Manish Parashar

TAAS Journal 2025 Journal Article

Autonomic Computing Rebooted: Taming the Computing Continuum

• Manish Parashar

Technological advances and rapid service deployments have resulted in a pervasive and interconnected computing continuum, which is enabling new classes of application formulations and workflows, delivering novel services to consumers, and is becoming a core engine for discovery, innovation, and economic growth. The computing continuum is also unleashing new system and application management challenges, which must be addressed before its potential and promises are truly realized. Autonomic computing can provide the abstractions and mechanisms essential to effectively harnessing the computing continuum, but must evolve to address these new challenges. This paper is a call to action for rebooting autonomics to enable us to harness the computing continuum.

AAAI Conference 2020 Conference Paper

A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning

• Kevin Fauvel
• Daniel Balouek-Thomert
• Diego Melgar
• Pedro Silva
• Anthony Simonet
• Gabriel Antoniu
• Alexandru Costan
• Véronique Masson

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems. In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a realworld dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an ef- ficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometeronly approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.

TAAS Journal 2013 Journal Article

Editorial

• Manish Parashar
• Franco Zambonelli

TAAS Journal 2012 Journal Article

Design and evaluation of decentralized online clustering

• Andres Quiroz
• Manish Parashar
• Nathan Gnanasambandam
• Naveen Sharma

Ensuring the efficient and robust operation of distributed computational infrastructures is critical, given that their scale and overall complexity is growing at an alarming rate and that their management is rapidly exceeding human capability. Clustering analysis can be used to find patterns and trends in system operational data, as well as highlight deviations from these patterns. Such analysis can be essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require appropriate management actions. This work analyzes the automated application of clustering for online system management, from the point of view of the suitability of different clustering approaches for the online analysis of system data in a distributed environment, with minimal prior knowledge and within a timeframe that allows the timely interpretation of and response to clustering results. For this purpose, we evaluate DOC (Decentralized Online Clustering), a clustering algorithm designed to support data analysis for autonomic management, and compare it to existing and widely used clustering algorithms. The comparative evaluations will show that DOC achieves a good balance in the trade-offs inherent in the challenges for this type of online management.

KER Journal 2006 Journal Article

Enabling dynamic composition and coordination for autonomic Grid applications using the Rudder Agent framework

• Zhen Li
• Manish Parashar

This paper introduces Rudder, a peer-to-peer agent framework for supporting autonomic applications in decentralized distributed environments. The framework provides agents to discover, select, and compose elements, and defines agent interaction and negotiation protocols to enable appropriate application behaviors to be negotiated and enacted dynamically. The implementations of these protocols as well as agent coordination and negotiation activities are supported by Comet, a scalable decentralized coordination substrate. The operation and experimental evaluation of Rudder is presented.