Husanbir Singh Pannu

AAD: Adaptive Anomaly Detection System for Cloud Computing Infrastructures

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructure. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software failures. Autonomic failure detection is a crucial technique for understanding emergent, cloudwide phenomena and self-managing cloud resources for system-level dependability assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled, and thus a prior failure history is not always available in production clouds, especially for newly managed or deployed systems. In this paper, we present an Adaptive Anomaly Detection (AAD) framework for cloud dependability assurance. It employs data description using hypersphere for adaptive failure detection. Based on the cloud performance data, AAD detects possible failures, which are verified by the cloud operators. They are confirmed as either true failures with failure types or normal states. The algorithm adapts itself by recursively learning from these newly verified detection results to refine future detections. Meanwhile, it exploits the observed but undetected failure records reported by the cloud operators to identify new types of failures. We have implemented a prototype of the algorithm and conducted experiments in an on-campus cloud computing environment. Our experimental results show that AAD can achieve more efficient and accurate failure detection than other existing scheme.

A self-evolving anomaly detection framework for developing highly dependable utility clouds

Utility clouds continue to grow in scale and in the complexity of their components and interactions, which introduces a key challenge to failure and resource management for highly dependable cloud computing. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To identify anomalies, we need to monitor the system execution and collect health-related runtime performance data. These data are usually unlabeled and a prior failure history is not always available in production systems, especially for newly deployed or managed utility clouds. In this paper, we present a self-evolving anomaly detection framework with mechanisms for dependability assurance in utility clouds. No prior failure history is required. The detector self-evolves by recursively exploring newly generated verified detection results for future anomaly identification. Statistical learning technologies are exploited in detector determination and working dataset selection. Experimental results in an institute-wide cloud computing system show that the detection accuracy improves as it evolves. With self-evolvement, the detector can achieve 92.1% detection sensitivity and 83.8% detection specificity, which makes it well suitable for building highly dependable utility clouds.

Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform

ABSTRACT Image fusion is the concept to integrate multiple same scene images while drawing out maximum radiometric information from them by avoiding noise and fictional data. The main objective is to improve the radiometric quality of fused image compared to individual images of the same scene. Existing methods are found to be efficient, but if the similar radiometric information is fused into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a fuzzy and stationary discrete wavelet transform (FSDWT)-based image fusion technique is proposed. It decomposes Landsat image into stationary values, and then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, FSDWT injects high-frequency blocks from input images and returns a single Landsat image with maximum radiometric data. Quantitative analysis has clearly demonstrated that FSDWT has better structural detail, spatial resolution and spectral information than existing methods.

AFD: Adaptive failure detection system for cloud computing infrastructures

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructure. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software failures. Autonomic failure detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled, and thus a prior failure history is not always available in production clouds, especially for newly managed or deployed systems. In this paper, we present an Adaptive Failure Detection (AFD) framework for cloud dependability assurance. AFD employs data description using hypersphere for adaptive failure detection. Based on the cloud performance data, AFD detects possible failures, which are verified by the cloud operators. They are confirmed as either true failures with failure types or normal states. AFD adapts itself by recursively learning from these newly verified detection results to refine future detections. Meanwhile, AFD exploits the observed but undetected failure records reported by the cloud operators to identify new types of failures. We have implemented a prototype of the AFD system and conducted experiments in an on-campus cloud computing environment. Our experimental results show that AFD can achieve more efficient and accurate failure detection than other existing schemes.

A Hybrid Anomaly Detection Framework in Cloud Computing Using One-Class and Two-Class Support Vector Machines

Modern production utility clouds contain thousands of computing and storage servers. Such a scale combined with ever-growing system complexity of their components and interactions, introduces a key challenge for anomaly detection and resource management for highly dependable cloud computing. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system level dependability assurance. We propose a new hybrid self-evolving anomaly detection framework using one-class and two-class support vector machines. Experimental results in an institute wide cloud computing system show that the detection accuracy of the algorithm improves as it evolves and it can achieve 92.1% detection sensitivity and 83.8% detection specificity, which makes it well suitable for building highly dependable clouds.

Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring

Air pollutants such as benzene (

Efficient disparity estimation from stereo images using hybrid-guided image filter

\text {C}_6\text {H}_6

Comprehensive review of continuous and discrete orthogonal moments in biometrics

C6H6) have accelerated the rate of cancer among human beings. Currently, atmospheric contamination is measured using spatially separated networks with limited sensors. However, the expenses involving multiple sensors with varying sizes limit the operational efficiency. Therefore, in this paper, a novel multi-objective regression model is proposed to predict benzene concentration in the ambient air pollution data, without need to deploy actual sensors for benzene detection. It is possible because there is a relation among various atmospheric gasses and thus regression can be performed to measure