Amortized efficiency of list update and paging rules
Communications of the ACM
Competitive algorithms for on-line problems
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Competitive algorithms for server problems
Journal of Algorithms
New results on server problems
SIAM Journal on Discrete Mathematics
Online computation and competitive analysis
Online computation and competitive analysis
Best-fit bin-packing with random order
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
The relative worst order ratio for online algorithms
ACM Transactions on Algorithms (TALG)
The relative worst-order ratio applied to paging
Journal of Computer and System Sciences
On the separation and equivalence of paging strategies
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Randomized Competitive Analysis for Two-Server Problems
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
List factoring and relative worst order analysis
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
On the absolute approximation ratio for First Fit and related results
Discrete Applied Mathematics
A comparison of performance measures via online search
FAW-AAIM'12 Proceedings of the 6th international Frontiers in Algorithmics, and Proceedings of the 8th international conference on Algorithmic Aspects in Information and Management
Relative interval analysis of paging algorithms on access graphs
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
The frequent items problem in online streaming under various performance measures
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
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This paper provides a systematic study of several proposed measures for online algorithms in the context of a specific problem, namely, the two server problem on three colinear points. Even though the problem is simple, it encapsulates a core challenge in online algorithms which is to balance greediness and adaptability. We examine Competitive Analysis, the Max/Max Ratio, the Random Order Ratio, Bijective Analysis and Relative Worst Order Analysis, and determine how these measures compare the Greedy Algorithm and Lazy Double Coverage, commonly studied algorithms in the context of server problems. We find that by the Max/Max Ratio and Bijective Analysis, Greedy is the better algorithm. Under the other measures, Lazy Double Coverage is better, though Relative Worst Order Analysis indicates that Greedy is sometimes better. Our results also provide the first proof of optimality of an algorithm under Relative Worst Order Analysis.