Miquel Angel Senar Unitat d’Arquitectura de Computadors i Sistemes Operatius Universitat Autònoma de Barcelona Self-Adjusting Scheduling of Master-Worker Applications on Opportunistic Environments

Self-Adjusting Scheduling of Master-Worker Applications Problem Backgroung › Parallel Application that follows the master-worker model › A master process is assigned several batches of tasks › The master process allocates worker processes to solve each batch › Batches are solved iteratively

Self-Adjusting Scheduling of Master-Worker Applications A simple example: an image thinning application Original 10 It. Latter 36 It. Latter

Self-Adjusting Scheduling of Master-Worker Applications Master Process Divides Image Workers Compute Concurrently Master Aggregates Image Image Thinning as a Master-Worker Application!

Self-Adjusting Scheduling of Master-Worker Applications Running the application with MW + Condor-PVM master tasks worker results worker PVM

Self-Adjusting Scheduling of Master-Worker Applications Challenges of master-worker applications › Task scheduling › Number of workers › Dealing with heterogeneous processor › Impact of preemption

Self-Adjusting Scheduling of Master-Worker Applications Our Self-Adjusting Strategy › Dynamically measures application performance and task execution times › Predicts the resource requirements from measured history › Schedules tasks on the resources according to that prediction in order to minimize the completion time of the application › Voluntary relinquishes resources when they are not plentifully utilized › Allocates more resources whenever a significant loss in speedup is detected.

Self-Adjusting Scheduling of Master-Worker Applications Influence of Task Scheduling

Self-Adjusting Scheduling of Master-Worker Applications How does our strategy work (1)? › Collects task execution times at each iteration › Sorts tasks according to their average execution time (Prediction) › At each iteration, tasks are scheduled according following the order of that list

Self-Adjusting Scheduling of Master-Worker Applications Influence of the Number of Workers

Self-Adjusting Scheduling of Master-Worker Applications Influence of the Number of Workers

Self-Adjusting Scheduling of Master-Worker Applications How does our strategy work (2)?  Initially, allocates 1 Worker per Task  Reduces the number of workers to  Allocates 1 more Worker if (ExecutionTime > (Largest Task + Threshold))  Releases 1 Worker if (Efficiency < 0.8)

Self-Adjusting Scheduling of Master-Worker Applications Sample Result: No. of Workers  Self-Adjusting  Non Self-Adjusting

Self-Adjusting Scheduling of Master-Worker Applications  Self-Adjusting  Non Self-Adjusting Sample Result: Efficiency

Self-Adjusting Scheduling of Master-Worker Applications Sample Result: Execution Time  Self-Adjusting  Non Self-Adjusting

Self-Adjusting Scheduling of Master-Worker Applications Dealing with heterogeneity › Problem: wall clock time reflects application code and resource performance › Master and Worker machines have a performance normalization factor  (i). (Benchmarking) › Task scheduling decisions are based on normalized task execution times (user time multiplied by  ’s) › Worker allocation decisions are based on wall clock time measured at the master process

Self-Adjusting Scheduling of Master-Worker Applications Dealing with heterogeneity  Self-Adjusting  Non Self-Adjusting

Self-Adjusting Scheduling of Master-Worker Applications Dealing with Preemption We expect this This is what happens And this is what we get

Self-Adjusting Scheduling of Master-Worker Applications Dealing with Preemption › SOLUTION (still working on it): Using extra machines › Complete Replication (extra machines running a copy of one of the largest tasks) usually performs better than No Replication › Every extra machines has a negative impact on overall efficiency (between 3% and 8%) › CR with 2 extra machines exhibits a good trade-off if one machine is lost at every iteration.

Self-Adjusting Scheduling of Master-Worker Applications And coming soon… delays

Miquel Angel Senar Unitat d’Arquitectura de Computadors i Sistemes Operatius Universitat Autònoma de Barcelona Self-Adjusting Scheduling of Master-Worker Applications on Opportunistic Environments