandwidth to determine the bottleneck bandwidth for the entire path. There are four characteristics [33] that describe the ”bandwidth of a path” i.e capacity, utilization, available bandwidth and achievable bandwidth. Each of these can be used to describe characteristics of an entire path as well as hops.

Delay and Jitter

(55)

existing tools to measure end-to-end and round trip delays line ping, pingER etc. Jitter is the variance in the delay and it is a critical parameter for the voice and video applications. Jitter is used in sizing playout buffers for applications requiring regular delivery of packets.

Packet Loss

Packet loss can impact the quality of service provided by network application programs. The sensitivity to loss of individual packets, as well as to frequency and patterns of loss among longer packet sequences is strongly dependent on the application itself [33]. For streaming media, packet loss results in reduced quality of sound and images. For data transfers, packet loss can cause severe degradation of achievable bandwidth.

Network Topology

The Grid middleware needs to have the network topology for scheduling decisions because the resources are located at different sites with different domains and policies. This information is needed by the DNRM for path computation. The ERBs of different domains will exchange the abstracted topology information for path computation across multiple domains. The topology information can also be used for optimization of the network. In the EnLIGHTened computing project we use OSPF-TE for distribution of routing information. We obtain the topology information from the Link State Database of one of the optical switch in the domain. For the ethernet switches this information can be obtained using SNMP MIBs if they support the OSPF MIBs.

Traffic Flows Analysis

A flow is identified as a unidirectional stream of packets between a given source and destination both defined by a network-layer IP address and transport-layer source and destination port numbers. This detailed traffic flow information can be used for application and user monitoring and profiling, network planning, ensuring and verifying QoS.

4.2.2 Compute Resource Characteristics

Cluster Monitoring

(56)

like number of jobs running, finished, queued etc. We also monitor the load, cpu usage, memory usage, disk usage of each node that are part of the cluster. Since jobs from several Grids might be running on the same cluster, the monitoring modules need to interact with the Grid middleware to get the information related to only those jobs that are scheduled by the middleware for EnLIGHTened Grid.

Application Specific Monitoring and Discovery

The ERB also requires application specific monitoring and discovery information. For example, a visualization Grid application may need information about the visualiza-tion servers such as hardware informavisualiza-tion, graphic cards, resoluvisualiza-tion etc., and the dynamic information like cpu usage, load and bench marking performance data.

4.3 Monitoring Issues

The Monitoring system should collect and deliver monitoring information reliably and on-time. But there are several issues that have to be dealt with to ensure this reliability and robustness. These are explained in detail below:

4.3.1 Information Collection

The monitoring information encompasses a variety of parameters collected from different Grid components using several tools and techniques discussed before. There are many implementations already available for many of the measurements. We need to com-pare these and figure out the most accurate and suitable and make modifications and enhancements to existing solutions wherever required and possible. The architecture has to be scalable to accommodate more tools without affecting the entire system.

4.3.2 Representation Format

(57)

time being as they have become universal description languages and are being used by the majority of the Grid community.

4.3.3 Update Frequency

The frequency of update from different monitoring sensors plays a very important role in determining the overall load of monitoring on the Grid. The main challenge is to collect this information accurately at near real-time basis without overloading the network to avoid conflicts and blocking. For example we collect the optical switch information using TL1 currently. A higher frequency of these queries increases the CPU and memory consumptions the switch. Similarly end-to-end bandwidth measured by Iperf introduces heavy traffic on the network, so may be its better to conduct a test when required or after longer time periods. We need to analyze the trade offs to determine the right frequency for different types of tools while not affecting the freshness of information. Because if it is collected after long time intervals it is old and will not be very effective.

4.3.4 Non-Intrusiveness

Different monitoring techniques require different amount of resources themselves. The load incurred by the monitoring tools should be insignificant part of the Grid usage. Similarly we do not want the monitoring applications to require any root privileges or special permissions etc. on the network elements and compute resources.

4.3.5 Monitoring Strategies

Active and Passive Monitoring

(58)

Emphasis on Standard Protocols

As discussed above, it is not a good idea to use special permissions or administrator privileges for the monitoring applications. Instead, we need to choose monitoring techniques that require minimum of these. We must select standard protocols and methods like SNMP for management of network devices.

Link vs Path Monitoring

End-to-end path monitoring gives a view of a system that is filtered through rout-ing. This type of monitoring is very useful for Grid aware applications as they give a summarized view of the network that helps the ERB in resource selection considering the network path characteristics. The single link monitoring gives a view from a single observa-tion point. These measurements give a very detailed view of the network and can be used in failure detection.

4.3.6 Distribution of Monitoring Data

The monitoring data is stored at some place to be used by the middleware. It is recommended that the repository where it is stored should not be centralized. It is better to have several small repositories with each site to avoid single point of failure. The second problem with centralized data management is that it forms a performance bottleneck. For dynamic data writes often outnumber reads with measurements taken every few seconds or minutes [38].

4.3.7 Security

Since the monitoring information will include the detailed system information, network configurations and topology, it is important to secure this data from malicious users and attackers. Public key certificates can be used to enforce authentication. There should be an access control list to avoid any intrusions.

4.3.8 Fault Tolerance

(59)

Figure 4.1: Monitoring Architecture

4.4 Monitoring Architecture

(60)

Figure 4.2: MonALISA client GUI : current monitoring information from the EnLIGHTened testbed can be viewed

4.5 Current Deployment Scenario

Currently we have MonALISA services running at four Grid sites in Raleigh, Chicago, Baton Rouge and Los Angeles. These services have their own databases and web services to store and publish the information. We also have a web repository to collect relevant data from all these sites to maintain history and detailed analysis. These services can be seen in the Figures 4.2 and 4.3.

(61)

(62)

Chapter 5

Conclusions and Future Work

In this thesis, we have evaluated various techniques for advance scheduling of light-paths that can be implemented in a Grid resource scheduler. The scheduling of lightlight-paths in-volve both routing and wavelength assignment. Our simulation results show that minimum cost adaptive routing that tries to balance the load in the network provides the minimum blocking. Also, searching for k alternate paths within the scheduling window significantly improves the performance. For wavelength assignment, we have evaluated First-Fit and Min-Gap schemes and our results show that the Min-Gap scheme that minimizes unused leading or trailing gaps gives better performance.

We have also analyzed several approaches for failure recovery. In all these ap-proaches a re-routing interval is determined and all the reservations on the failed link dur-ing this period are re-scheduled. The results indicate that a short re-routdur-ing interval causes a large number of terminated connections while a long interval re-routes unaffected con-nections. There is a tradeoff between the two and the best results are obtained when the re-routing interval is based on the moving average of the historical failure times.

The network utilization can be improved further by offline optimization of the reserved connections that are not in service yet. In our simulations, we have implemented periodic re-configuration of reserved lightpaths. This improves the performance but not significantly. The results are slightly improved if we allow for termination of already reserved requests.

Finally, we have come up with an architecture to support the Grid Resource Broker in making scheduling decisions and providing failure recovery by using the up-to-date status of the Grid resources.

(63)

• Implement the advance scheduling algorithms in the DNRM: In this thesis, we have presented our results based on simulations. The next step is to actually implement and test these scheduling algorithms in the DNRM for the EnLIGHTened Computing Project, integrate the DNRM with the Monitoring and Discovery component and test how fast the system reacts to the network faults.

• Scheduler for a distributed environment: We have considered a centralized Network Resource Manager for a single domain. In view of this we assume that the DNRM has complete access to all the network resources and keeps track of the current status of these resources. However, this is not the case in a distributed environment, where there are multiple domains each controlled by its own DNRM. In this environment, each DNRM does not have a complete view of the other domains. The scheduling schemes have to be adopted to this distributed environment.

(64)

Bibliography

[1] Cheetah web page. http://cheetah.cs.virginia.edu/.

[2] Dragon web page. http://dragon.east.isi.edu/twiki/bin/view/Main/WebHome.

[3] DynaCode web page. http://www.nsf.gov/awardsearch/showAward.do?

AwardNumber=0540374.

[4] NWS Webpage. http://nws.cs.ucsb.edu.

[5] perfSonar web page. http://wiki.perfsonar.net.

[6] The Global Grid Forum web page. http://www.gridforum.org.

[7] The Globus Alliance web page. http://www.globus.org.

[8] Enlightened computing project website. http://www.enlightenedcomputing.org.

[9] G-lambda project website. http://www.g-Lambda.net.

[10] Global lambda integrated facility (glif) website. http://www.glif.is.

[11] Maui scheduler website. http://www.clusterresources.com/pages/products/

maui-cluster-scheduler.php.

[12] Phosphorus project website. http://www.ist-phosphorus.eu.

[13] User-controlled lightpath website. http://www.uclp.ca.

(65)

[15] Tzvetelina Battestilli. Phd dissertation: Performance analysis of optical burst switched networks with dynamic simultaneous link possession, 2005. North Carolina State Uni-versity.

[16] Randeep Bhatia, Murali S. Kodialam, and T. V. Lakshman. Fast network re-optimization schemes for mpls and optical networks. Computer Networks 50(3), pages 317–331, 2006.

[17] Eric Bouillet, Jean-Francois Labourdette, Ramu Ramamurthy, and Sid Chaudhuru. Lightpath re-optimization in mesh optical networks. pages 437–447, 2005.

[18] Lars-Olof Burchard. Networks with advance reservations: Applications, architecture, and performance. Journal of Network and Systems Management, 13(4), December 2005.

[19] Lars-Olof Burchard, Hans-Ulrich heiss, and Ceaser A. F. De Rose. Performance issues of bandwidth reservation for grid computing. In Proceedings of 15th Symposium on Computer Architecture and High Performance Computing, 2003.

[20] Lars-Olof Burchard, Barry Linnert, and Joerg Schneider. Rerouting strategies for networks with advance reservations. e-Science 2005, pages 446 –453, 2005.

[21] I. Chlamtac, A. Ganz, and G. Karmi. An approach to high-bandwidth optical wans. IEEE Transactions on Communications, 40(7):1171–1182, July 1992.

[22] Tommaso Coviello, Tiziana Ferrari, Kostas Kavoussanakis, Loukik Kudarimoti, Mark Leese, Alistair Phipps, Martin Swany, and Arthur S. Trew. Bridging network monitor-ing and the grid. InCESNET 2006, 2006.

[23] Chiara Curti, Tiziana Ferrari, Leon Gommans, S. van Oudenaarde, Elisabetta Ronchieri, Francesco Giacomini, and Cristina Vistoli. On advance reservation of het-erogeneous network paths.Future generation Computer Systems, 21(4):525 – 538, April 2005.

(66)

[25] Ian Foster. Gt4 primer. Available from URL http://www.globus.org/toolkit/docs/4.0/key/, May 2005.

[26] Eric He, Xi Wang, and Jason Leigh. A flexible advance reservation model for multi-domain wdm optical networks. IEEE GRIDNETS 2006, October 2006.

[27] Andrei Hutanu, Gabrielle Allen, Stephen D. Beck, Petr Holub, Hartmut Kaiser, Ar-chit Kulshrestha, Miloˇs Liˇska, Jon MacLaren, Ludˇek Matyska, Ravi Paruchuri, Steffen Prohaska, Ed Seidel, Brygg Ullmer, and Shalini Venkataraman. Distributed and collab-orative visualization of large data sets using high-speed networks. Future Generation Computer Systems: The International Journal of Grid Computing: Theory, Methods and Applications, 8:1004–1010, Oct. 2006.

[28] A. Jaekel. Lightpath scheduling and allocation under a flexible scheduled traffic model. GLOBECOMM 2006, 2006.

[29] J. Kuri, N. Puech, M. Gagnaire, E. Dotaro, and R. Douville. Routing and wavelength assignment of scheduled lightpath demands. IEEE JSAC, 21(8):1231–1240, October 2003.

[30] Alvaro Saurin Ladan Gharai, Tom Lehman and Colin Perkins. Experiences with high definition interactive video conferencing. In Proceedings of IEEE International Con-ference on Multimedia and Expo (ICME), Toronto, Canada, July 2007.

[31] I. C. Legrand, H. B. Newman, R. Voicu, C. Cirstoiu, C. Grigoras, M. Toarta, and C. Dobre. Monalisa: An agent based, dynamic service system to moni-tor, control and optimize grid based applications. In CHEP 2004, 2004. URL http://monalisa.cacr.caltech.edu/.

[32] Jen-Yao Chung Liang-Jie Zhang and Qun Zhou. Developing grid comput-ing applications, part 1. March 2005. Available from URL http://www-128.ibm.com/developerworks/library/gr-grid1/.

(67)

[34] Rajiv Ramaswami and Kumar N. Sivarajan. Routing and wavelength assignment in all-optical networks. IEEE/ACM Transactions on Networking, 3(5):489–500, 1995.

[35] R. L. Ribler, J. S. Vetter, Huseyin Simitci, and Daniel A. Reed. Autopilot: Adaptive control of distributed applications. In HPDC, pages 172–179, 1998. Available from URL http://citeseer.ist.psu.edu/ribler98autopilot.html.

[36] Jennifer M. Schopf, Ioan Raicu, Laura Pearlman, Neill Miller, Carl Kesselman, Ian Foster, and Mike DArcy. Monitoring and discovery in a web services framework: Func-tionality and performance of globus toolkit mds4. Technical report, Technical Report ANL/MCS-P1248, 2004.

[37] Globus Toolkit MDS Team. Mds4 and project deployments. Technical report, GGF, 2005. Available from URL http://www-unix.mcs.anl.gov/ schopf/Pubs/jmspubs.html.

[38] B. Tierney, R. Aydt, D. Gunter, W. Smith, M. Swany, V. Taylor, and R. Wolski. A grid monitoring architecture. Technical report, GGF Performance Working Group, January 2002.

[39] Brian Tierney, Brian Crowley, Dan Gunter, Mason Holding, Jason Lee, and Mary Thompson. A monitoring sensor management system for grid environments. In HPDC, pages 97–104, 2000. Available from URL http://citeseer.ist.psu.edu/tierney00monitoring.html.

[40] A. Waheed, W. Smith, J. George, and J. Yan. An infrastructure for monitoring and management in computational grids. InProceedings of the 2000 Conference on Lan-guages, Compilers, and Runtime Systems, pages 235–245, 2000.

[41] Bin Wang, Tianjian li, Xubin Luo, Yuqi Fan, and Chunsheng Xin. On service provi-sioning under a scheduled traffic model in reconfigurable wdm optical networks. 2nd International Conference on Broadband Networks, 1:13 – 22, October 2005.

(68)

[43] Xi Yang, Lu Shen, Ajay Todimala, B. Ramamurthy, and T. Lehman. An efficient scheduling scheme for on-demand lightpath reservations in reconfigurable wdm optical networks. In Optical Fiber Communication Conference 2006 and the 2006 National Fiber Optic Engineers Conference, page 32, 2006.

[44] H. Zang, J. Jue, L. Sahasrabuddhe, R. Ramamurthy, and B. Mukherjee. Dynamic lightpath establishment in wavelength routed networks, 2001. IEEE Communications Magazine, 39(9):100–108.

[45] H. Zang, J.P. Jue, and B. Mukherjee. A review of routing and wavelength assignment approaches for wavelength-routed optical wdm networks. Optical Networks Magazine, 1:47–60, January 2000.

Network Resource Scheduling and Management of Optical Grids

Contents

List of Figures

List of Tables

Chapter 1

Introduction

1.1

Background

1.2

Motivation

1.3

Contribution

1.4

Thesis Layout

Chapter 2

Related Work

2.1

Advance Reservation of Lightpaths

2.2

Routing and Wavelength Assignment of Advance

Reser-vation Requests

2.3

Failure Recovery

2.4

Network Re-optimization

Chapter 3

Advance Reservation Scheduling

Strategies

3.1

Problem Description

3.2

Scheduling Algorithms

3.3

Wavelength Assignment

3.4

Network Failures

3.5

Network Re-Optimization

3.6

Performance Evaluation

Chapter 4

Monitoring and Discovery for Grid

Middleware

4.1

Existing Monitoring and Discovery Frameworks for Grid

4.2

EnLIGHtened Computing Monitoring Framework

4.3

Monitoring Issues

4.4

Monitoring Architecture

4.5

Current Deployment Scenario

Chapter 5

Conclusions and Future Work

Bibliography