Advanced measurement infrastructures for time-sensitive applications using ACP architecture
The growth of humanity and online sources of stored digital recordings increases day by day in the form of digital files available in cloud explorer formats. Most of the industry will be affected by Bigdata in the foreseeable future. By providing a comprehensive and trustworthy summary of available...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
|
| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422000575 |
| _version_ | 1811276703962497024 |
|---|---|
| author | Ummadi Janardhan Reddy G. Ravikanth M. Ranjit Reddy |
| author_facet | Ummadi Janardhan Reddy G. Ravikanth M. Ranjit Reddy |
| author_sort | Ummadi Janardhan Reddy |
| collection | DOAJ |
| description | The growth of humanity and online sources of stored digital recordings increases day by day in the form of digital files available in cloud explorer formats. Most of the industry will be affected by Bigdata in the foreseeable future. By providing a comprehensive and trustworthy summary of available information, Big Data can assist in the transformation of key organizational processes. Big data has played an important part in identifying violent crime. The current architecture for developing Bigdata deployments is efficient, high-performance computing by analyzing Bigdata combining multiple computing units to perform sophisticated computing. This article presents the Additional Commodity Planner (ACP) lightweight structure examines the basic concepts of many typical big data deployments. It concentrates on the notion of time-critical big data systems from the standpoint of requirements. ACP is the standard computational architecture that helps MapReduce and other Hadoop applications. ACP requires the concurrent execution of multiple programs on a shared server and the use of consent programs to allocate services as required. This final assessment was followed by issues raised by systems and facilities that support applications, and provide frameworks for effective initial features that often contribute to operational reliability. |
| first_indexed | 2024-04-13T00:02:27Z |
| format | Article |
| id | doaj.art-6ba94a7636034f8abe3fe3304b6bb602 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-13T00:02:27Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-6ba94a7636034f8abe3fe3304b6bb6022022-12-22T03:11:19ZengElsevierMeasurement: Sensors2665-91742022-12-0124100423Advanced measurement infrastructures for time-sensitive applications using ACP architectureUmmadi Janardhan Reddy0G. Ravikanth1M. Ranjit Reddy2School of Computer Science and Engineering, REVA University, Bengaluru, 560064, Karnataka, India; Corresponding author.Department of Electronics and Communications Engineering, BVC College of Engineering, Rajahmundry, 533102, IndiaDepartment of Computer Science and Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, 515701, IndiaThe growth of humanity and online sources of stored digital recordings increases day by day in the form of digital files available in cloud explorer formats. Most of the industry will be affected by Bigdata in the foreseeable future. By providing a comprehensive and trustworthy summary of available information, Big Data can assist in the transformation of key organizational processes. Big data has played an important part in identifying violent crime. The current architecture for developing Bigdata deployments is efficient, high-performance computing by analyzing Bigdata combining multiple computing units to perform sophisticated computing. This article presents the Additional Commodity Planner (ACP) lightweight structure examines the basic concepts of many typical big data deployments. It concentrates on the notion of time-critical big data systems from the standpoint of requirements. ACP is the standard computational architecture that helps MapReduce and other Hadoop applications. ACP requires the concurrent execution of multiple programs on a shared server and the use of consent programs to allocate services as required. This final assessment was followed by issues raised by systems and facilities that support applications, and provide frameworks for effective initial features that often contribute to operational reliability.http://www.sciencedirect.com/science/article/pii/S2665917422000575ACP ArchitectureLightweight architectureTask trackersBig data |
| spellingShingle | Ummadi Janardhan Reddy G. Ravikanth M. Ranjit Reddy Advanced measurement infrastructures for time-sensitive applications using ACP architecture Measurement: Sensors ACP Architecture Lightweight architecture Task trackers Big data |
| title | Advanced measurement infrastructures for time-sensitive applications using ACP architecture |
| title_full | Advanced measurement infrastructures for time-sensitive applications using ACP architecture |
| title_fullStr | Advanced measurement infrastructures for time-sensitive applications using ACP architecture |
| title_full_unstemmed | Advanced measurement infrastructures for time-sensitive applications using ACP architecture |
| title_short | Advanced measurement infrastructures for time-sensitive applications using ACP architecture |
| title_sort | advanced measurement infrastructures for time sensitive applications using acp architecture |
| topic | ACP Architecture Lightweight architecture Task trackers Big data |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422000575 |
| work_keys_str_mv | AT ummadijanardhanreddy advancedmeasurementinfrastructuresfortimesensitiveapplicationsusingacparchitecture AT gravikanth advancedmeasurementinfrastructuresfortimesensitiveapplicationsusingacparchitecture AT mranjitreddy advancedmeasurementinfrastructuresfortimesensitiveapplicationsusingacparchitecture |