A P2P Approach to Scalable Network-Booting

2012 ◽  
Author(s):  
Shingo Takada ◽  
Akira Sato ◽  
Yasushi Shinjo ◽  
Hisashi Nakai ◽  
Akiyoshi Sugiki ◽  
...  
Keyword(s):  
Scalable Network

scholarly journals Design and Implementation of Virtual Security Function Based on Multiple Enclaves

2021 ◽  
Vol 13 (1) ◽  
pp. 12
Author(s):  
Juan Wang ◽  
Yang Yu ◽  
Yi Li ◽  
Chengyang Fan ◽  
Shirong Hao
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Network Function Virtualization ◽  
Sensitive Data ◽  
Network Function ◽  
State Security ◽  
Security Challenges ◽  
Iot Platforms ◽  
Scalable Network ◽  
Virtual Network Function

Network function virtualization (NFV) provides flexible and scalable network function for the emerging platform, such as the cloud computing, edge computing, and IoT platforms, while it faces more security challenges, such as tampering with network policies and leaking sensitive processing states, due to running in a shared open environment and lacking the protection of proprietary hardware. Currently, Intel® Software Guard Extensions (SGX) provides a promising way to build a secure and trusted VNF (virtual network function) by isolating VNF or sensitive data into an enclave. However, directly placing multiple VNFs in a single enclave will lose the scalability advantage of NFV. This paper combines SGX and click technology to design the virtual security function architecture based on multiple enclaves. In our design, the sensitive modules of a VNF are put into different enclaves and communicate by local attestation. The system can freely combine these modules according to user requirements, and increase the scalability of the system while protecting its running state security. In addition, we design a new hot-swapping scheme to enable the system to dynamically modify the configuration function at runtime, so that the original VNFs do not need to stop when the function of VNFs is modified. We implement an IDS (intrusion detection system) based on our architecture to verify the feasibility of our system and evaluate its performance. The results show that the overhead introduced by the system architecture is within an acceptable range.

scholarly journals RVIP: an indistinguishable approach to scalable network simulation at real time

2014 ◽  
Vol 15 (18) ◽  
pp. 2219-2232 ◽  
Author(s):  
Jiejun Kong ◽  
Tingzhen Li ◽  
Dapeng Oliver Wu
Keyword(s):  
Real Time ◽  
Network Simulation ◽  
Scalable Network

scholarly journals A Flexible Software/Hardware Adaptive Network for Embedded Distributed Architectures

2021 ◽  
Vol 08 (03) ◽  
pp. 01-15
Author(s):  
Celine Azar
Keyword(s):  
Data Transfer ◽  
Interconnection Network ◽  
Test Cases ◽  
Distributed Architectures ◽  
Small Footprint ◽  
Routing Mechanism ◽  
Routing Method ◽  
Scalable Network ◽  
Direct Management ◽  
Near Future

Embedded platforms are projected to integrate hundreds of cores in the near future, and expanding the interconnection network remains a key challenge. We propose SNet, a new Scalable NETwork paradigm that extends the NoCs area to include a software/hardware dynamic routing mechanism. To design routing pathways among communicating processes, it uses a distributed, adaptive, non-supervised routing method based on the ACO algorithm (Ant Colony Optimization). A small footprint hardware unit called DMC speeds up data transfer (Direct Management of Communications). SNet has the benefit of being extremely versatile, allowing for the creation of a broad range of routing topologies to meet the needs of various applications. We provide the DMC module in this work and assess SNet performance by executing a large number of test cases.

scholarly journals Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1788
Author(s):  
Vy T. Duong ◽  
Elizabeth M. Diessner ◽  
Gianmarc Grazioli ◽  
Rachel W. Martin ◽  
Carter T. Butts
Keyword(s):  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Dynamic Models ◽  
Structural Information ◽  
Coarse Graining ◽  
Molecular Structures ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Scalable Network ◽  
Atomic Coordinates

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

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