Optical Circuit Switching and Optical Burst Switching

Several optical network architectures based on Optical Circuit Switching (OCS) or Optical Burst Switching (OBS) have been proposed, with the objective of effi-ciently supporting Grid services. The choice of OCS or OBS depends on band-width or delay requirements of the Grid applications. Optical Circuit Switch-ing allows the user to access bandwidth at the wavelength level (e.g., 10 or 40 Gbps), while Optical Burst Switching allows bandwidth to be accessed at the sub-wavelength level.

In general, there are architectures based on either OCS (via wavelength rout-ing) or OBS, depending on the bandwidth or delay requirement of Grid appli-cations. In [25], an OCS-based approach (or Grid-over-OCS) was proposed for applications requiring huge bandwidth for a long period. In this approach, the Grid and optical-layer resources can be managed either separately in an overlay manner or jointly by extending the optical control plane for Grid-resource provi-sioning. Another type of architecture to support Grid services is based on OBS (or Grid-over-OBS), which is suitable for applications having small job sizes [13].

5.6.1 Studies on OCS-based Grids

Recently many authors such as Wang et al.[28], Liuet al.[21], Banerjee et al.

[10], and Demeyer et al. [14] have proposed and developed new algorithms to jointly schedule computing and network resources by modifying the traditional list algorithm. An OCS-based approach was proposed in [25] for applications requiring huge bandwidths, where managing the Grid and optical-layer resources can be done either separately in an overlay manner or jointly by extending the optical control.

In [18] the authors defined a joint scheduling problem in the context of pro-viding efficient support for emerging distributed computing applications in a Lambda Grid network. They focused on jointly scheduling both network and computing resources to maximize job acceptance rate and minimize total schedul-ing time. Various job selection heuristics and routschedul-ing algorithms are proposed and tested on a 24-node NSFNet topology. The feasibility and efficiency of the proposed algorithms are evaluated on the basis of various metrics such as job blocking rate and effectiveness.

The algorithms proposed by Wang et al. [28] and Liu et al.[21] have imple-mented task scheduling to schedule the nodes for Grid resources. Wang et al.

[28] has also used an adaptive routing scheme in communication scheduling to schedule the edges in the optical network along the lightpath.

Wanget al.[28] proposed a heuristic to minimize the completion time of jobs submitted. They proposed a modified list scheduling algorithm, Earliest Start Route First (ESRF). This algorithm has been used to map the resources from DAG to the O-Grid model extended resource system. Here the ESRF algorithm

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has been used to improve the accuracy of the scheduling. This algorithm deter-mined the earliest start route for each schedule by modifying the traditional Dijk-stra algorithm and reduced the total scheduling length. Better performance was identified with the ESRF algorithm, especially with average high node degree, when compared with the fixed and alternative routing algorithms. It has also been observed that the performances of all routing algorithms were identical when sufficient communication resources were available. The modified list algo-rithm uses the greedy approach to allocate resources which may not always be the shortest path. For this reason, the authors remark that the performance of the ESRF routing algorithm could have been improved with a better modified list algorithm.

The new algorithm [28] was implemented on a 16-Node network, the NSF-network and a mesh torus NSF-network and the algorithm of [21] was implemented on the ASAP network. The uniqueness of the work by Liuet al.[21] and Banerjee et al.[10] was that both used an integer linear programming (ILP) approach for scheduling. The authors in [10] had also implemented a greedy approach over ILP to improve the scalability of the network.

Lianget al.[19] proposed an optical Grid model based on the characteristics of optical network. The model presented a communication contention-aware solu-tion to minimize the total execusolu-tion time. The solusolu-tion was based on the list scheduling for given tasks in an optical Grid. The Dijkstra algorithm was modi-fied and deployed to minimize the total scheduling time. The modimodi-fied Dijkstra algorithm was proved to be more feasible and efficient.

Liuet al.[21] present formulations to minimize the completion time and mini-mize the cost usage to satisfy a job. They propose an algorithm to jointly sched-ule network and computing resources. They use fixed routing over any adaptive algorithm to schedule the network resources. They proposed a greedy approach to schedule and execute tasks sequentially without any contention. They also propose a list scheduling approach that embeds the greedy approach in a list heuristic algorithm. To minimize the cost usage in the network, they propose a min-cost algorithm which tries to minimize the cost involved in the network along with the deadline constraint. The results showed that for a pipelined DAG, the scheduling length was less for the new list algorithm than for the tradi-tional list algorithm. It has also been reported that for a general DAG the new list algorithm had an insignificant advantage over the traditional list algorithm.

Though the network scheduling and computing node schedule is effective, it is more application-specific. Usage of an adaptive algorithm can increase the per-formance of the overall network.

Banerjee et al. [10] have considered the identification of a route for the file transfer and scheduling it over the respective circuits. The authors formulated a mathematical model and used the greedy approach to solve for the routing and scheduling on a Lambda Grid. Ahybrid approach for both online and offline scheduling has been proposed. In this approach, offline scheduling solved for the route and the transfer of files. The provision for readjustment of the time in

100 A. Ravula and B. Ramamurthy

online scheduling, to transfer the entire file, reduced the total transfer time. The developed TPSP algorithm, proving its ability to optimize with MILP, was used for offline scheduling. The inappropriate scaling with the MILP made the authors use the greedy approach for TPSP. The approach chose a file and determined its earliest route and then scheduled that file transfer along that route.

They [10] also proposed two heuristics to determine the best file which was then routed and scheduled using the APT-Bandwidth scheduling or K-Random Path (KRP) algorithms. The best file was chosen with either the largest file first (LFF) or the most distant file first (MDFF) heuristics. For the chosen file, the APT algorithm computed all the time slots between the source and destination for a given duration. The bandwidth scheduling algorithm was implemented which selected the best-fit time slot. Using the KRP algorithm, the best route is chosen from K random paths. The file may be lost or transferred earlier than the finish time during the file transfer within the network. Earlier completion of the file transfer will allow the assignment of the sub-wavelength for later scheduled applications. The entire file or the lost partial file has to be retransmitted when the file is lost. Evaluating their proposed heuristics and algorithms on different networks, the authors identified that the LFF heuristics and the KRP algorithm together had better performance.

5.6.2Studies on OBS-based Grids

In [12], the authors proposed a novel efficient and cost-effective infrastructure for Grids based on a Dual-Link-Server OBS network to improve the performance of the burst contention resolution scheme with an aim of solving the collision problem. The authors in [13] discussed an architecture which supports the Grid services based on OBS, suitable for applications having small job sizes. In [24]

OBS is used for the Multi-Resource Many-cast (MRM) technique for its ability to statistically multiplex packet switching without increasing the overheads. Various heuristics are used to determine the destinations and selection of resources.

Optical Burst Switching is an alternative to jointly scheduling the network and computing resources and was proposed in [24, 25]. The work by Sheet al.

[24] used many-casting over the network to perform OBS and Simeonidouet al.

[25] have utilized an extension of the existing wavelength-switched network and also an optical burst-switching network.

Multi-Resource Many-cast over OBS networks for distributed applications was investigated in [24]. In this network, each source generates requests that required multiple resources and each destination had different computing resources. Each node was deployed with various requirements and resource availability. The objective of this paper was to determine the destination with sufficient resources and a route, to minimize the resource blocking rate. The OBS network was selected for the MRM technique for its ability to statistically multiplex packet switching without increasing the overheads. The authors have used the Closest Destination First (CDF), Most Available First (MAF), and Random Selection (RS) heuristics to determine the destinations. The Limit per Burst (LpB) and

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Limit per Destination (LpD) heuristics were used as resource selection policies.

It has been identified that the performance of the CDF was better than the MAF and RS heuristics on a 14-node NSF network. The authors also suggested that the use of destination selection heuristics minimized the resource blocking rate in resource selection.

Two different optical network infrastructures for Grid services have been pro-posed [25]. The architecture of extended wavelength-switched network facilitated the user-controlled bandwidth provisioning for applications that were data inten-sive. For this approach, three different solutions were provided. The first solu-tion was to separately manage the Grid and optical layer. AGrid middleware managed the Grid resources and the optical layer managed the lightpath. The utilization of Grid middleware APIs enabled user application with the visibility of optical-network topology resources. The G-OUNI interface was the other solu-tion that participated in resource discovery and allocasolu-tion mechanism funcsolu-tions.

The third solution utilized OBS and active router technologies. These solutions were suitable only for data-intensive applications and future Grid services.

Another approach was the use of OBS for a programmable network. This supported data-intensive and emerging Grid applications utilizing the advanced hardware solutions and a new protocol. The OBS networking scheme provided efficient bandwidth resource utilization. This proposed architecture offered a global reach of computing and storage resource using fiber infrastructure. The advantage of the OBS router was its usability in the normal network traffic and also for Grid network traffic.

Similar to the work in [25], Adamiet al.[6] have also used a resource broker for a Grid network. It enhanced the capabilities by providing a network resource manager to manage and integrate the scheduling mechanism of network and computing resources.

In [23], the authors conducted simulation studies for various scheduling scenar-ios within a data Grid. Their work recommends decoupling of data replication from computation while scheduling jobs on the Grid and concludes that it is best to schedule jobs to computational resources that are closest to the data required for that job. But the scheduling and simulation studies are restricted to homogeneous nodes with a simplified First-In-First-Out (FIFO) strategy within local schedulers.

5.7 Conclusion

The introduction to Grid Computing and the ongoing research in Grid net-working were presented in this chapter. The basic architecture of the Grid and Lambda Grids was introduced. Various scheduling schemes in Grid networks have been proposed under different scenarios. Some of the job scheduling schemes in Lambda Grids proposed by researchers are discussed in this chapter. The concept of cloud computing was also discussed. There is a huge scope for research and development in Grid networking.

102 A. Ravula and B. Ramamurthy

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Part II