Automated Cloud Datastore and Infrastructure Management Under SLA
type: CreativeWork
description: Modern web-based client applications, like Netflix, Youtube, Facebook, Amazon, BitTorrent, etc., use quorum-replicated datastores, like Cassandra, MongoDB, Hbase, etc., to process huge volumes of data on a cluster of commodity machines. Our research explores novel techniques to adapt the client-centric performance (i.e., performance observed from client application) of such applications according to a given usecase through automated tuning of configuration of underlying datastores. Further, to reduce capital expenditure (capex) for maintaining the hosting infrastructure, client applications are often hosted on infrastructures provided by third party cloud service providers, like Amazon, Rackspace, Microsoft, etc. Users pay the cloud service providers a cloud usage cost on the basis of the hourly usage of virtual machine instances composing the above hosting infrastructure. For minimizing cloud usage cost and optimizing client-centric performance of such applications, composition of the hosting infrastructures or configuration of underlying datastores needs to be managed according to the given usecase (or the Service Level Agreement corresponding to the usecase). Manual management of a cloud infrastructure or manual configuration of an underlying distributed datastores, considering the trade off in client-centric performance, is difficult, because of the large number of possible usecases and dynamic workload changes, which affect the client-centric performance. Moreover, state-ofthe- art cloud management tools do not consider client-centric performance metrics, like latency in the SLA (i.e., Service Level Agreement). Further, workloads in real world cloud-based web applications widely vary over time. For example, Netflix observes that the network traffic for its applications reaches almost 37% of Internet traffic during peak workload hours. State-of-the-art cloud management tools cannot adapt configurations with dynamically changing workload characteristics, like var…
abstract: Modern web-based client applications, like Netflix, Youtube, Facebook, Amazon, BitTorrent, etc., use quorum-replicated datastores, like Cassandra, MongoDB, Hbase, etc., to process huge volumes of data on a cluster of commodity machines. Our research explores novel techniques to adapt the client-centric performance (i.e., performance observed from client application) of such applications according to a given usecase through automated tuning of configuration of underlying datastores. Further, to reduce capital expenditure (capex) for maintaining the hosting infrastructure, client applications are often hosted on infrastructures provided by third party cloud service providers, like Amazon, Rackspace, Microsoft, etc. Users pay the cloud service providers a cloud usage cost on the basis of the hourly usage of virtual machine instances composing the above hosting infrastructure. For minimizing cloud usage cost and optimizing client-centric performance of such applications, composition of the hosting infrastructures or configuration of underlying datastores needs to be managed according to the given usecase (or the Service Level Agreement corresponding to the usecase). Manual management of a cloud infrastructure or manual configuration of an underlying distributed datastores, considering the trade off in client-centric performance, is difficult, because of the large number of possible usecases and dynamic workload changes, which affect the client-centric performance. Moreover, state-ofthe- art cloud management tools do not consider client-centric performance metrics, like latency in the SLA (i.e., Service Level Agreement). Further, workloads in real world cloud-based web applications widely vary over time. For example, Netflix observes that the network traffic for its applications reaches almost 37% of Internet traffic during peak workload hours. State-of-the-art cloud management tools cannot adapt configurations with dynamically changing workload characteristics, like var…
author: Sidhanta, Subhajit
keywords: cloud computing, cloud provisioning, distributed system, automated tuning
date published: 2016-01-01T08:00:00Z
date modified: 2026-07-24T02:58:17Z
provider: Lousiana State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Computer Science

PropertyValue
property ID: DOI
value: 10.31390/gradschool_dissertations.707