Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Resource provisioning and scheduling in clouds: QoS perspective

Abstract: Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounter a problem of allocation of resources, which cannot be addressed with existing resource management frameworks. Resource scheduling, if done after efficient resource provisioning, will be more effective and the cloud resources would be scheduled as per the user requirements (QoS) on provisioned resources. Execution of cloud workloads should be as per QoS parameters to fully satisfy the cloud consumer. Therefore, based on QoS parameters, it is mandatory to predict and verify the resource provisioning before actual resource scheduling. In this paper, a resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources. Cloud workloads have been re-clustered using k-means-based clustering algorithm after firstly clustering them through workload patterns to identify the QoS requirements of a workload, and then based on identified QoS requirements resources are provisioned before actual scheduling. Further, scheduling has been done based on different scheduling policies. Finally, the performance of the proposed framework has been evaluated in both real and simulated cloud environment and experimental results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.

Cellulase Immobilization onto Magnetic Halloysite Nanotubes: Enhanced Enzyme Activity and Stability with High Cellulose Saccharification

Abstract: A quest for efficient biotransformation of cellulosic material into sustainable biochemical products for recent biotechnological interventions is currently under way. Herein, we report the fabricat...

Efficient Hierarchical Identity-Based Signature With Batch Verification for Automatic Dependent Surveillance-Broadcast System

Abstract: The automatic-dependent surveillance-broad-cast (ADS-B) is generally regarded as the most important module in air traffic surveillance technology To obtain better airline security, ADS-B system will be deployed in most airspace by 2020, where aircraft will be equipped with an ADS-B device that periodically broadcasts messages to other aircraft and ground station controllers Due to the open communication environment, the ADS-B system is subject to a broad range of attacks To simultaneously implement both integrity and authenticity of messages transmitted in the ADS-B system, Yang et al proposed a new authentication frame based on the three-level hierarchical identity-based signature (TLHIBS) scheme with batch verification, as well as constructing two schemes for the ADS-B system However, neither TLHIBS schemes are sufficiently lightweight for practical deployment due to the need for complex hash-to-point operation or expensive certification management In this paper, we construct an efficient TLHIBS scheme with batch verification for the ADS-B system Our scheme does not require hash-to-point operation or (expensive) certification management We then prove the TLHIBS scheme secure in the random oracle model We also demonstrate the practicality of the scheme using experiments, whose findings indicate that the TLHIBS scheme supports attributes required by the ADS-B system without the computation cost in Chow et al ’s scheme and Yang et al ’s TLHIBS schemes

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

Abstract: Air pollutants such as benzene ( $$\text {C}_6\text {H}_6$$ ) 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 $$\text {C}_6\text {H}_6$$ if the concentration level of other gasses is known. Proposed technique utilizes adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict $$\text {C}_6\text {H}_6$$ density in the air. PSO is employed to enhance the accuracy of ANFIS for runtime parameter tuning by calculating multi-objective fitness function which involves accuracy, root mean squared error and correlation (r). The proposed technique is tested on well-known publicly available air pollution datasets and on real-time primary dataset for quantitative analysis. Experimental results indicate that the proposed method consistently outperforms over available methods to predict $$\text {C}_6\text {H}_6$$ concentration in the atmosphere. Thus, it is well suitable to build self-dependable time and cost-effective benzene prediction model.