What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Central processing unit
His primary areas of study are Parallel computing, Embedded system, Memory map, Scheduling and Disk array.
His Parallel computing research is multidisciplinary, relying on both Memory management and C dynamic memory allocation.
Hubertus Franke has included themes like System software, Latency and Parsing in his Embedded system study.
His Memory map research incorporates elements of Registered memory, Interleaved memory, Computer memory and Flat memory model.
His Scheduling research includes themes of Workload, Supercomputer and Distributed computing.
His Disk array research includes elements of Power management, Memory architecture and Server.
His most cited work include:
- DRPM: dynamic speed control for power management in server class disks (386 citations)
- Thermal-aware task scheduling at the system software level (172 citations)
- Method and apparatus for automated measurement of properties of perishable consumer products (131 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Operating system, Distributed computing, Embedded system, Scheduling and Parallel computing.
His Distributed computing study integrates concerns from other disciplines, such as Workload, Job scheduler, Virtual machine and Coscheduling.
Within one scientific family, Hubertus Franke focuses on topics pertaining to Software under Embedded system, and may sometimes address concerns connected to Cloud computing.
His biological study spans a wide range of topics, including Supercomputer and Computer multitasking.
His primary area of study in Parallel computing is in the field of Cache.
Hubertus Franke interconnects Registered memory and Computer memory in the investigation of issues within Memory management.
He most often published in these fields:
- Operating system (21.62%)
- Distributed computing (18.38%)
- Embedded system (14.05%)
What were the highlights of his more recent work (between 2014-2020)?
- Distributed computing (18.38%)
- Cloud computing (6.49%)
- Software (10.81%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Distributed computing, Cloud computing, Software, Operating system and Set.
Hubertus Franke has researched Distributed computing in several fields, including Scheduling and Latency.
Hubertus Franke focuses mostly in the field of Latency, narrowing it down to matters related to Backplane and, in some cases, Embedded system.
His studies in Embedded system integrate themes in fields like Configurator and Resource.
His studies deal with areas such as Scalability, Database, Software deployment, Provisioning and Server as well as Cloud computing.
His Image research extends to the thematically linked field of Operating system.
Between 2014 and 2020, his most popular works were:
- Composable architecture for rack scale big data computing (41 citations)
- Creating new cloud resource instruction set architecture (30 citations)
- Disaggregated and optically interconnected memory: when will it be cost effective? (24 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Central processing unit
The scientist’s investigation covers issues in Distributed computing, Workload, Operating system, Cloud computing and Memory management.
Hubertus Franke combines subjects such as Latency, Provisioning, Server and Big data with his study of Distributed computing.
His Latency research is multidisciplinary, incorporating elements of Backplane, Instruction set, Embedded system, Code refactoring and Configurator.
His study in Workload is interdisciplinary in nature, drawing from both Virtual machine, Server-side and Set.
His study in the fields of Synchronization, Computer program and PCI Express under the domain of Operating system overlaps with other disciplines such as Systems architecture and Server farm.
His studies examine the connections between Memory management and genetics, as well as such issues in Service-level agreement, with regards to Scalability.
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