Anna R. Karlin

Network support for IP traceback

This paper describes a technique for tracing anonymous packet flooding attacks in the Internet back toward their source. This work is motivated by the increased frequency and sophistication of denial-of-service attacks and by the difficulty in tracing packets with incorrect, or “spoofed,” source addresses. In this paper, we describe a general purpose traceback mechanism based on probabilistic packet marking in the network. Our approach allows a victim to identify the network path(s) traversed by attack traffic without requiring interactive operational support from Internet Service Providers (ISPs). Moreover, this traceback can be performed “post mortem”—after an attack has completed. We present an implementation of this technology that is incrementally deployable, (mostly) backward compatible, and can be efficiently implemented using conventional technology.

On the scale and performance of cooperative Web proxy caching

While algorithms for cooperative proxy caching have been widely studied, little is understood about cooperative-caching performance in the large-scale World Wide Web environment. This paper uses both trace-based analysis and analytic modelling to show the potential advantages and drawbacks of inter-proxy cooperation. With our traces, we evaluate quantitatively the performance-improvement potential of cooperation between 200 small-organization proxies within a university environment, and between two large-organization proxies handling 23,000 and 60,000 clients, respectively. With our model, we extend beyond these populations to project cooperative caching behavior in regions with millions of clients. Overall, we demonstrate that cooperative caching has performance benefits only within limited population bounds. We also use our model to examine the implications of future trends in Web-access behavior and traffic.

Implementing global memory management in a workstation cluster

Advances in network and processor technology have greatly changed the communication and computational power of local-area workstation clusters. However, operating systems still treat workstation clusters as a collection of loosely-connected processors, where each workstation acts as an autonomous and independent agent. This operating system structure makes it difficult to exploit the characteristics of current clusters, such as low-latency communication, huge primary memories, and high-speed processors, in order to improve the performance of cluster applications. This paper describes the design and implementation of global memory management in a workstation cluster. Our objective is to use a single, unified, but distributed memory management algorithm at the lowest level of the operating system. By managing memory globally at this level, all system- and higher-level software, including VM, file systems, transaction systems, and user applications, can benefit from available cluster memory. We have implemented our algorithm in the OSF/1 operating system running on an ATM-connected cluster of DEC Alpha workstations. Our measurements show that on a suite of memory-intensive programs, our system improves performance by a factor of 1.5 to 3.5. We also show that our algorithm has a performance advantage over others that have been proposed in the past.

A study of integrated prefetching and caching strategies

Prefetching and caching are effective techniques for improving the performance of file systems, but they have not been studied in an integrated fashion. This paper proposes four properties that optimal integrated strategies for prefetching and caching must satisfy, and then presents and studies two such integrated strategies, called aggressive and conservative. We prove that the performance of the conservative approach is within a factor of two of optimal and that the performance of the aggressive strategy is a factor significantly less than twice that of the optimal case. We have evaluated these two approaches by trace-driven simulation with a collection of file access traces. Our results show that the two integrated prefetching and caching strategies are indeed close to optimal and that these strategies can reduce the running time of applications by up to 50%.

Dynamic Perfect Hashing: Upper and Lower Bounds

The dynamic dictionary problem is considered: provide an algorithm for storing a dynamic set, allowing the operations insert, delete, and lookup. A dynamic perfect hashing strategy is given: a randomized algorithm for the dynamic dictionary problem that takes

Competitive randomized algorithms for non-uniform problems

O(1)

Implementation and performance of integrated application-controlled file caching, prefetching, and disk scheduling

worst-case time for lookups and

Empirical studies of competitve spinning for a shared-memory multiprocessor

O(1)

Greedy bidding strategies for keyword auctions

amortized expected time for insertions and deletions; it uses space proportional to the size of the set stored. Furthermore, lower bounds for the time complexity of a class of deterministic algorithms for the dictionary problem are proved. This class encompasses realistic hashing-based schemes that use linear space. Such algorithms have amortized worst-case time complexity

A trace-driven comparison of algorithms for parallel prefetching and caching

\Omega(\log n)