Valve: Securing Function Workflows on Serverless Computing Platforms

2020 ◽  
Author(s):  
Pubali Datta ◽  
Prabuddha Kumar ◽  
Tristan Morris ◽  
Michael Grace ◽  
Amir Rahmati ◽  
...  
Keyword(s):  
Computing Platforms

scholarly journals Scheduling Challenges in Mixed Critical Real-Time Heterogeneous Computing Platforms

2013 ◽  
Vol 18 ◽  
pp. 1891-1898
Author(s):  
Chetan Kumar N G ◽  
Sudhanshu Vyas ◽  
Ron K. Cytron ◽  
Christopher D. Gill ◽  
Joseph Zambreno ◽  
...  
Keyword(s):  
Real Time ◽  
Heterogeneous Computing ◽  
Computing Platforms

scholarly journals A Survey of Software-Defined Networks-on-Chip: Motivations, Challenges and Opportunities

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 183
Author(s):  
Jose Ricardo Gomez-Rodriguez ◽  
Remberto Sandoval-Arechiga ◽  
Salvador Ibarra-Delgado ◽  
Viktor Ivan Rodriguez-Abdala ◽  
Jose Luis Vazquez-Avila ◽  
...  
Keyword(s):  
Single Chip ◽  
Synthesis Time ◽  
Networks On Chip ◽  
Data Dependencies ◽  
Layered Architecture ◽  
Systems On Chip ◽  
Challenges And Opportunities ◽  
Computing Platforms ◽  
On Chip ◽  
Many Core

Current computing platforms encourage the integration of thousands of processing cores, and their interconnections, into a single chip. Mobile smartphones, IoT, embedded devices, desktops, and data centers use Many-Core Systems-on-Chip (SoCs) to exploit their compute power and parallelism to meet the dynamic workload requirements. Networks-on-Chip (NoCs) lead to scalable connectivity for diverse applications with distinct traffic patterns and data dependencies. However, when the system executes various applications in traditional NoCs—optimized and fixed at synthesis time—the interconnection nonconformity with the different applications’ requirements generates limitations in the performance. In the literature, NoC designs embraced the Software-Defined Networking (SDN) strategy to evolve into an adaptable interconnection solution for future chips. However, the works surveyed implement a partial Software-Defined Network-on-Chip (SDNoC) approach, leaving aside the SDN layered architecture that brings interoperability in conventional networking. This paper explores the SDNoC literature and classifies it regarding the desired SDN features that each work presents. Then, we described the challenges and opportunities detected from the literature survey. Moreover, we explain the motivation for an SDNoC approach, and we expose both SDN and SDNoC concepts and architectures. We observe that works in the literature employed an uncomplete layered SDNoC approach. This fact creates various fertile areas in the SDNoC architecture where researchers may contribute to Many-Core SoCs designs.

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Danielle V. Handel ◽  
Anson T. Y. Ho ◽  
Kim P. Huynh ◽  
David T. Jacho-Chávez ◽  
Carson H. Rea
Keyword(s):  
Cloud Computing ◽  
Data Science ◽  
Internet Access ◽  
Next Generation ◽  
Web Browser ◽  
Computer Laboratory ◽  
Economics Students ◽  
Computing Platforms

AbstractThis paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

STC: Exposing Hidden Compromised Devices in Networked Sustainable Green Smart Computing Platforms by Partial Observation

2019 ◽  
Vol 4 (2) ◽  
pp. 178-190
Author(s):  
Derek Wang ◽  
Tingmin Wu ◽  
Sheng Wen ◽  
Xiaofeng Chen ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Partial Observation ◽  
Computing Platforms ◽  
Smart Computing

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

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