Task System Problem Research Articles

A Primal-Dual Randomized Algorithm for Weighted Paging

We study the weighted version of the classic online paging problem where there is a weight (cost) for fetching each page into the cache. We design a randomized O (log k )-competitive online algorithm for this problem, where k is the cache size. This is the first randomized o ( k )-competitive algorithm and its competitive ratio matches the known lower bound for the problem, up to constant factors. More generally, we design an O (log( k /( k − h + 1)))-competitive online algorithm for the version of the problem where the online algorithm has cache size k and it is compared to an optimal offline solution with cache size h ≤ k . Our solution is based on a two-step approach. We first obtain an O (log k )-competitive fractional algorithm based on an online primal-dual approach. Next, we obtain a randomized algorithm by rounding in an online manner the fractional solution to a probability distribution on the possible cache states. We also give an online primal-dual randomized O (log N )-competitive algorithm for the Metrical Task System problem (MTS) on a weighted star metric on N leaves.

On page migration and other relaxed task systems

This paper is concerned with the page migration (or file migration) problem (Black and Sleator, Technical Report CMU-CS-89-201, Department of Computer Science, Carnegie-Mellon University, 1989) as part of a large class of on-line problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is accessed by different processors over time. The page is allowed to be migrated between processors. However a migration incurs higher communication cost than an access (proportionally to the page size). The problem is that of deciding when and where to migrate the page in order to lower access costs. A more general setting is the k- page migration problem where we wish to maintain k copies of the page. The page migration problems are concerned with a dilemma common to many on-line problems: determining when it is beneficial to make configuration changes. We deal with the relaxed task systems model which captures a large class of problems of this type, that can be described as the generalization of some original task system problem (Borodin et al., J. ACM 39(4) (1992) 745–763). Given a c-competitive algorithm for a task system we show how to obtain a deterministic O(c 2) and randomized O(c) competitive algorithms for the corresponding relaxed task system. The result implies deterministic algorithms for k- page migration by using k-server (Manasse et al., J. Algorithms 11(2) (1990) 208–230) algorithms, and for network leasing by using generalized Steiner tree algorithms (Awerbuch et al., Proc 7th Ann. ACM-SIAM Symp. on Discrete Algorithms, January 1996, pp. 68–74), as well as providing solutions for natural generalizations of other problems (e.g. storage rearrangement (Fiat et al., Proc. 36th Ann. IEEE Symp. on Foundations of Computer Science, October 1995, pp. 392–403). We further study some special cases of the k-page migration problem and get optimal deterministic algorithms. For the classical page migration problem we present a deterministic algorithm that achieves a competitive ratio of ∼4.086, improving upon the previously best competitive ratio of 7 (Awerbuch et al., Proc. 25th ACM Symp. on Theory of Computing, May 1993, pp. 164–173). (The current lower bound on the problem is ∼3.148 (Chrobak et al., J. Algorithms 24(1) (1997) 124–157).)

On-line Learning and the Metrical Task System Problem

The problem of combining expert advice, studied extensively in the Computational Learning Theory literature, and the Metrical Task System (MTS) problem, studied extensively in the area of On-line Algorithms, contain a number of interesting similarities. In this paper we explore the relationship between these problems and show how algorithms designed for each can be used to achieve good bounds and new approaches for solving the other. Specific contributions of this paper include: • An analysis of how two recent algorithms for the MTS problem can be applied to the problem of tracking the best expert in the “decision-theoretic” setting, providing good bounds and an approach of a much different flavor from the well-known multiplicative-update algorithms. • An analysis showing how the standard randomized Weighted Majority (or Hedge) algorithm can be used for the problem of “combining on-line algorithms on-line”, giving much stronger guarantees than the results of Azar, Y., Broder, A., & Manasse, M. (1993). Proc ACM-SIAM Symposium on Discrete Algorithms (pp. 432–440) when the algorithms being combined occupy a state space of bounded diameter. • A generalization of the above, showing how (a simplified version of) Herbster and Warmuth's weight-sharing algorithm can be applied to give a “finely competitive” bound for the uniform-space Metrical Task System problem. We also give a new, simpler algorithm for tracking experts, which unfortunately does not carry over to the MTS problem. Finally, we present an experimental comparison of how these algorithms perform on a process migration problem, a problem that combines aspects of both the experts-tracking and MTS formalisms.

A Decomposition Theorem for Task Systems and Bounds for Randomized Server Problems

A lower bound of $\Omega(\sqrt{\log k / \log \log k})$ is proved for the competitive ratio of randomized algorithms for the k-server problem against an oblivious adversary. The bound holds for arbitrary metric spaces (having at least k+1 points) and provides a new lower bound for the metrical task system problem as well. This improves the previous best lower bound of $\Omega(\log \log k)$ for arbitrary metric spaces [H. J. Karloff, Y. Rabani, and Y. Ravid, SIAM J. Comput., 23 (1994), pp. 293--312] and more closely approaches the conjectured lower bound of $\Omega(\log k)$. For the server problem on k+1 equally spaced points on a line, which corresponds to a natural motion-planning problem, a lower bound of $\Omega(\frac{\log k}{\log \log k})$ is obtained. The results are deduced from a general decomposition theorem for a simpler version of both the k-server and the metrical task system problems, called the pursuit-evasion game. It is shown that if a metric space $\cal M$ can be decomposed into two spaces $\cal M_L$ and $\cal M_R$ such that the distance between them is sufficiently large compared to their diameter, then the competitive ratio for this game on $\cal M$ can be expressed nearly exactly in terms of the ratios on each of the two subspaces. This yields a divide-and-conquer approach to bounding the competitive ratio of a space.

Unfair Problems and Randomized Algorithms for Metrical Task Systems

Borodin, Linial, and Saks introduced a general model for online systems called metrical task systems (1992, J. Assoc. Comput. Mach. 39 (4), 745–763). In this paper, the unfair two state problem, a natural generalization of the two state metrical task system problem, is studied. A randomized algorithm for this problem is presented, and it is shown that this algorithm is optimal. Using the analysis of the unfair two state problem, a proof of a decomposition theorem similar to that of Blum, Karloff, Rabani, and Saks (1992, “Proc. 33rd Symposium on Foundations of Computer Science,” pp. 197–207) is presented. This theorem allows one to design divide and conquer algorithms for specific metrical task systems. Our theorem gives the same bounds asymptotically, but it has less restrictive boundary conditions.