A CLOUD ARCHITECTURE FOR BIOINFORMATICS WORKFLOWS

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

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Development of a hybrid DLT cloud architecture for the automated use of finite element simulation as a service for fine blanking

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-05567-5 ◽

2020 ◽

Vol 108 (11-12) ◽

pp. 3717-3724 ◽

Cited By ~ 1

Author(s):

Joachim Stanke ◽

Martin Unterberg ◽

Daniel Trauth ◽

Thomas Bergs

Keyword(s):

Finite Element ◽

Finite Element Simulation ◽

Fine Blanking ◽

Cloud Architecture ◽

Element Simulation

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Fog-Integrated Cloud Architecture enabled multi-attribute combinatorial reverse auctioning framework

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2021.102307 ◽

2021 ◽

Vol 109 ◽

pp. 102307

Author(s):

Anubha Aggarwal ◽

Neetesh Kumar ◽

Deo Prakash Vidyarthi ◽

Rajkumar Buyya

Keyword(s):

Cloud Architecture

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Design of intelligent micro network group intelligent operation and maintenance management platform based on Cloud Architecture

2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) ◽

10.1109/auteee50969.2020.9315699 ◽

2020 ◽

Author(s):

Zewei Pu ◽

Li Wan ◽

Min Wu ◽

Qimiciren ◽

Wenya Wang

Keyword(s):

Maintenance Management ◽

Management Platform ◽

Cloud Architecture ◽

Operation And Maintenance

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HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments

Applied Sciences ◽

10.3390/app11030923 ◽

2021 ◽

Vol 11 (3) ◽

pp. 923

Author(s):

Guohua Li ◽

Joon Woo ◽

Sang Boem Lim

Keyword(s):

High Performance ◽

Cloud Services ◽

Workload Management ◽

Job Management ◽

Security Issues ◽

Cloud Architecture ◽

Management Efficiency ◽

Complexity Problem ◽

And Performance ◽

Hpc Cloud

The complexity of high-performance computing (HPC) workflows is an important issue in the provision of HPC cloud services in most national supercomputing centers. This complexity problem is especially critical because it affects HPC resource scalability, management efficiency, and convenience of use. To solve this problem, while exploiting the advantage of bare-metal-level high performance, container-based cloud solutions have been developed. However, various problems still exist, such as an isolated environment between HPC and the cloud, security issues, and workload management issues. We propose an architecture that reduces this complexity by using Docker and Singularity, which are the container platforms most often used in the HPC cloud field. This HPC cloud architecture integrates both image management and job management, which are the two main elements of HPC cloud workflows. To evaluate the serviceability and performance of the proposed architecture, we developed and implemented a platform in an HPC cluster experiment. Experimental results indicated that the proposed HPC cloud architecture can reduce complexity to provide supercomputing resource scalability, high performance, user convenience, various HPC applications, and management efficiency.

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