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Handling Large Datasets in Parallel Metaheuristics: A ... - Google Sites
solution space. â« Solution traces to reduce searching. â« Keeping track of past searches. â« Keeping track of past s
Handling Large Datasets in Parallel Metaheuristics: A Spares Management p Case Study y and Optimization
Chee Shin Yeo, Elaine Wong Kay Li, Yong Siang Foo
Metaheuristics
2
Solve optimization problems in diverse domains Search over a solution space for an optimal solution that will minimise an objective function Challenges Exponentially increasing execution time Memory intensive Inconsistent performance due to random generation
Parallel Metaheuristics
3
Search in parallel using multiple searches over a solution space Solution traces to reduce searching Keeping track of past searches Cooperative methods with different initial solutions Parallel searches exchange intermediate results Problem: Large datasets Insufficient Memory Network bottlenecks
Parallel Metaheuristics: Flow Control Workflow
4
Run as a flow control workflow with n states, states each state with x Multiple Independent Runs (MIR)
Optimal solution is the output On of the last state (Sn)
Flow Control Workflow: Clustering Policy Workflow Clustering Policy: Executes the entire workflow as a single job
S1 MIR1a MIR1b
Processor P2 P1
Td
S2 MIR2a MIR2b
S3 MIR3a MIR3b
entire workflow
Ta
Time
State Clustering Policy: Executes each state of the entire workflow as a single g jjob
S1 MIR1a MIR1b
Processor P2 P1
Td
S1
S2 MIR2a MIR2b
Ta Td
S2
S3 MIR3a MIR3b
Ta Td
S3
Ta
Time
Job Clustering Policy: Executes each MIR in a state as a single job
S1 MIR1a MIR1b
5
Processor P2 P1
S2 MIR2a MIR2b
S3 MIR3a MIR3b
Td MIR1a Ta Td MIR2a Ta Td MIR3a Ta Td MIR1b Ta Td MIR2b Ta Td MIR3b Ta
Time
Case Study: Spares Management and Optimization (SMO)
6
Optimization scenario Aircraft spare parts 59 airports – with time time-based based delivery commitment at selective airports Logistics flights between all locations Flight network – ~320,000 Flight Hours/year
Experimental Setup
7
Effect of Stop Criterion on IBM (2 (2.26GHz, 26GHz 32GB)
8
Effect of Clustering Policy on DELL (3 (3.0GHz, 0GHz 4GB)
9
Conclusion
10
Flow control workflow for p parallel metaheuristics Stop Criterion: Exchange of intermediate data Clustering g Policy: y Assignment g of jjobs Memory y availability y is a critical issue Less iterations for stop criterion is better Less memory, shorter completion time Job clustering policy is better Least memory, memory more reliable completion
Future Work
11
More intelligent g optimization p Self-configuring stop criterion Resource contention in multi-user environment Effective scheduling g mechanism
End of Presentation Thank You Any Questions/Comments?
