WiGriMMA: A Wireless Grid Monitoring Model Using Agents

Birje, Mahantesh N.; Manvi, Sunilkumar S.

doi:10.1007/s10723-011-9181-4

Cited by 16 publications

(4 citation statements)

References 30 publications

(44 reference statements)

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“…In agent based data collection, the agents are installed at different component of cloud to collect and send the data to central server. In the agentless CMS, data collection is easy and cheaper in cost, as it is not essential to install software agent [85]. The agentless services, talk directly to the underlying cloud platform through the service provider's API to collect the data.…”

Section: Data Collectionmentioning

confidence: 99%

Cloud Monitoring System: Basics, Phases and Challenges

Birje¹,

Bulla²

2019

IJRTE

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Due to many economical benefits cloud computing has quickly become popular and is widely used for delivering services over the Internet. As the number of cloud users increases day by day, data centers are continuing to grow in terms of hardware resources, virtual resources and traffic volume; thus making cloud operation and management more intricate. To manage complex infrastructure of cloud, an efficient and effective Cloud Monitoring System (CMS) is needed to improve the overall performance of cloud. Cloud monitoring is the process of reviewing, controlling and managing the operational workflow and processes within a cloud-based IT asset or infrastructure. This paper deals with detailed study of CMSs based on their architecture, monitoring phases, properties and functions. It describes various phases of cloud monitoring activities and presents the comparative study of the state-of-theart works of theses phases. Usage of agents for monitoring of cloud activities is also described. Finally, various challenges/issues and possible future directions of cloud monitoring is discussed. This paper helps researchers, engineers and scientists to know the state-of-the-art works for monitoring cloud activities

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Section: Data Collectionmentioning

confidence: 99%

Cloud Monitoring System: Basics, Phases and Challenges

Birje¹,

Bulla²

2019

IJRTE

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“…Some researchers consider agent‐based approaches to be best suited considering the enormous scale of nodes with huge computing capacity collectively. Mahantesh et al proposed an agent‐based multiple monitoring approaches that continuously monitor the mobility state, resource availability information, current state of the resource, selfish user, and the device state to avoid overloading of a node. The 2 types of agencies used in the model are device agency and grid information service agency for monitoring different kinds of resources and accordingly perform the task scheduling.…”

Section: Literature Surveymentioning

confidence: 99%

A quantum‐inspired binary gravitational search algorithm–based job‐scheduling model for mobile computational grid

Singh

Raza

2017

Concurrency and Computation

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Owing to the advancements in low-power consumption processors and high-power storage in a small-sized battery, the cost of handheld mobile devices, eg, mobiles, tabs, or personal digital assistants, have reduced to a great extent. This has enabled people to have at least 1 smartphone in general with this number increasing exponentially. However, the increasing use of these mobile devices results in an equal increase in the underused processing capacity of these devices too. This encourages the research aiming to use this processing power by forming a mobile computational grid. Because of the inherent limitations of bandwidth, battery, and computational power, job scheduling on these devices demands an efficient scheduling approach to harness the true potential of the grid. The problem becomes even more challenging considering the dynamic nature of these mobile devices. Job scheduling being nondeterministic polynomial time-complete allows the use of evolutionary approaches by exploring and exploiting the search space efficiently. The exploration gets boosted even more with the use of quantum-computing concepts. This work proposes a quantum-inspired Newtonian approach of attraction based on gravitational search algorithm for scheduling the jobs on mobile computational grid. Simulation study has been performed to evaluate the performance of the model over various dimensions. A comparative study has been performed with quantum-genetic algorithm. Simulation result establishes the effectiveness of model under various test conditions. KEYWORDS genetic algorithm (GA), gravitational search algorithm (GSA), mobile grid computing (MGC), quantum computing (QC), quantum-inspired genetic algorithm (QGA), turnaround time (TAT)

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“…To improve performance of fog computing, a monitoring system is required that keep track of all the activities and behavior of fog infrastructure with associated IoT Devices (Birje & Bulla, 2019). Monitoring system is also used in predictive maintenance system to detect and predict faulty or deviating behavior of fog nodes and IoT devices (Birje & Manvi, 2011). The anomaly detection and root cause analysis models play a vital role in improving performance of smart applications such as smart industries and smart healthcare system.…”

Section: Introductionmentioning

confidence: 99%

Improved Data-Driven Root Cause Analysis in a Fog Computing Environment

Bulla

Birje

2022

International Journal of Intelligent Information Technologies

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Internet of Things (IoT) and cloud computing are used in many real-time smart applications such as smart health-care, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT Devices. To improve the performance of these smart applications, a predictive maintenance system needs to adopt an anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in the future. The state of art work on data-driven root cause analysis suffers from scalability, accuracy, and interpretability. In this paper, a multi-agent based improved data-driven root cause analysis technique is introduced to identify anomalies and its root cause. The deep learning model LSTM autoencoder is used to find the anomalies, and a game theory approach called SHAP algorithm is used to find the root cause of the anomaly. The evaluation result shows the improvement in accuracy and interpretability, as compared to state-of-the-art works.

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WiGriMMA: A Wireless Grid Monitoring Model Using Agents

Cited by 16 publications

References 30 publications

Cloud Monitoring System: Basics, Phases and Challenges

Cloud Monitoring System: Basics, Phases and Challenges

A quantum‐inspired binary gravitational search algorithm–based job‐scheduling model for mobile computational grid

Improved Data-Driven Root Cause Analysis in a Fog Computing Environment

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