Summary: Small Cache, Big Effect: Provable Load Balancing for
Randomly Partitioned Cluster Services
Bin Fan, Hyeontaek Lim, David G. Andersen, Michael Kaminsky
Carnegie Mellon University,
Load balancing requests across a cluster of back-end servers is
critical for avoiding performance bottlenecks and meeting service-
level objectives (SLOs) in large-scale cloud computing services.
This paper shows how a small, fast popularity-based front-end cache
can ensure load balancing for an important class of such services;
furthermore, we prove an O(n log n) lower-bound on the necessary
cache size and show that this size depends only on the total number
of back-end nodes n, not the number of items stored in the system.
We validate our analysis through simulation and empirical results
running a key-value storage system on an 85-node cluster.
CATEGORIES AND SUBJECT DESCRIPTORS
D.4.2 [Operating Systems]: Storage Management; D.4.7
[Operating Systems]: Organization and Design; D.4.8 [Operating