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Creators/Authors contains: "Tiwari, Devesh"

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  1. ; ; ; ; ; ; ; ( , Future Generation Computer Systems)
    Free, publicly-accessible full text available April 1, 2025
  2. ; ; ; ; ; ; ; ; ; ; et al ( , ACM)
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  3. ; ; ; ; ; ; ( , DSN)
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  4. ; ; ( , ASPLOS '22: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
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  5. ; ; ; ; ( , Proceedings of the ASCR Workshop on the Management and Storage of Scientific Data)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ( , Proceedings of the ASCR Workshop on the Management and Storage of Scientific Data)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ( , Design, Automation and Test in Europe Conference. The European Event for Electronic System Design and Test (DATE’22))
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  8. ; ; ; ; ( , Social Network Analysis and Mining)
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  9. ; ; ( , 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID))
    Academic cloud infrastructures require users to specify an estimate of their resource requirements. The resource usage for applications often depends on the input file sizes, parameters, optimization flags, and attributes, specified for each run. Incorrect estimation can result in low resource utilization of the entire infrastructure and long wait times for jobs in the queue. We have designed a Resource Utilization based Migration (RUMIG) system to address the resource estimation problem. We present the overall architecture of the two-stage elastic cluster design, the Apache Mesos-specific container migration system, and analyze the performance for several scientific workloads on three different cloud/cluster environments. In this paper we (b) present a design and implementation for container migration in a Mesos environment, (c) evaluate the effect of right-sizing and cluster elasticity on overall performance, (d) analyze different profiling intervals to determine the best fit, (e) determine the overhead of our profiling mechanism. Compared to the default use of Apache Mesos, in the best cases, RUMIG provides a gain of 65% in runtime (local cluster), 51% in CPU utilization in the Chameleon cloud, and 27% in memory utilization in the Jetstream cloud. 
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  10. ; ; ; ( , 2019 IEEE 12th International Conference on Cloud Computing (CLOUD))
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