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Min-Max Graph Partitioning. The Problem. k. 1. Motivation. Cloud Computing. n communicating processes. Bandwidth B. machines. Previous Work. A Related Problem. A Related Problem. Our Results. Our Results. Good Partition: Disjoint Cover with “good” sets (SSE’s). Our Results.
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. . . k 1 Motivation Cloud Computing n communicating processes Bandwidth B machines
Our Results Good Partition: Disjoint Cover with “good” sets (SSE’s)
Our Results Good Partition: Disjoint Cover with “good” sets (SSE’s) LP: Each vertex covered to extent 1
. . . k 1 Our Results Machines
Outline • Introduction • Graph Partitioning (quick intro) • Our Approach • Coverings to Partition
Graph Partitioning Approaches 0 1 (all vertices sit here)
Graph Partitioning Approaches 0 1 (all vertices sit here)
. . . k 1 Breaks down for min-max 0 Any k-partitioning is bad: Part containing vertex 0 LP/SDP can always cheat (by smearing vertex 0) (even with triangle inequalities, all kinds of separating constraints)
Outline • Introduction • Graph Partitioning (quick intro) • Our Approach • Coverings to Partition