Loading…

Dynamic placement of resources in cloud computing and network applications

We address the problem of dynamic resource placement in general networking and cloud computing applications. We consider a large-scale system faced by time varying and regionally distributed demands for various resources. The system operator aims at placing the resources across regions to maximize r...

Full description

Saved in:
Bibliographic Details
Published in:Performance evaluation 2017-10, Vol.115, p.1-37
Main Authors: Rochman, Yuval, Levy, Hanoch, Brosh, Eli
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We address the problem of dynamic resource placement in general networking and cloud computing applications. We consider a large-scale system faced by time varying and regionally distributed demands for various resources. The system operator aims at placing the resources across regions to maximize revenues, and thus needs to address the problem of how to dynamically reposition the resources in reaction to the time varying demand. The challenge posed by this setting is to deal with arbitrary multi-dimensional stochastic demands which vary over time. Under such settings one should provide a tradeoff between optimizing the resource placement as to meet its demand, and minimizing the number of added and removed resources to the placement. Our analysis and simulations reveal that optimizing the resource placement may inflict huge resource repositioning costs, even if the demand has small fluctuations. We therefore propose an algorithmic framework that overcomes this difficulty and yields very efficient dynamic placements with bounded repositioning costs. Our solution is developed under a very wide cost model, and thus allows accommodation of many systems. Our solutions are based on new analytic techniques utilizing graph theory methodologies that can be applied to other optimization/combinatorial problems.
ISSN:0166-5316
1872-745X
DOI:10.1016/j.peva.2017.06.003