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The PBNM architecture addresses both of the challenges with existing network management systems discussed earlier Con guration of all the individual network parameters is simpli ed and automated Administrators do not need to con gure each individual parameter They just de ne high-level policies and goals through the policy management tool and the PBNM system is then responsible for con guring the detailed individual con guration parameters automatically based on the high-level input This is accomplished by the PDP, which parses the high-level policies, as discussed earlier, and then con gures the PEPs in a way that is consistent with those policies The PBNM also deals effectively with changes whether they are introduced by the administrators or due to changes in the network itself Administrators can introduce new high-level policies or change existing policies through the policy management tool The PDP is then responsible for parsing the changes introduced by the administrators and recon guring the PEPs to ensure that the updated policies are enforced The PDP also gets noti ed about changes in the network such as cyber attacks, faults, and performance degradation The PDP is then responsible for recon guring the PEPs based on the new events to ensure that network effectively supports the highlevel policies The PBNM architecture is a very elegant architecture that can address the challenge of automating network management and in particular dealing with the constant changes that are of particular importance for wireless ad hoc networks There are two areas of the architecture that have proven to be challenging in implementing the high-level PBNM architecture and have attracted the interest of the research community The rst area is the de nition of policy languages that can be used for capturing the high-level goals that need to be enforced by the PBNM architecture The second area is the problem of extending the PBNM architecture to operate in a fully distributed environment We will now discuss these two areas in detail.

package jswc04b;

The smallest vector component of vector expression is, of course, minus the largest component of vector expression Maximum or minimum selection is useful in parameter-influence studies and optimization studies (Section 4-3d) Note that these operations apply neatly to arrays created by vector equivalences (Section 3-11)

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import javaxejb*; import javarmiRemoteException; /** * This is the Home Interface for the Stateful Session EJB * <tt>OrderSF</tt> * @see jswc04bOrderSF * @see jswc04bOrderSFBean */ public interface OrderSFHome extends EJBHome { /* Creates a Stateful Session EJB to manage a new order customer the customer this order belongs to the created order, @exception P2GException if the customer doesn't exist */ OrderSF create(Customer customer) throws CreateException, RemoteException, P2GException; /* Creates a Stateful Session EJB to manage an existing order @param orderNum the primary key of the existing order @return the created order Sexception P2GException if the order doesn't exist */ OrderSF create(long orderNum) throws CreateException, RemoteException, P2GException; } @param Sreturn

The remote interface defines a large number of methods that are available in the implementation of the bean:

(b) Masking Vector Expressions Vector expressions used with Vector and Vectr d/dt operations (and also with Vectr delta operations, Section 3-4) can be masked with an n-dimensional mask vector vv, as in

package jswc04b;

The policy language used for allowing users and administrators to de ne the high-level policies is critical for making the policy management architecture effective If the policy language is not expressive enough then it cannot capture the user requirements Therefore it may not be possible to effectively use the policy management architecture for the speci c application If the language is too complex then it may be too dif cult for users to understand As a result, users may not be inclined to use the system The best way to de ne policies is through a natural language that is easy for a user to use and understand Unfortunately, little progress has been made towards achieving that goal Three main approaches have been pursued instead by the research community so far for creating policy languages that can be used in the policy management architecture:

import javaxejb*; import JavarmiRemoteException; import JavautilVector; /** * This is the remote interface for the Stateful Session EJB * <tt>OrderSF</tt> * Remote Interface for the Stateful Session EJB "Order" * * @see jswc04bOrderSFHome * @see jswc04bOrderSFBean */ public interface OrderSF extends EJBObject { /** * Returns this order's customer * * @return the customer */ public Customer getCustomer() throws RemoteException; /** * Returns this order's OrderMaster information * * Sreturn the OrderMaster */ public OrderMaster getOrderMaster() throws RemoteException; /** * Returns all the order details included in the order

The ith component of a masked vector expression is set to 0 for all values of the index i such that vv[i] 0 Mask vectors vv are set up by the experimentprotocol program and do not change in the course of a simulation run Vector masking has been used to prune neuron layers in neural-network simulations

* return a Vector with all the order details */ public Vector getOrderDetail() throws RemoteException; /** * This method sets the outlet code the customer will * get the order from It automatically sets the delivery mode * to "Outlet" * * @exception P2GException * if the status order is already delivered */ public void setOutlet(long outletNum) throws RemoteException, P2GException;

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