Experiences With Scheduling and Mapping
Games for Adaptive Distributed Systems
Bin Lin, Peter Dinda
Department of EECS
Northwestern University
{binlin365, pdinda}@gmail.com
empathicsystems.org
Game Interface
Is it possible to map a scheduling and mapping problem in distributed and parallel
systems to a game that a naïve user can play, with the side effect having of good game
play correspond to a good solution to the problem? YES!
Can naive users play such a game well? YES!
Our technique: Human-driven Search
A game for naïve users in which game-play corresponds to solving a well
posed, but difficult to solve optimization problem in adaptive virtualized
computing
The user is trying to migrate the ball (VM) in the left-most resource box (host) to the second resource box (host)
to the left. The position in the box (host) corresponds to a periodic real-time schedule for the ball (VM)
Problem: Maximize the performance of a BSP program running in a collection of VMs
through VM migration and selection of periodic real-time schedules
A physical host – a resource box; A VM – a ball
VM efficiency - happiness of a ball: (% of available compute time being used)
Objective function f(x) - score: global cumulative happiness of all of the balls,
assuming all the balls are working together to make progress towards a global goal (parallel
efficiency).
Goal of the game: to achieve the highest possible score.
How to play: play by dragging balls within boxes (VM schedule change), or
between boxes (VM to host mapping). As the user drags a ball, the game highlights and
enforces the scheduling and capacity constraints on where the user may place it.
Final screen: global cumulative happiness (f(x)) and its time average
User Study
21 users with various backgrounds
2 warm-up tasks and 9 formal tasks
Percentage of users who find the optimal mapping; 95% confidence interval.
Details can be found in Lin’s Dissertation (NWU-EECS-07-04), available from our web site.
Duration to the optimal mapping
Conclusions
Considerable variation in user performance
as expected in any game.
In almost all scales and types of tasks considered,
there are users who perform near-optimally
(compared with optimal solutions either by
construction or by simulation-based search)
Most users are able to find optimal mappings.
In the worst case task, more than 65%
of users were able to find the optimal VM mapping.
As task difficulty and problem size grow,
the average time to find the optimal mapping grows.
However, users were able to find an optimal
mapping in 2–3 minutes.
The Empathic Systems Project (empathicsystems.org) is funded by NSF CNS-0720691