Machine Learning and Grid Optimization Noah Edelson Distributed

Machine Learning and Grid Optimization Noah Edelson Distributed Systems Department 9/17/2004 DSD Department Meeting

Motivation • Computation is important to science. • Search is one of the most important problems in computation. • Uninformed “blind” search is the most general search technique. • Uninformed searches often perform better than uninformed programmers. 9/17/2004 DSD Department Meeting

The Black Art of Fussing with Parameters • • • > Rather than cranking the buffers all the way up, it may be better to > use multiple streams to fill the pipe. It may not possible to saturate > a a fat pipe with just one connection for the above reasons, but also > because of traffic shapers and various other limits. You can always > try of course : -) For parallel connections use some reasonable > estimate, e. g. divide the maximum "pipe width" (bandwith * rtt) by what > you seem to be able to get out from a single stream, give or take a few > more streams. It would probably help to be multi-threaded if using > multiple streams (to avoid txqueuelen exhaustion). Alternatively, > simply copying several files in parallel achieves the same. 9/17/2004 DSD Department Meeting

Blind Search • Queue <- Make. Queue(Initial-State) • while( Not Empty(Queue) ){ Node <- Remove-Front(Queue) if( Goal-Test(Node) ) return Solution Queue <- Queuing-FN( Expand(Node), Queue) } return Failure 9/17/2004 DSD Department Meeting

Search Taxonomy 9/17/2004 DSD Department Meeting

Evolutionary Computation. Selection Parents Recombination (crossover) Population Mutation Replacement Offspring Borrowed from Alba & Troya - Improving flexibility and efficiency by adding parallelism to genetic algorithms, Statistics and Computing 12 9/17/2004 DSD Department Meeting

Genetic Algorithms t reproduction selection mutation recombination Borrowed from Alba & Troya - Improving flexibility and efficiency by adding parallelism to genetic algorithms, 9/17/2004 Statistics and Computing 12 DSD Department Meeting t+1

“Combinatorial Explosion” • A search, by definition, picks 1 of N solutions from the solution space. • Given a bitstring with cardinality |N|, there are 2^|N| possible states to search. • A well known AI professor at Cal claims that GA ignore the “combinatoral explosion” of the search space. He is wrong. 9/17/2004 DSD Department Meeting

The Black Art of Fitness Functions • A “good” fitness function converges quickly. • • Minimize |genes| (degrees of freedom) Avoid “Deceptive Trap Functions” Encourage “smooth” fitness functions Decouple fitness dependence between genes -- What happens when your fitness function halts? 9/17/2004 DSD Department Meeting

A Solution to the Entscheidungsproblem!!! • When all else fails- give up! • A solution whose evaluation halts is decidedly unfit. • Boils down to “throwing out the outliers” 9/17/2004 DSD Department Meeting

Parameter Tuning by way of Search (2 steps!) • An optimizer simply performs a “search for optimal performance. ” • Step 1) Encode your input parameters • Step 2) Invent a fitness metric. I used Execution. Time^-1 9/17/2004 DSD Department Meeting

Example Fitness Function def test. Func(data, args=''): if not data: return 3, 1. 0, 0. 0 p, b, t = int(1+data[0]*10. 0) , 8*1024+int(data[1]*255. 0))*1024, 8*1024+int(data[2]*255. 0)*1024) start. Time = time() for samples in range(num_samples): lazy_forker(‘globus-url-copy’ + ' -p ' + str(p)' -bs ' +str(b) + ' -tcp-bs’ + str(t) + ' ' + src + ' ' + dst, thresh. Hold) end. Time = time() - start. Time return 1. 0/end. Time 9/17/2004 DSD Department Meeting

Step 3? • Step 3) Grin in satisfaction, as your GA will explore multiple dimensions of your solution-space at once. 9/17/2004 DSD Department Meeting

Results Globus-url-copy py. Globus-url-copy (2 x avg) (4 x avg) Performance Boost 64 35% 19% 128 22 15 192 08 00 256 03 06 mb 9/17/2004 DSD Department Meeting

Results • Globus-url-copy and py. Globus-url-copy are similar in terms of performance given input parameters and running environment. • Parameter tuning affords both utilities a noticeable increase in performance given little knowledge of the problem domain. 9/17/2004 DSD Department Meeting

Why an optimizer service? . Optimizer *Tune. Service(FF) *Save/Load Tunings[Env] Fitness. Function(x, y, z) Grid. Service. A(x, y, z) 9/17/2004 *Test. Env Fitness. Function(x, y, z) Grid. Service. B(w, q, r) DSD Department Meeting

Too Much Work? • Writing an application-tuning fitness function is similar to wrapping or service-ifying the application. • If you wrap a well-used app whose performance is sensitive to the parameters you support, you might as well provide a mechanism for tuning those parameters for higher performance. 9/17/2004 DSD Department Meeting

Future Work Service-ify the GA - What standard to use? Production runs on WAN? - Planet. Lab, EMULab? What besides py. Globus-url-copy and globus-urlcopy? - Grid Schedulers? - RLS Strategies? - Parameter tuning for important applications & services? 9/17/2004 DSD Department Meeting

Thanks! Have a good weekend! 9/17/2004 DSD Department Meeting