His primary scientific interests are in Distributed computing, Cloud computing, Virtual machine, Grid computing and Operating system. His Distributed computing study typically links adjacent topics like Software. His biological study spans a wide range of topics, including Resource allocation, Database, Memory bandwidth, Gigabit Ethernet and Node.
His Virtual machine study integrates concerns from other disciplines, such as Scalability, Workload, Volume, Elasticity and Data center. In general Grid computing, his work in Data grid is often linked to Computer security, Computer security model and Information privacy linking many areas of study. His Operating system research integrates issues from SQL and Distributed object.
His scientific interests lie mostly in Grid computing, Distributed computing, Cloud computing, Computer security and World Wide Web. His Grid computing study frequently draws connections to adjacent fields such as Semantic grid. The study incorporates disciplines such as Scheduling, Computer network, Virtual machine and Software in addition to Distributed computing.
His Cloud computing study necessitates a more in-depth grasp of Operating system. Interoperability is the focus of his World Wide Web research. His research integrates issues of Machine learning and Artificial intelligence in his study of Workload.
Marty Humphrey mainly focuses on Cloud computing, Real-time computing, Scalability, Workload and Artificial intelligence. The various areas that Marty Humphrey examines in his Cloud computing study include Data modeling, Distributed computing, Computer security, Scheduling and Provisioning. Marty Humphrey performs multidisciplinary study in Distributed computing and Production in his work.
His Real-time computing study incorporates themes from Cloud service provider, Cloud computing security, Service, Calibration algorithm and Job scheduler. Marty Humphrey has researched Scalability in several fields, including Water resources, Communications system, Data science and Implementation. His study in the fields of Autoscaling under the domain of Workload overlaps with other disciplines such as Scaling.
His primary areas of study are Cloud computing, Real-time computing, Job scheduler, Remote sensing and Risk analysis. His research in Cloud computing intersects with topics in Workload, Scalability, Calibration and Speedup. His studies deal with areas such as Variety, Artificial intelligence, Machine learning and Cost efficiency as well as Real-time computing.
His study in Job scheduler is interdisciplinary in nature, drawing from both Service, Cloud computing security, Cloud testing, Simulation and Flexibility. His Remote sensing research is multidisciplinary, incorporating perspectives in Consistency, Metadata, Data collection and Time series. His Risk analysis research incorporates Response time and Resource Management System.
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Auto-scaling to minimize cost and meet application deadlines in cloud workflows
Ming Mao;Marty Humphrey.
ieee international conference on high performance computing data and analytics (2011)
A Performance Study on the VM Startup Time in the Cloud
Ming Mao;Marty Humphrey.
international conference on cloud computing (2012)
Cloud auto-scaling with deadline and budget constraints
Ming Mao;Jie Li;Marty Humphrey.
grid computing (2010)
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello;Carlo Trotta;Eleonora Canfora;Housen Chu.
Scientific Data (2020)
The MyProxy online credential repository
Jim Basney;Marty Humphrey;Von Welch.
Software - Practice and Experience (2005)
Mobile OGSI.NET: Grid Computing on Mobile Devices
David C. Chu;Marty Humphrey.
grid computing (2004)
Early observations on the performance of Windows Azure
Zach Hill;Jie Li;Ming Mao;Arkaitz Ruiz-Alvarez.
high performance distributed computing (2010)
VEST: an aspect-based composition tool for real-time systems
J.A. Stankovic;Ruiqing Zhu;R. Poornalingam;Chenyang Lu.
real time technology and applications symposium (2003)
Security for Grids
M. Humphrey;M.R. Thompson;K.R. Jackson.
Lawrence Berkeley National Laboratory (2005)
eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform
Jie Li;Marty Humphrey;Deb Agarwal;Keith Jackson.
international parallel and distributed processing symposium (2010)
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