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  2. Master Thesis / Tesis Master
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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476440
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dc.contributor.advisorRavie Chandren Muniyandi, Dr.
dc.contributor.authorAhmed Salih Mahdi (P74217)
dc.date.accessioned2023-10-06T09:18:30Z-
dc.date.available2023-10-06T09:18:30Z-
dc.date.issued2016-07-18
dc.identifier.otherukmvital:85958
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/476440-
dc.descriptionInfrastructure as a service (IaaS) is one of the most established cloud services. It pro-vides virtual machines (VMs) with high flexibility. One of the challenges is how to manage a huge amount of VM images effectively. Cloud input-output (IO) perfor-mance will affect VMs. On the other hand, lots of storage resources are consumed and need higher management cost. The current optimization is well done by two ways, either improving the performance or decreasing image size, but the low storage con-sumption and high IO performance cannot be satisfied at the same time. Zone-based method balances these requirements. In this research, computing nodes are partitioned into many zones, and construct a shared storage in each zone for hot data in order to achieve high IO performance and low storage consumption. The proposed method consists of the use of Adaptive Replacement Cache (ARC) and probabilistic content placement (PROB) algorithms which is called Zone Based–Adaptive Replacement Cache and Probabilistic content placement (ZB-ARCPROB). The proposed method provides more support to the cache management of images with considering all re-quirements to achieve high IO performance and low storage consumption. We evalu-ated the ZB-ARCPROB method, to measure the QoS (Quality of Service) of Cloud performance, using Network Simulator version 2.35 (NS2). The performance of the proposed method was compared with Zone-Base method. The comparison was evalu-ated in terms of three metrics get most attentions from industry and academia includ-ing IO latency, IO throughput, and relative storage consumption. The comparison re-sults indicate that the proposed ZB-ARCPROB outperforms the Zone-Base method.,Certification of Master's/Doctoral Thesis" is not available
dc.language.isoeng
dc.publisherUKM, Bangi
dc.relationFaculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rightsUKM
dc.subjectCloud services
dc.subjectVirtual machines
dc.subjectAlgorithms
dc.subjectAdaptive replacement
dc.subjectProbabilistic content
dc.subjectDissertations, Academic -- Malaysia
dc.titleCloud performance and storage consumption enhancement using adaptive replacement and probabilistic content placement algorithms
dc.typetheses
dc.format.pages94
dc.identifier.barcode002656(2017)
Appears in Collections:Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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