POLICY MANAGEMENT in .NET

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Figure 64 Hierarchical policy management architecture
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is possible that there are multiple layers of hierarchy in which a policy manager responsible for a lower domain is itself a top policy manager for multiple layers below it In a wireless ad hoc networking environment, connectivity in the network is intermittent and therefore it is possible that policy managers in lower domains will become disconnected from the higher layers in the hierarchy This is not a problem in the hierarchical architecture Policy managers that lose connectivity to a policy manager higher in the hierarchy can continue functioning based on the existing policies A new hierarchy may potentially need to be established (for example through the approaches discussed in Section 6243) for coordinating policies across the domains that are still connected, and a policy manager may become the top of the hierarchy temporarily for the layers below it that are still connected to it Once the original hierarchy is reconnected, the policy managers can reestablish the original hierarchy At that point, the policy managers would probably have to resynchronize their states This is because changes that may have occurred during the times that the hierarchy was disconnected could affect policy management across the network For example, the original top policy manager may need to be noti ed of any failures, attacks, or performance degradations, that had occurred during the time that it was disconnected from the rest of the network Policies and states in the higher layers in the hierarchy will probably receive a higher priority when the synchronization happens because they have broader management responsibilities, although more recent events should take priority over past events Performing such state and policy synchronization is a challenging problem in a very dynamic environment with many policy managers losing and gaining connectivity from the hierarchy This problem has not been adequately addressed in the literature The big advantage of the hierarchical architecture compared to the peer-to-peer architecture is its suitability to environments that are naturally hierarchical For example, a military environment is typically hierarchical The top of the hierarchy may be a typical military division headquarters The policy manager in the division headquarters may de ne a policy that needs to be enforced across the division Lower domains in the hierarchy such as brigades, battalions, and companies in the division may have their own policy managers, but will need to comply with the policies speci ed by the policy
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62 POLICY-BASED NETWORK MANAGEMENT
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manager in the division headquarters Policy managers lower in the hierarchy still have some exibility in selecting local policies as long as the local policies are consistent with the global policies that are de ned at the top of the hierarchy Management of networks in commercial enterprises is also naturally hierarchical within enterprises For example, a top level policy manager (probably located in the headquarters of a corporation) may de ne a global policy that applies across the entire enterprise while local organizations within the enterprise may de ne local policies that only apply within that local organization as long as such policies are consistent with the global policy Both the peer-to-peer architecture and the hierarchical architecture seem to have important applications in speci c environments It is also possible that the two architectures can work together in support of some applications For example, in a military environment the hierarchical approach can be used for managing the network of a coalition partner while policy management across coalition partners can be supported by the peer-to-peer architecture Similarly in a commercial environment the hierarchical architecture can be used for managing secure collaboration within the enterprise while the peer-to-peer architecture is used for managing secure collaboration across multiple enterprises 6243 Use of Clustering Even though policy management has been an area of interest in the past several years, most of the efforts have focused on the application of policy management to enterprise wireline and fairly static networks Limited research has been conducted on how to apply the policy management concepts to the wireless ad hoc networking environment The most prominent efforts have been conducted through funding from the Army and have been published in [114 116] A unique challenge in the wireless ad hoc networking environment is the placement of policy servers within the network This problem applies both to the peer-to-peer and hierarchical architectures discussed earlier In a traditional wireline network, the policy manager is placed on speci c nodes that are selected by network administrators when the network is rst con gured The policy manager is not migrated to a different node unless the node hosting it fails In the ad hoc networking environment, a static policy management architecture where the policy manager is placed on a prede ned node is not viable This is because nodes could move around and may cross from one domain to another, nodes could get captured and destroyed on a frequent basis, nodes could lose connectivity from other nodes, often forcing the redrawing of domains, and the network could change in unpredictable ways Hence it might not be appropriate for any prede ned nodes to be hosting the policy manager Therefore, selecting the nodes where to host the policy manager is a challenging research problem in such networks Chadha et al [116] have proposed to use clustering for organizing event reporting and dissemination functions as well as for selecting where to place certain policy management functions in a MANET This concept is similar to the concept used for selecting where to place the intrusion detection correlation capabilities described in 5 The key idea is the use of clustering algorithms for organizing nodes in groups (called clusters) Such groups are typically groups of nodes that are located close to each other and have direct connectivity to each other Once clusters have been formed, the clustering algorithms can be used for electing leaders in those groups called clusterheads The clusterheads can be used for hosting a speci c function for the whole group One such function can be the policy manager or intrusion correlation function as discussed in 5 Since the clusters are usually organized based on how nodes are connected, clusterheads usually tend to be centrally located within the cluster and have direct connectivity with a large number of nodes belonging to the cluster Such an approach reduces the overhead
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