Amortized efficiency of list update and paging rules
Communications of the ACM
Competitive paging with locality of reference
Selected papers of the 23rd annual ACM symposium on Theory of computing
Approximating total flow time on parallel machines
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Synthesis of a Feedback Queueing Discipline for Computer Operation
Journal of the ACM (JACM)
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
A new average case analysis for completion time scheduling
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Smoothed analysis of the perceptron algorithm for linear programming
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Random knapsack in expected polynomial time
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Minimizing flow time nonclairvoyantly
Journal of the ACM (JACM)
Average Case and Smoothed Competitive Analysis of the Multi-Level Feedback Algorithm
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Computing equilibria for congestion games with (im)perfect information
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Typical properties of winners and losers in discrete optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines
Journal of the ACM (JACM)
Smoothed analysis of binary search trees
Theoretical Computer Science
Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Smoothed analysis: an attempt to explain the behavior of algorithms in practice
Communications of the ACM - A View of Parallel Computing
On an Online Traveling Repairman Problem with Flowtimes: Worst-Case and Average-Case Analysis
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Average-case competitive analyses for one-way trading
Journal of Combinatorial Optimization
On robust online scheduling algorithms
Journal of Scheduling
Smoothed Analysis of the k-Means Method
Journal of the ACM (JACM)
Smoothed performance guarantees for local search
ESA'11 Proceedings of the 19th European conference on Algorithms
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In this paper, we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng [25] to explain the behavior of algorithms that work well in practice while performing very poorly from a worst-case analysis point of view. We apply this notion to analyze the multilevel feedback algorithm (MLF) to minimize the total flow time on a sequence of jobs released over time when the processing time of a job is only known at time of completion. The initial processing times are integers in the range [1, 2K]. We use a partial bit randomization model, i.e., the initial processing times are smoothed by changing the k least significant bits under a quite general class of probability distributions. We show that MLF admits a smoothed competitive ratio of O((2k/)3 (2k/)2 2K-k), where denotes the standard deviation of the distribution. In particular, we obtain a competitive ratio of O(2K-k) if (2k). We also prove an (2K-k) lower bound for any deterministic algorithm that is run on processing times smoothed according to the partial bit randomization model. For various other smoothing models, including the additive symmetric smoothing one, which is a variant of the model used by Spielman and Teng [25], we give a higher lower bound of (2K). A direct consequence of our result is also the first average-case analysis of MLF. We show a constant expected ratio of the total flow time of MLF to the optimum under several distributions including the uniform one.