Supporting Temporal Views in a Management Information Base

Abstract

In many network management applications, likepost-mortem fault analysis or performance trendsprofiling, it is advantageous to have the ability toview the state of the network as it was at some time in the past. To support such Temporal Views anefficient data organization, or access method, is neededfor storing and updating network related data (as thenetwork evolves over time) and for retrieving requested past network states. For applications where thenetwork manager is not interested in the full (and maybetoo large) snapshot of a past network state it is usefulif partial state snapshots can be extracted quickly. It is thus of particular interest toconstruct an access method that can efficiently supportPartial Temporal Views. Efficiency implies that arequested partial temporal view should be constructed directly, without first computing the elaboratefull temporal view. In this paper we present a newaccess method (called the Neighbor History Index) forthis problem. One of the advantages of this method is that the update processing is independent ofthe evolution size (the total number of changes in theevolution). In addition, our method uses a small diskspace overhead. We then present a general framework for organizing time-evolving network data. Ourframework distinguishes between flat and hierarchicalevolutions and subsequently between flat andhierarchical temporal views. We also provide a way toefficiently construct temporal views on hierarchicalevolutions. This paper shows that supporting temporalviews on flat or hierarchical evolutions is notexpensive: our solutions use small space overhead, havesmall updating and compute temporal viewsfast.