Abstract

Our computing demands have grown so much that we need a robust distributed computing platform to process data. To feed such data hungry systems, we need an equally robust distributed file systems that span across multiple geographically separate locations. Most distributed file systems break any file into a set of blocks or chunks which are spread across the cluster. The major bottleneck is to identify where to place the blocks and where to place the replicas so that cluster is optimized on certain parameters like disk utilization, minimizing network congestion, maximizing throughput, optimal power utilization, etc. This paper proposes assigning a distance measure for each one of the data sources with respect to the other and placing the blocks on the specified disk on the node that minimizes the total distance of the last few requests made for that block. As the request pattern and parameters change, the distances are updated and the blocks are moved dynamically to minimized the distance, in effect optimizing on the required parameters. The distance function is to be modeled based on the cluster and the parameters you wish to optimize on. It can be a function of just bandwidth or bandwidth and latency or other miscellaneous features like disk utilization, processing power, disk speed, power utilization, cooling requirements, temperature, etc. A detailed performance analysis was carried out with disk bandwidth, network bandwidth and disk utilization as the parameters and the performance is far better in comparison to the reference system (10% or more depending on specification differences) which has no understanding of the different type of disks present and the nature of the cluster. Due to the performance aware nature, the system was inherently able to utilize memory for speeding up performance through the use of in-memory partitions.

Abstract

Current PaaS solutions use an underlying IaaS provider and restrict themselves to a few geographical regions. They operate in one/two zones only and use others for failover or disaster recovery. In this paper, we present the design of MultiPaaS, a PaaS solution which runs on multiple cloud/IaaS providers and leverages the combined global span. Unlike traditional scaling techniques where app servers are scaled up or down in a specific region or data center, we present a design to perform it on a global scale. Origin of the request burst is identified and instances are booted in a data center closest to it. It ensures database persistence and cross-region scalability via a combination of replication techniques. We identify common features available across all IaaS providers and use that as a provider interface to ensure interoperability, but use provider specific extensions to leverage unique offerings and benefits.

Abstract

Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean — content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party Web pages, but the ads in this form are rich multimedia ads — image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user's probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser's side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.

Abstract

As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema. We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.

Abstract

With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21% in terms of energy consumption and in terms of maintaining the SLAs, it performs 60% better than Single Threshold algorithm.

Abstract

Sandhi splitting is the primary task for computational processing of text in Sanskrit and Dravidian languages. In these languages, words can join together with morpho-phonemic changes at the point of joining. This phenomenon is known as Sandhi. Sandhi splitter splits the string of conjoined words into individual words. Accurate execution of sandhi splitting is crucial for text processing tasks such as POS tagging, topic modelling and document indexing. We have tried different approaches to address the challenges of sandhi splitting in Malayalam, and finally, we have thought of exploiting the phonological changes that take place in the words while joining. This resulted in a hybrid method which statistically identifies the split points and splits using predefined character level linguistic rules. Currently, our system gives an accuracy of 91.1%.

Abstract

Fault detection and diagnostics (FDD) is a method to monitor a system, identify when a fault has occurred, and point out the type of fault and its location. This method improves comfort, and reduces the operation, maintenance, and utility costs, thus reducing the environmental impact. In this paper, the design of FDD lab is presented where a user can create different types of faults in Heating, Ventilation and Air-conditioning (HVAC) systems, and develop and test algorithms for the detection and diagnostics of faults. This facility will help identify and analyse the faults pertaining to HVAC systems that are prevalent in India nowadays.

Abstract

Increasing roof reflectance reduces absorption of solar radiation, roof surface temperature and heat ingress into the building. This leads to an increase in indoor comfort and reduction in room air temperature that in turn results in energy saving by reducing AC demand. To assess this impact of cool roofs on buildings in rural and peri-urban areas of India, a study was performed on two un-conditioned school buildings in Hyderabad (ZPH School) and Nagpur (VNHM School). Two classrooms of same size, function, and occupancy from both the school buildings were monitored for a period of ten weeks starting from March 2014 till May 2014. The data from the experiments and their analyses showed promising results. The average reduction and peak reduction in indoor air, roof underdeck and roof overdeck surface temperatures are 2.1 ◦C, 5.0 ◦C and 12.3 ◦C and 4.3 ◦C, 10.0 ◦C and 26.3 ◦C respectively for the room with white roof as opposed to grey roof in ZPH School and 1.5 ◦C, 4.0 ◦C and 9.5 ◦C, and 3.3 ◦C, 4.2 ◦C, and 25.2 ◦C respectively in VNHM School. The results from the experiment prove that white roof has a significant effect in reducing the indoor air temperatures in buildings due to reduction in outdoor and indoor roof surface temperature.

Abstract

This article presents the effect of envelope measures on thermal environmental conditions of a naturally ventilated building block in composite climate of India. Envelope measures such as roof U-value, wall U-value, glass U-value and glass solar heat gain coefficient, roof reflectance and two combinations of these measures (total seven cases) are analysed as recommended by National Energy Conservation Building Code-India (ECBC) through prescriptive route. Subject building is modelled and simulated using dynamic thermal simulation tool. Two approaches of thermal comfort have been used in this study such as heat balance model based on laboratory study and adaptive model of thermal comfort based on field findings. Thermal environmental conditions are evaluated based on operative temperature with and without considering the effect of thermal adaptation. This study found that comfortable temperature …

Abstract

The electric load of an office building can be classified into lighting load, HVAC load, and plug load. Monitoring the lighting and HVAC load is relatively simple. However, the product range of plug load devices is diverse and their usages vary considerably making it difficult to understand plug load usage and consumption. Plug loads account for more than 20 percent of energy consumption in a building. Even though the share of plug load in building energy is increasing, very few studies exist on the device level energy usage and consumption in an academic institution in India. Therefore, the study of plug load is done to gain insight on their usage and energy consumption patterns. A total of 93 energy meters were installed on a sample of plug load devices. For each connected device, the current, voltage, and accumulated power are logged along with the timestamp for a period of 4 months. Different analysis like estimation of annual energy usage, average load profiles on different day types, variability in energy consumption among devices and so on were performed. Based on the analysis, the study recommends strategies to reduce plug load consumption and also suggests load schedules which can be used in energy simulation for realistic energy results.