What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Programming language
Distributed computing, Symmetric multiprocessor system, Scheduling, Parallel computing and Heuristics are his primary areas of study.
His Distributed computing research integrates issues from Computer network, Resource allocation, Mobile computing, Wireless ad hoc network and Grid computing.
The concepts of his Symmetric multiprocessor system study are interwoven with issues in Virtual machine, Task and Heuristic.
His biological study spans a wide range of topics, including Resource Management System, Benchmarking and Load balancing.
His study in Parallel computing is interdisciplinary in nature, drawing from both Computation and Interconnection.
Howard Jay Siegel interconnects Workload, Supercomputer and Theoretical computer science in the investigation of issues within Heuristics.
His most cited work include:
- A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems (1521 citations)
- Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems (718 citations)
- Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach (384 citations)
What are the main themes of his work throughout his whole career to date?
Howard Jay Siegel mainly investigates Distributed computing, Parallel computing, Symmetric multiprocessor system, Heuristics and Resource allocation.
His Distributed computing study also includes
- Scheduling together with Supercomputer,
- Workload, which have a strong connection to Data center.
His study looks at the intersection of Parallel computing and topics like Interconnection with Routing, Fault tolerance and Network topology.
The various areas that he examines in his Symmetric multiprocessor system study include Energy consumption, Genetic algorithm, Set, Heuristic and Task.
Howard Jay Siegel combines subjects such as Distributed algorithm, Real-time computing and Distributed Computing Environment with his study of Heuristics.
His studies deal with areas such as Wireless ad hoc network, Mathematical optimization, Job shop scheduling and Server as well as Resource allocation.
He most often published in these fields:
- Distributed computing (45.92%)
- Parallel computing (30.07%)
- Symmetric multiprocessor system (24.01%)
What were the highlights of his more recent work (between 2011-2021)?
- Distributed computing (45.92%)
- Symmetric multiprocessor system (24.01%)
- Resource allocation (19.11%)
In recent papers he was focusing on the following fields of study:
Howard Jay Siegel mainly focuses on Distributed computing, Symmetric multiprocessor system, Resource allocation, Scheduling and Heuristics.
Howard Jay Siegel has researched Distributed computing in several fields, including Supercomputer, Energy consumption, Workload, Real-time computing and Server.
Howard Jay Siegel usually deals with Symmetric multiprocessor system and limits it to topics linked to Job shop scheduling and Scalability.
His Resource allocation study combines topics from a wide range of disciplines, such as Cache, Genetic algorithm, Mathematical optimization, Smart grid and Robustness.
Howard Jay Siegel has included themes like Efficient energy use and Bellman equation in his Scheduling study.
His work in Heuristics addresses subjects such as Heuristic, which are connected to disciplines such as Frequency scaling.
Between 2011 and 2021, his most popular works were:
- Heuristic Optimization for an Aggregator-Based Resource Allocation in the Smart Grid (67 citations)
- A Bee Colony based optimization approach for simultaneous job scheduling and data replication in grid environments (62 citations)
- Power and Thermal-Aware Workload Allocation in Heterogeneous Data Centers (42 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Programming language
His primary areas of investigation include Distributed computing, Resource allocation, Symmetric multiprocessor system, Scheduling and Supercomputer.
His Distributed computing research is multidisciplinary, incorporating perspectives in Energy consumption, Pareto principle, Mathematical optimization, Multi-core processor and Job shop scheduling.
His work deals with themes such as Workload and Robustness, which intersect with Resource allocation.
His Workload study combines topics in areas such as Data center and Parallel computing.
His Symmetric multiprocessor system research incorporates themes from Real-time computing and Heuristics.
His research in Heuristics intersects with topics in Utility computing, Frequency scaling, Task, Heuristic and Energy.
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