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John Noga

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers
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Possible papers

8

TCS Journal 2009 Journal Article

Optimally competitive list batching

  • Wolfgang Bein
  • Leah Epstein
  • Lawrence L. Larmore
  • John Noga

Batching has been studied extensively in the offline case, but applications such as manufacturing or TCP acknowledgment often require online solutions. We consider online batching problems, where the order of jobs to be batched is fixed and where we seek to minimize the sum of the completion times of the jobs. We present optimally competitive online algorithms for both s -batch and p -batch problems, and we also derive results for certain naturally occurring special cases, such as the case of unit processing times.

TCS Journal 2003 Journal Article

More on randomized on-line algorithms for caching

  • Marek Chrobak
  • Elias Koutsoupias
  • John Noga

We address the tradeoff between the competitive ratio and the resources used by randomized on-line algorithms for caching. Two algorithms reported in the literature that achieve the optimal ratio H k require a lot of memory and perform extensive computation at each step. On the other hand, a very simple algorithm called RMARK has competitive ratio 2H k −1, within a factor of 2 of the optimum. A natural question that arises here is whether there is a tradeoff between simplicity and the competitive ratio. In particular, is it possible to achieve a competitive ratio better than 2H k −1 with a simple algorithm like RMARK? We first consider marking algorithms that are natural generalizations of RMARK, and we prove that, for any ε>0, there is no randomized marking algorithm for caching with competitive ratio (2−ε)H k. Thus RMARK is essentially optimal among marking algorithms. Another model of simple caching algorithms is that of trackless algorithms. These are algorithms that do not store any information about items that are not in the cache. It is known that, for k=2, there is no randomized trackless algorithm for caching with ratio better than 37 24 ≈1. 5416. The trivial upper bound is 2, achieved even by deterministic algorithms LRU and FIFO. We reduce this gap by giving a trackless randomized algorithm with competitive ratio 1 4 (3+ 13 )≈1. 6514.

TCS Journal 2002 Journal Article

Solution of a problem in DNA computing

  • Eric Anderson
  • Marek Chrobak
  • John Noga
  • Jiřı́ Sgall
  • Gerhard J. Woeginger

We answer a question of Rozenberg and Salomaa arising from a problem in DNA computing. This problem was posed at the ICALP conference in July 1999 in Prague.

TCS Journal 2001 Journal Article

An optimal online algorithm for scheduling two machines with release times

  • John Noga
  • Steven S. Seiden

We present a deterministic online algorithm for scheduling two parallel machines when jobs arrive over time and show that it is 1 2 (5− 5 )≈1. 38198-competitive. The best previously known algorithm is (3/2)-competitive. Our upper bound matches a previously known lower bound, and thus our algorithm has the best possible competitive ratio. We also present a lower bound of 1. 21207 on the competitive ratio of any randomized online algorithm for any number of machines. This improves a previous result of 4−2 2 ≈1. 17157.

TCS Journal 2000 Journal Article

Competitive analysis of randomized paging algorithms

  • Dimitris Achlioptas
  • Marek Chrobak
  • John Noga

The paging problem is defined as follows: we are given a two-level memory system, in which one level is a fast memory, called cache, capable of holding k items, and the second level is an unbounded but slow memory. At each given time step, a request to an item is issued. Given a request to an item p, a miss occurs if p is not present in the fast memory. In response to a miss, we need to choose an item q in the cache and replace it by p. The choice of q needs to be made on-line, without the knowledge of future requests. The objective is to design a replacement strategy with a small number of misses. In this paper we use competitive analysis to study the performance of randomized on-line paging algorithms. Our goal is to show how the concept of work functions, used previously mostly for the analysis of deterministic algorithms, can also be applied, in a systematic fashion, to the randomized case. We present two results: we first show that the competitive ratio of the marking algorithm is exactly 2Hk−1. Previously, it was known to be between Hk and 2Hk. Then we provide a new, Hk -competitive algorithm for paging. Our algorithm, as well as its analysis, is simpler than the known algorithm by McGeoch and Sleator. Another advantage of our algorithm is that it can be implemented with complexity bounds independent of the number of past requests: O(k2 log k) memory and O(k2) time per request.

v2026.09.13