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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Battula Srinivasa Rao
VIT-AP University
Computer Science & IT Electrical & Electronics Engineering

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Scalable epidemic message passing interface fault tolerance Soma Sekhar Kolisetty; Battula Srinivasa Rao
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3374

Abstract

Resilience and fault tolerance are challenging tasks in the field of high performance computing (HPC) and extreme scale systems. Components fail more often in such systems, results in application abort. Adopting fault–tolerance techniques can be consistently detect failures and continue application’s execution even if the failures exist. A prominent parallel programming specification, message passing interface (MPI), as it would be used to implement failure detection and consensus algorithm in this paper. Although the MPI does not facilitate fault tolerant behavior, this work presents a fault tolerant, matrix based failure detection and consensus algorithm. The proposed algorithm uses Gossiping. To detect failures, randomised pinging will be applied during the execution of the algorithm by using piggybacked gossip messages. In order to achieve consensus on the failures in the system, failed processes’ information will be sent using the same piggybacked gossip messages to all the alive processes. The algorithm was implemented in MPI framework and is completely fault tolerant. The results exhibit all the MPI process failures were detected using randomised pinging and global consensus has achieved on failed MPI process in the system.
Colorectal multi-class image classification using deep learning models Mallela Siva Naga Raju; Battula Srinivasa Rao
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3299

Abstract

Colorectal image classification is a novel application area in medical image processing. Colorectal images are one of the most prevalent malignant tumour disease type in the world. However, due to the complexity of histopathological imaging, the most accurate and effective classification still needs to be addressed. In this work we proposed a novel architecture of convolution neural network with deep learning models for the multiclass classification of histopathology images. We achieved the findings using three deep learning models, including the vgg16 with 96.16% and a modified version of Resnet50 with 97.08%, however the proposed Adaptive Resnet152 model generated the best accuracy of 98.38%. The colorectal image multiclass dataset is publicly available which has 5000 images with 8 classes. In this study we have increased all classes equally, total 15000 images have been generated using image augmentation technique. This dataset consists of 60% training images and 40% testing images. The suggested method in this paper produced better results than the existing histopathology image categorization methods with the lowest error rate. For histopathological image categorization, it is a straightforward, effective, and efficient method. We were able to attain state-of-the-art outcomes by efficiently utilizing the resourced dataset.
Efficient failure detection and consensus at extreme-scale systems Soma Sekhar Kolisetty; Battula Srinivasa Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5339-5347

Abstract

Distributed systems and extreme-scale systems are ubiquitous in recent years and have seen throughout academia organizations, business, home, and government sectors. Peer-to-peer (P2P) technology is a typical distributed system model that is gaining popularity for delivering computing resources and services. Distributed systems try to increase its availability in the event of frequent component failures and functioning the system in such scenario is notoriously difficult. In order to identify component failures in the system and achieve global agreement (consensus) among failed components, this paper implemented an efficient failure detection and consensus algorithm based on fail-stop type process failures. The proposed algorithm is fault-tolerant to process failures occurring before and during the execution of the algorithm. The proposed algorithm works with the epidemic gossip protocol, which is a randomly generated paradigm of computation and communication that is both fault-tolerant and scalable. A simulation of an extreme-scale information dissemination process shows that global agreement can be achieved. A P2P simulator, PeerSim, is used in the paper to implement and test the proposed algorithm. The proposed algorithm results exhibited high scalability and at the same time detected all the process failures. The status of all the processes is maintained in a Boolean matrix.
Co-Authors Mallela Siva Naga Raju Soma Sekhar Kolisetty