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Alternative Models for Online Analysis Alex López-Ortiz University of Waterloo joint work with Reza Dorrigiv, Spyros Angelopoulos and Ian Munro

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Competitive analysis drawbacks Sometimes too pessimistic Focus on worst case at the expense of every-day case Uses off-line optimum concept Perhaps one of the reasons for the theoretical vs practical motion planning divide?

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Paging: A case study One of the earliest problems to be studied under the online model Competitive analysis not always satisfactory, too pessimistic A good starting point for considering alternatives “Real life” inputs well understood and characterized (temporal + spatial locality)

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Paging Algorithms Data brought from slower memory into cache RAM CPU

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Paging Algorithms Data brought from slower memory into cache Unit of transfer: pages of equal size Paging algorithm ≡ Eviction policy Commonly studied under competitive ratio framework All lazy marking algorithms are “optimal”

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Theory 1.Commonly studied under competitive ratio framework 2.Worst case analysis 3.Marking algorithms optimal 4.In practice LRU is best 5.LFD is offline optimal 6.Competitive ratio is k 7.User is malicious adversary Systems 1.Commonly studied using fault rate measure 2.Typical case analysis 3.LRU and friends is best 4.LRU is impractical 5.Huh? 6.Competitive ratio is 2 or 3 7.User is your friend

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Online motion planning 1.Commonly studied under competitive ratio framework 2.Worst case analysis 3.Continuous curved motions 4.Perfect scans 5.Flawless detection 6.No error in motion 7.Architects are your enemy Robotics 1.Commonly studied using distance & scan cost 2.Typical case analysis 3.Piecewise linear 4.Scanning error 5.High detection error 6.Forward & rotational lag 7.Architects are your friend

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“Architects are your friend” Most of the time, anyhow.

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Fix the Theory-Practice disconnect 1.Make both columns match How? Fix reality or Fix the model A more realistic theoretical model is likely to lead to practical insights

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Previous work (paging case) 1.Disconnect has been noted before. 2.Has been subject of intense study, viz. 1.Borodin et al. 2.Karlin et al. 3.Koutsoupias and Papadimitriou 4.Sleator and Tarjan 5.Denning 6.Young 7.Albers et al. 8.Boyar et al. + many others

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Theory 1.Commonly studied under competitive ratio framework 2.Worst case analysis 3.Marking algorithms optimal 4.In practice LRU is best 5.LFD is offline optimal 6.Competitive ratio is k 7.User is malicious adversary Systems 1.Commonly studied using fault rate measure 2.Typical case analysis 3.LRU and friends is best 4.LRU is impractical 5.Huh? 6.Competitive ratio is 2 or 3 7.User is your friend

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Results 1.competitive ratio framework/fault rate framework –new model which incorporates fault rate considerations (concave analysis) 2.worst case analysis/typical case analysis –model focuses on “every-case” analysis, not just worst case (bijective analysis)

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Results marking algorithms optimal/LRU and friends is best –LRU is unique optimum under new model in practice LRU is best/LRU is impractical –initiated study of computationally limited caching strategies

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Results LFD is offline optimal/huh? –removed concept of offline optimum competitive ratio is k/competitive ratio is 2 or 3 –Nothing to do!

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Results user is malicious adversary/user is your friend –incorporated assumption of locality of reference in our analysis Applies to other online problems e.g. list access This leads to a new notion called cooperative ratio

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Cooperative ratio Agreement between user and algorithm about inputs which are: –likely –common –good –important

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Cooperative ratio Badly written code (not cache conscious) –(Rightly) considered the programmer’s fault –Paging strategy not responsible for predicting non-standard paging behaviour Well written code (cache conscious) –Code can rely on well defined paging behaviour to produce better code (e.g. I/O model, cache oblivious model)

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Cooperative ratio for motion planning Robot must search efficiently scenes which are “reasonable” Can perform somewhat worse in “unreasonable” scenes Leads to adaptive-style analysis. E.g. define niceness measure of office floor plan in terms of orthogonality of scene, number of rooms/corridors, size of smallest relevant feature, etc.

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Cooperative ratio for motion planning Look around the corner Leads to “straighter” curve Applicable to polygon recognition (work in progress)

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Conclusions Improved model for paging Bridged theory-practice disconnect Next talk: Unique optimality of LRU under new, more realistic model New cooperative analysis model applicable to online research

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