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Small Cache, Big Effect: Provable Load Balancing for Randomly Partitioned Cluster Services
 

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,
Intel Labs
{binfan,hl,dga}@cs.cmu.edu, michael.e.kaminsky@intel.com
ABSTRACT
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

  

Source: Andersen, Dave - School of Computer Science, Carnegie Mellon University
Carnegie Mellon University, School of Computer Science, Informedia Project
Lu, Ying - Department of Computer Science and Engineering, University of Nebraska-Lincoln

 

Collections: Computer Technologies and Information Sciences