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                <forename type="first">Varun</forename>
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                  <title>14th International Middleware Conference (Middleware)</title>
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                <editor>David Hutchison</editor>
                <editor>Takeo Kanade</editor>
                <editor>Madhu Sudan</editor>
                <editor>Demetri Terzopoulos</editor>
                <editor>Doug Tygar</editor>
                <editor>Moshe Y. Vardi</editor>
                <editor>Gerhard Weikum</editor>
                <editor>David Eyers</editor>
                <editor>Karsten Schwan</editor>
                <editor>Josef Kittler</editor>
                <editor>Jon M. Kleinberg</editor>
                <editor>Friedemann Mattern</editor>
                <editor>John C. Mitchell</editor>
                <editor>Moni Naor</editor>
                <editor>Oscar Nierstrasz</editor>
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                <term xml:lang="en">Backup</term>
                <term xml:lang="en">Read Probability Weight</term>
                <term xml:lang="en">Data Storage</term>
                <term xml:lang="en">Magnetic Tapes</term>
                <term xml:lang="en">Archiving</term>
                <term xml:lang="en">Cloud</term>
                <term xml:lang="en">Data Centers</term>
                <term xml:lang="en">Priority Queue</term>
                <term xml:lang="en">Middleware</term>
                <term xml:lang="en">Cost Efficiency</term>
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              <p>Data backup and archiving is an important aspect of business processes to avoid loss due to system failures and natural calamities. As the amount of data and applications grow in number, concerns regarding cost efficient data preservation force organizations to scout for inexpensive storage options. Addressing these concerns, we present Tape Cloud, a novel, highly cost effective, unified storage solution. We leverage the notably economic nature of Magnetic Tapes and design a cloud storage infrastructure-as-a-service that provides a centralized storage platform for unstructured data generated by many diverse applications. We propose and evaluate a proficient middleware that manages data and IO requests, overcomes latencies and improves the overall response time of the storage system. We analyze traces obtained by live archiving applications to obtain workload characteristics. Based on this analysis, we synthesize archiving workloads and design suitable algorithms to evaluate the performance of the middleware and storage tiers. From the results, we see that the use of the middleware provides close to 100% improvement in task distribution efficiency within the system leading to a 70% reduction in overall response time of data retrieval from storage. Due to its easy adaptability with the state of the art storage practices, the middleware contributes in providing the much needed boost in reducing storage costs for data archiving in cloud and colocated infrastructures.</p>
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