Indian Institute of Technology Kharagpur

About: Indian Institute of Technology Kharagpur is a education organization based out in Kharagpur, India. It is known for research contribution in the topics: Computer science & Dielectric. The organization has 16887 authors who have published 38658 publications receiving 714526 citations.

Diesel engine emissions and performance from blends of karanja methyl ester and diesel

Abstract: This paper presents the results of investigations carried out in studying the fuel properties of karanja methyl ester (KME) and its blend with diesel from 20% to 80% by volume and in running a diesel engine with these fuels. Engine tests have been carried out with the aim of obtaining comparative measures of torque, power, specific fuel consumption and emissions such as CO, smoke density and NOx to evaluate and compute the behaviour of the diesel engine running on the above-mentioned fuels. The reduction in exhaust emissions together with increase in torque, brake power, brake thermal efficiency and reduction in brake-specific fuel consumption made the blends of karanja esterified oil (B20 and B40) a suitable alternative fuel for diesel and could help in controlling air pollution.

Virtual machine power metering and provisioning

Abstract: Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

Selective Molecular Separation by Interfacially Crystallized Covalent Organic Framework Thin Films

Abstract: Exponential interest in the field of covalent organic frameworks (COFs) stems from the direct correlation between their modular design principle and various interesting properties. However, existing synthetic approaches to realize this goal mainly result in insoluble and unprocessable powders, which severely restrict their widespread applicability. Therefore, developing a methodology for easy fabrication of these materials remains an alluring goal and a much desired objective. Herein, we have demonstrated a bottom-up interfacial crystallization strategy to fabricate these microcrystalline powders as large-scale thin films under ambient conditions. This unique design principle exploits liquid–liquid interface as a platform, allowing simultaneous control over crystallization and morphology of the framework structure. The thin films are grown without any support in free-standing form and can be transferred onto any desirable substrate. The porous (with Tp-Bpy showing highest SBET of 1 151 m2 g–1) and crystal...

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

Abstract: This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.