Evaluating job packing in warehouse-scale computing
Metadata
Presented in CLUSTER 2014.
Authors: Abhishek Verma, Madhukar Korupolu, John Wilkes (Google).
Understanding the paper
The paper compares four metrics for evaluating the packing efficiency of schedulers.
Aggregate utilization
Metric: the allocation rate of each type (e.g., CPU, RAM).
Adv.
Simple, most commonly used.
Disadv.
Cannot distinguish between schedulers that place all tasks.
Hide fragmentation effects.
Hide stranding resources.
Hole filling
Method: count how many appropriately-sized units of size U can fit into the holes.
Adv.
Fast and simple.
Disadv.
Ignore constraints.
Ignore heterogeneity of workloads.
Workload inflation
Method: scale up the original workload until it no longer fits.
An improved version of hole filling (consider the heterogeneity).
Perspective of workloads.
Details
Horizontal scaling.
Vertical scaling.
Monte-Carlo technique.
Adv.
Answer "what if?" questions about future workload growth.
Disadv.
Multiple policy choices.
Cluster compaction
Method: shrink the cluster until the workload no longer fits.
Perspective of machines.
Evaluation methodology in Borg.
Steps
Generate a random permutation of machines.
Binary search to determine the minimum machines to run the workload.
Repeated trials to obtain a distribution of the compacted cluster sizes.
Adv.
Directly answer "how small a cluster could be used to run this workload?".
Fewer policy choices.
Disadv.
Longer running time.
| Aggregate utilization | Hole filling | Workload inflation | Cluster compaction | |
|---|---|---|---|---|
Accuracy | low | medium | high | high |
Fragmentation/stranding | no | yes | yes | yes |
Attributes/constraints | no | no | yes | yes |
Time for computation | < 1 min | ~ 30 mins | ~ 2 hours | ~ 5 hours |
Context where useful | quick approximation | fixed-size slot counts | cluster operators | capacity planners |
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