What is he best known for?
The fields of study he is best known for:
- Operating system
- Software
- Microsoft Windows
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 most cited work include:
- Auto-scaling to minimize cost and meet application deadlines in cloud workflows (436 citations)
- A Performance Study on the VM Startup Time in the Cloud (397 citations)
- Cloud auto-scaling with deadline and budget constraints (270 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Grid computing (31.16%)
- Distributed computing (28.99%)
- Cloud computing (23.91%)
What were the highlights of his more recent work (between 2013-2021)?
- Cloud computing (23.91%)
- Real-time computing (5.80%)
- Scalability (10.87%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (67 citations)
- Experiences creating a framework for smart traffic control using AWS IOT (25 citations)
- Calibration of SWAT models using the cloud (22 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Microsoft Windows
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
