What is she best known for?
The fields of study she is best known for:
- Operating system
- Computer network
- Central processing unit
Her primary areas of study are Distributed computing, Workload, Capacity planning, Scalability and Queueing theory.
The various areas that Evgenia Smirni examines in her Distributed computing study include Parallel computing, Input/output and Scheduling, Computer network, Provisioning.
Her Computer network study incorporates themes from Web server and Round-robin DNS.
Her Workload research incorporates themes from Data center, Server and Robustness.
The Capacity planning study combines topics in areas such as Application lifecycle management, Anomaly detection, Real-time computing and Service.
In Queueing theory, Evgenia Smirni works on issues like Algorithm, which are connected to Hyperexponential distribution, Data set, Markovian arrival process and Mathematical optimization.
Her most cited work include:
- A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications (244 citations)
- Workload-aware load balancing for clustered Web servers (114 citations)
- Anomaly? application change? or workload change? towards automated detection of application performance anomaly and change (111 citations)
What are the main themes of her work throughout her whole career to date?
The scientist’s investigation covers issues in Distributed computing, Workload, Scheduling, Queueing theory and Server.
Her studies deal with areas such as Capacity planning and Computer network, Resource allocation as well as Distributed computing.
Her Capacity planning research focuses on subjects like Scalability, which are linked to Benchmark.
Her Workload study integrates concerns from other disciplines, such as Job scheduler, Real-time computing, Data center and Cloud computing.
Her work deals with themes such as Supercomputer, Idle and Computer data storage, which intersect with Scheduling.
She combines subjects such as Algorithm, Queue and Mathematical optimization with her study of Queueing theory.
She most often published in these fields:
- Distributed computing (39.25%)
- Workload (33.87%)
- Scheduling (19.35%)
What were the highlights of her more recent work (between 2014-2021)?
- Workload (33.87%)
- Cloud computing (10.75%)
- Distributed computing (39.25%)
In recent papers she was focusing on the following fields of study:
Evgenia Smirni mostly deals with Workload, Cloud computing, Distributed computing, Virtual machine and Scheduling.
The study incorporates disciplines such as Data mining, Predictive modelling, Machine learning, Real-time computing and Data center in addition to Workload.
Evgenia Smirni interconnects Predictive analytics, Supercomputer and End user in the investigation of issues within Distributed computing.
The concepts of her Virtual machine study are interwoven with issues in Computer network and Data science.
Her Scheduling research incorporates elements of Queueing theory and Artificial intelligence.
Her research integrates issues of Provisioning and Resource allocation in her study of Virtualization.
Between 2014 and 2021, her most popular works were:
- PRACTISE: Robust prediction of data center time series (47 citations)
- A large-scale study of soft-errors on GPUs in the field (41 citations)
- Machine Learning Models for GPU Error Prediction in a Large Scale HPC System (31 citations)
In her most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Central processing unit
Her primary areas of investigation include Distributed computing, Virtual machine, Workload, Data center and Computer network.
Her Distributed computing research focuses on Scheduling and how it connects with Cloud computing.
The various areas that Evgenia Smirni examines in her Workload study include Data mining and Time series.
In her work, Usage data, Host and Capacity planning is strongly intertwined with Ticket, which is a subfield of Data center.
Her studies in Computer network integrate themes in fields like Temporal isolation among virtual machines, Data virtualization, Virtualization, Thin provisioning and Hardware virtualization.
Her Dynamic priority scheduling study integrates concerns from other disciplines, such as Scalability and Cache.
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