Selected Publications

Many predictive approaches have been proposed to overcome the limitations of reactive autoscaling on clouds. These approaches leverage workload predictors that are usually targeted for a particular workload pattern and can fail to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time, or may be irregular. The result is that resources are frequently under- and overprovisioned. To address this problem, we create a novel cloud workload prediction framework called CloudInsight, leveraging the combined power of multiple workload predictors that collectively provide a 'council of experts'.
In 10th IEEE International Conference on Cloud Computing (Cloud 2018), July 2 - July 7, 2018, San Francisco, USA

This paper presents Orchestra, a cloud-specific framework for managing both foreground applications (e.g.,Web, DBMS) and background services (e.g., backup, security check, batch jobs) in the user space. Orchestra is designed to address 'resource storms' caused by sudden executions of the background services on the cloud instances.
In 17th IEEE International Symposium on Parallel and Distributed Computing (IEEE ISPDC 2018), June 25 - 28, 2018, Geneva, Switzerland

Data quality control is one of the most time consuming activities within Research Infrastructures (RIs), especially when involving observational data and multiple data providers. In this work we report on our ongoing development of data rogues, a scalable approach to manage data quality issues for observational data within RIs.
Proceedings of the 13th IEEE International Conference on eScience (eScience 2017). Oct 24-27 2017

While early public cloud IoT success stories have focused on smaller-scale scenarios such as connected houses, it is unclear to what extent these new public cloud mechanisms and abstractions are suitable and effective for larger-scale and/or scientific scenarios, which often have a different set of constraints or requirements. This paper addresses the challenge of implementing a scalable IoT infrastructure testbed in the public cloud for scientific experimentation. The system created is for dynamic vehicle traffic control based on vehicle volumes/patterns and weather conditions. We find that while AWS IoT performance and performance scalability are likely to meet the requirements of many next-generation scientific IoT use-cases, manageability/modifications of a scientific IoT scenario can be challenging for moderate- to large-scale deployments.
In 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016), December 6-9, 2016 – Tongji University – Shanghai, China

Recent Publications

More Publications

  • CloudInsight: Utilizing a Council of Experts to Predict Future Cloud Application Workloads (IEEE Cloud 2018)

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  • Orchestra: Guaranteeing Performance SLAs for Cloud Applications by Avoiding Resource Storms (IEEE ISPDC 2018)

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  • Hunting Data Rogues at Scale: Data Quality Control for Observational Data in Research Infrastructures (eScience 2017)

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  • iCSI: A Cloud Garbage VM Collector for Addressing Inactive VMs with Machine Learning (IC2E 2017)

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  • Experiences Creating a Framework for Smart Traffic Control using AWS IOT (UCC 2016)

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  • Empirical Evaluation of Cloud Workload Forecasting Techniques (Cloud 2016)

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  • WDCloud: An End to End System for Large-Scale Watershed Delineation on Cloud (Big Data-Geosciences 2015)

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  • PICS: A Public IaaS Cloud Simulator (Cloud 2015)

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  • Toward Optimal Resource Provisioning for Cloud MapReduce and Hybrid Cloud Applications (Cloud 2015)

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  • Calibration of SWAT models using the Cloud (J. Env Model/SW 2014)

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  • Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds (UCC 2014)

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  • CloudDRN: A Lightweight, End-to-End System for Sharing Distributed Research Data in the Cloud (eScience 2013)

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  • Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows (IPDPS 2013)

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  • Calibration of Watershed Models using Cloud Computing (eScience 2012)

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  • A Performance Study on the VM Startup Time in the Cloud (Cloud 2012)

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  • A Model and Decision Procedure for Data Storage in Cloud Computing (CCGrid 2012)

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  • Assessing the Value of Cloudbursting: A Case Study of Satellite Image Processing on Windows Azure (eScience 2011)

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  • Auto-scaling to minimize cost and meet application deadlines in cloud workflows (Supercomputing 2011)

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  • Data-intensive science: The Terapixel and MODISAzure projects (Int J HPCA 2011)

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  • An automated approach to cloud storage service selection (Science Cloud 2011)

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  • Predictable high-performance computing using feedback control and admission control (J IEEE Trans Par Dis 2011)

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  • Early observations on the performance of Windows Azure (J Sci Prog 2011)

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  • Fault tolerance and scaling in e-Science cloud applications: observations from the continuing development of MODISAzure (eScience 2010)

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  • CSAL: A Cloud Storage Abstraction Layer to Enable Portable Cloud Applications (CloudCom 2010)

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  • Cloud auto-scaling with deadline and budget constraints (Grid 2010)

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  • A data-centered collaboration portal to support global carbon-flux analysis (J Con Comp:Prac Exp 2010)

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  • eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform (IPDPS 2010)

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  • Fluxdata.org: Publication and Curation of Shared Scientific Climate and Earth Sciences Data (eScience 2009)

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  • A Quantitative Analysis of High Performance Computing with Amazon’s EC2 Infrastructure: The Death of the Local Cluster? (Grid 2009)

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  • Self-Tuning Virtual Machines for Predictable eScience (CCGrid 2009)

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Projects

  • AmeriFlux

    The AmeriFlux network is a community of sites and scientists measuring ecosystem carbon, water, and energy fluxes across the Americas, and committed to producing and sharing high quality eddy covariance data. AmeriFlux investigators and modelers work together to generate understanding of terrestrial ecosystems in a changing world. We are part of the data team, which is led by Deb Agarwal of LBL.

  • Cloud Auto-scaling

    Public cloud computing has almost all of the capabilities one could ask for; how can we automatically choose/provision/manage the cloud resources to match our requirements?

  • Fluxnet

    Today, the eddy covariance flux measurements of carbon, water vapor, energy exchange are being made routinely across a confederation of regional networks in North, Central and South America, Europe, Asia, Africa, and Australia, in a global network, called FLUXNET. We are part of the infrastructure team, which is led by Dennis Baldocchi of UC-Berkeley.

Teaching

I teach the following courses at the University of Virginia:

  • CS4740: Cloud Computing (Spring 2018)
  • CS6501: Cloud Computing (graduate seminar) (Fall 2017)
  • CS4740: Cloud Computing (Spring 2017)
  • CS4740: Cloud Computing (Fall 2016)
  • CS4740: Cloud Computing (Spring 2016)
  • CS 6501: Cloud and Big Data (graduate seminar) (Fall 2015)
  • CS 4740: Cloud Computing (Spring 2015)
  • CS 6456: Operating Systems (Fall 2014)

Contact