A multi-resource load balancing algorithm for cloud cache systems

Jia, Yu and Brondino, Ivan and Jiménez-Peris, Ricardo and Patiño-Martínez, M. and Ma, Dianfu (2013). A multi-resource load balancing algorithm for cloud cache systems. In: "28th Symposium on Applied Computing", 18-22 Mar 2014, Coimbra, Portugal. ISBN 978-1-4503-1656-9.


Title: A multi-resource load balancing algorithm for cloud cache systems
  • Jia, Yu
  • Brondino, Ivan
  • Jiménez-Peris, Ricardo
  • Patiño-Martínez, M.
  • Ma, Dianfu
Item Type: Presentation at Congress or Conference (Article)
Event Title: 28th Symposium on Applied Computing
Event Dates: 18-22 Mar 2014
Event Location: Coimbra, Portugal
Title of Book: SAC'13: proceedings of the 28th Annual ACM Symposium on Applied Computing
Date: 2013
ISBN: 978-1-4503-1656-9
Volume: 1
Freetext Keywords: Distributed systems - Quality assurance - Minimax approximation and algorithms
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost.

More information

Item ID: 26476
DC Identifier: https://oa.upm.es/26476/
OAI Identifier: oai:oa.upm.es:26476
Official URL: http://dl.acm.org/citation.cfm?id=2480453
Deposited by: Memoria Investigacion
Deposited on: 05 Jun 2014 13:18
Last Modified: 30 Nov 2017 09:49
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