What is he best known for?
The fields of study he is best known for:
- Operating system
- Cloud computing
- Computer network
His main research concerns Cloud computing, Scheduling, Quality of service, Distributed computing and Service-level agreement.
His Cloud computing study combines topics in areas such as Virtual machine, Provisioning and Data center.
His biological study spans a wide range of topics, including Workload, Scalability and Green computing.
His study in Scalability is interdisciplinary in nature, drawing from both Profit margin, Fault tolerance, Microservices, Edge computing and Big data.
Saurabh Garg has included themes like Two-level scheduling, Fair-share scheduling and Cost efficiency in his Distributed computing study.
His work in Service-level agreement addresses subjects such as Services computing, which are connected to disciplines such as Cloud computing security, Resource Management System, Knowledge management, Utility computing and Service-oriented architecture.
His most cited work include:
- A framework for ranking of cloud computing services (590 citations)
- SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments (283 citations)
- Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers (265 citations)
What are the main themes of his work throughout his whole career to date?
Saurabh Garg mostly deals with Cloud computing, Distributed computing, Big data, Scheduling and Scalability.
His Cloud computing research includes elements of Quality of service, Computer network, Provisioning and Virtual machine.
His Quality of service study combines topics from a wide range of disciplines, such as Software deployment and Cloud computing security.
His Distributed computing research is multidisciplinary, incorporating perspectives in Resource allocation, Enhanced Data Rates for GSM Evolution, Grid computing, Dynamic priority scheduling and Fair-share scheduling.
In his research, Social network is intimately related to Data science, which falls under the overarching field of Big data.
Heuristics is closely connected to Workload in his research, which is encompassed under the umbrella topic of Scheduling.
He most often published in these fields:
- Cloud computing (51.01%)
- Distributed computing (38.26%)
- Big data (22.82%)
What were the highlights of his more recent work (between 2018-2021)?
- Cloud computing (51.01%)
- Distributed computing (38.26%)
- Big data (22.82%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Cloud computing, Distributed computing, Big data, Scalability and Workflow.
His work on Virtualization as part of general Cloud computing study is frequently linked to Spark, therefore connecting diverse disciplines of science.
His Distributed computing study incorporates themes from Resource allocation, Edge computing, Enhanced Data Rates for GSM Evolution, Resource and Fog computing.
The various areas that Saurabh Garg examines in his Big data study include Software-defined networking, Service layer, Scheduling, Provisioning and Data science.
As a part of the same scientific study, he usually deals with the Scheduling, concentrating on Workflow application and frequently concerns with Data stream mining.
His Scalability study integrates concerns from other disciplines, such as Process control, Cyber-physical system, Peer-to-peer and Benchmark.
Between 2018 and 2021, his most popular works were:
- Renewable Energy-Based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers (40 citations)
- Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment (27 citations)
- Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions (22 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Cloud computing
- The Internet
Saurabh Garg mainly focuses on Cloud computing, Distributed computing, Scalability, Big data and Edge computing.
His Cloud computing research is multidisciplinary, incorporating elements of Scheduling, Resource allocation and Data mining.
The Scheduling study combines topics in areas such as Virtualization, Green computing, Data center and Database.
His studies in Distributed computing integrate themes in fields like Enhanced Data Rates for GSM Evolution and Fog computing.
His Scalability research is multidisciplinary, relying on both Uncertainty quantification, Server and Computational model.
The Big data study combines topics in areas such as Private information retrieval, Service, Blockchain, The Internet and Workflow.
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