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1 L Zhang, Virtual clock: a new traffic control algorithm for packet switching networks, Proc ACM SIGCOMM, pp 19 29, Sep 1990 2 D Verma, H Zhang, and D Ferrari, Guaranteeing delay jitter bounds in packet switching networks, Proc Tricomm, pp 35 46, Apr 1991 3 S J Golestani, A self-clocked fair queuing scheme for broadband applications, Proc IEEE INFOCOM, pp 636 646, Apr 1994 4 A K Parekh and R G Gallager, A generalized processor sharing approach to flow control in integrated services networks: the single node case, IEEErACM Trans Netw, vol 1, no 3, pp 344 357, Jun 1993 5 A K Parekh and R G Gallager, A generalized processor sharing approach to flow control in integrated services networks: the multiple node case, IEEErACM Trans Netw, vol 2, no 2, pp 137 150, Apr 1994 6 D.

Oracle provides a rich set of functionality for action handlers. You ve already gotten an introduction to some aspects of functionality in the previous section. This section looks at functionality in depth. Discussed first is the XSQLActionHandler interface, which defines a class as an action handler. In the next discussion, you ll see all the benefits of the XSQLActionHandlerImpl base class. The XSQLPageRequest class, which was covered in the last chapter, also plays an important role. Though the earlier discussion of this class won t be repeated, you will see how to use the class to access servlet functionality. The last discussion covers some other APIs that can play important roles in your action handler development.

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Stiliadis, Traffic scheduling in packet-switched networks: analysis, design, and implementation, Ph D dissertation, Computer Science Department, University of California at Santa Cruz, Jun 1996 7 D Stiliadis and A Varma, Latency-rate servers: a general model for analysis of traffic scheduling algorithms, Proc IEEE INFOCOM, pp 111 119, Mar 1996 8 D Stiliadis and A Varma, Design and analysis of frame-based fair queueing: a new traffic scheduling algorithm for packet-switched networks, Proc ACM SIGMETRICS, pp 104 115, May 1996 9 D Stiliadis and A Varma, A general methodology for design efficient traffic scheduling and shaping algorithms, Proc IEEE INFOCOM, Kobe, Japan, Apr 1997 10 D Stiliadis and A Varma, Rate-proportional server: a design methodology for fair queueing algorithms, IEEErACM Trans Netw, vol 6, no 2, pp 164 174, Apr 1998 11 D Stiliadis and A Varma, Efficient fair queueing algorithms for packet-switched networks, IEEErACM Trans Netw, vol 6, no.

2, pp175 185, Apr 1998 12 J C R Bennett and H Zhang, WF 2 Q: worst-case fair weighted fair queueing, Proc IEEE INFOCOM, Mar 1996 13 J C R Bennett and H Zhang, Hierarchical packet fair queueing algorithms, IEEErACM Trans Netw, vol 5, no 5, pp 675 689, Oct 1997; Proc ACM SIGCOMM, Aug 1996 14 J C R Bennett, D C Stephens, and H Zhang, High speed, scalable, and accurate implementation of fair queueing algorithms in ATM networks, Proc ICNP, 1997..

Let G = G(V, E) be an undirected graph of vertices, V , with edges, E, which represent the data dependencies in the mesh We assume that both vertices and edges can be weighted (with nonnegative integer values) and that v denotes the weight of a vertex v and similarly for edges and sets of vertices and edges Given that the mesh needs to be distributed to P processors, de ne a partition to be a mapping of V into P disjoint subdomains, Sp , such that P Sp = V The weight of a subdomain is just the sum of the weights of the vertices assigned to it, Sp = v Sp v , and we denote the set of intersubdomain or cut edges (ie, edges cut by the partition) by Ec .

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Finally, note that we use the words subdomain and processor more or less interchangeably: the mesh is partitioned into P subdomains; each subdomain, Sp , is assigned to a processor p and each processor p owns a subdomain Sp The de nition of the graph partitioning problem is to nd a partition that evenly balances the load (ie, vertex weight) in each subdomain, while minimizing the communication cost To evenly balance the load, the optimal subdomain weight is given by S := V /P (where the ceiling function x returns the smallest integer greater than x) and the imbalance is then de ned as the maximum subdomain weight divided by the optimal (since the computational speed of the underlying application is determined by the most heavily weighted processor) Note that 1 and perfect balance is given by = 1.

0.8 0.6 0.4 0.1 -0.6 0.08 0.2 0.06 0.04 0.02 0.2 0.4 -0.2 -0.4 -0.2 -0.1

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