Packet classification by multi-level cutting of the classification space

Traditionally, the Internet provides only a “best-effort” service, treating all packets going to the same destination equally. However, providing differentiated services for different users based on their quality requirements is increasingly becoming a demanding issue. For this, routers need to have the capability to distinguish and isolate traffic belonging to different flows. This ability to determine the flow each packet belongs to is called packet classification. Technology vendors are reluctant to support algorithmic solutions for classification due to their nondeterministic performance. Although content addressable memories (CAMs) are favoured by technology vendors due to their deterministic high-lookup rates, they suffer from the problems of high-power consumption and high-silicon cost. This paper provides a new algorithmic-architectural solution for packet classification that mixes CAMs with algorithms based on multilevel cutting of the classification space into smaller spaces. The provided solution utilizes the geometrical distribution of rules in the classification space. It provides the deterministic performance of CAMs, support for dynamic updates, and added flexibility for system designers.

University space planning and space-type profiles

Universities planning the provision of space for their teaching requirements need to do so in a fashion that reduces capital and maintenance costs whilst still providing a high-quality level of service. Space plans should aim to provide sufficient capacity without incurring excessive costs due to over-capacity. A simple measure used to estimate over-provision is utilisation. Essentially, the utilisation is the fraction of seats that are used in practice, or the ratio of demand to supply. However, studies usually find that utilisation is low, often only 20–40%, and this is suggestive of significant over-capacity.

Our previous work has provided methods to improve such space planning. They identify a critical level of utilisation as the highest level that can be achieved whilst still reliably satisfying the demand for places to allocate teaching events. In this paper, we extend this body of work to incorporate the notions of event-types and space-types. Teaching events have multiple ‘event-types’, such as lecture, tutorial, workshop, etc., and there are generally corresponding space-types. Matching the type of an event to a room of a corresponding space-type is generally desirable. However, realistically, allocation happens in a mixed space-type environment where teaching events of a given type are allocated to rooms of another space-type; e.g., tutorials will borrow lecture theatres or workshop rooms.

We propose a model and methodology to quantify the effects of space-type mixing and establish methods to search for better space-type profiles; where the term “space-type profile” refers to the relative numbers of each type of space. We give evidence that these methods have the potential to improve utilisation levels. Hence, the contribution of this paper is twofold. Firstly, we present informative studies of the effects of space-type mixing on utilisation, and critical utilisations. Secondly, we present straightforward though novel methods to determine better space-type profiles, and give an example in which the resulting profiles are indeed significantly improved.