NDLI: Multi-dimensional multiple query scheduling with distributed semantic caching framework

Content Provider	Springer Nature Link
Author	Eom, Youngmoon Kim, Jinwoong Nam, Beomseok
Copyright Year	2015
Abstract	It is becoming more important to leverage a large number of distributed cache memory seamlessly in modern large scale systems. Several previous studies showed that traditional scheduling policies often fail to exhibit high cache hit ratio and to achieve good system load balance with large scale distributed caching facilities. To maximize the system throughput, distributed caching facilities should balance the workloads and leverage cached data at the same time. In this work, we present a distributed job processing framework that yields high cache hit ratio while achieving balanced system load. Our framework employs a scheduling policy—DEMA that considers both cache hit ratio and system load and it supports geographically distributed multiple job schedulers. We show collaborative task scheduling and the data migration can even further improve the performance by increasing the cache hit ratio while achieving good load balance. Our experiments show that the proposed job scheduling policies outperform legacy load-based job scheduling policy in terms of job response time, load balancing, and cache hit ratio.
Starting Page	1141
Ending Page	1156
Page Count	16
File Format	PDF
ISSN	13867857
Journal	Cluster Computing
Volume Number	18
Issue Number	3
e-ISSN	15737543
Language	English
Publisher	Springer US
Publisher Date	2015-06-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Semantic caching Multi-dimensional query Multiple query optimization Data migration Distributed query scheduling Processor Architectures Operating Systems Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Software

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