Minal Bhise

Service level agreement parameter matching in cloud computing

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like clouds resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

Data Management for Internet of Things

Internet of Things is projected to connect uniquely identifiable devices over the network to build an interactive system with high velocity and volume of data placing forth a challenge of interoperability between such devices. RDF provides a common standard for communication among devices of network and supports powerful data inference. The paper addresses the challenge of handling huge sensor data interactively using RDF. The experiment includes various RDF storage mechanisms such as triple store, property table, vertically and horizontally partitioned table, column store, and data aware hybrid storage. It also shows comparison between vertical partitioning approach and data aware hybrid storage approach for faster data retrieval in IOT systems. The experiment shows 12% of performance improvement using hybrid approach over vertical partitioning approach. It also represents a set of metrics which have been designed to take decision for using appropriate RDF data storage technique beforehand for IOT systems.

Workload Aware Hybrid Partitioning

Real life databases exhibit highly skewed access patterns. These skewed access patterns can be exploited to partition the data considering the query workload. The presented work proposes Workload Aware Hybrid Partitioning (WAHP). WAHP identifies clusters of attributes which are queried together. It identifies workload aware clusters for the actual query workload using a hybrid combination of horizontal and vertical partitioning. The paper demonstrates WAHP experiment using TPC-C benchmark, where 9% of the actual TPC-C data in workload aware clusters, is able to answer 73% of hottest query-workload with an average execution time gain of 37% against original database.

Agro advisory system for cotton crop

In the agricultural domain, the main challenge is to present the new information and research to the farmers so that they can leverage the power of ICT to improve their agricultural practices and thereby the production. Huge amount of agriculture related data like weather data, soil health records, cropping pattern, location specific crop disease and pest are collected from different sources like services, remote satellites, and network of sensors. An agro advisory system presented in this paper helps to bridge the gap between farmers and the agriculture domain experts and developed for the cotton farmers in Gujarat region of India. The system consists of three basic components; Cotton Ontology, Web Services, and Mobile Application Development. The cotton ontology maintains domain knowledge required for answering farmer queries. The ontology contains information regarding crop, soil, cultivation process, disease, pest, and other relevant information. Protégé ontology development tool is used to develop this ontology. Appropriate Web services were built which help interactions with different data sources. The RESTful Web services are programmed in Java using the JAX-RS/Jersey API and the Eclipse EE IDE. The services are developed and deployed on a cloud based application server provided by Heroku. The Web services are invoked from the mobile device and in turn they connect to various data sources like Open Weather API, SQL database and the Ontologies. The farmers can use this application based on very simple android mobile interfaces. The prototype is developed using Java, Android SDK - v14 and Eclipse IDE.

Crop cultivation information system on mobile devices

Mobile devices are used extensively by the people for communication, music, entertainment, Internet and social networking. There is a lack of applications, which can be really useful for the professionals to improve their working capabilities. Though mobile phones are used by people living in rural areas, but there are hardly any relevant applications for them to improve their productivity. In this paper, we have proposed and implemented an information system for farmers which can be operated on their mobile phones. The system is developed using Service Oriented Architecture (SOA) to process spatial data and knowledge base. The knowledge base is maintained in the form of ontologies. The system is an effort to fill the gap between farmers and agricultural experts. A farmer can provide inputs related to crops being cultivated and location specific information to get specific suggestions, alerts and recommendations to improve productivity. It will be generated using the knowledge base. Whenever a farmer observes some anomalous behavior for crops or climate, the system is able to generate recommendations based on inputs provided. We have resolved some of the queries as a part of on-going work and results are displayed on an Android based mobile devices for demonstration of the system.

Allocation of slotted deadline sensitive leases in infrastructure cloud

Resource allocation is an important aspect in cloud computing. In Cloud Computing environment, the user can access required resources in the form of a service. The resource may be a platform, a software or infrastructure. In an IaaS (Infrastructure as a Service) Cloud, users send requests to the cloud-provider in the form of a lease; The cloud-provider makes a scheduling plan for leases in order to maximize the number of leases it can accommodate. A lease stores information about the required resources, including the time at which the resources are required. Haizea is a popular resource lease manager which handles the scheduling of such leases. An algorithm for deadline sensitive leases is presented which accepts more number of leases by dividing a lease into multiple slots and by backfilling already accommodated leases. Experimental results show that our scheduling algorithm gives better performance than existing algorithms in Haizea.

Query Execution for RDF Data on Row and Column Store

This paper shows experimental comparison between various data storage techniques to manage RDF data. The work represents evaluation of query performance in terms of query execution time and data scalability, using row and column store for various data storage techniques. To demonstrate these ideas FOAF Friend Of A Friend data is used. The paper contributes experimental and analytical study for application of partitioning techniques on FOAF data which makes queries 168 times faster compared to traditional triples table. Materialized views over vertically partitioned data show an additional 8 times improvement in query performance against partitioned data for the frequently occurring queries. Vertical partitioning is executed on column store also, and as FOAF data size scales, an order of magnitude improved performance is observed over row store execution.

Query Execution for RDF Data Using Structure Indexed Vertical Partitioning

The paper explores use of various partitioning methods to store RDF data effectively, to meet the needs of extensively growing highly interactive semantic web applications. It proposes a combinational approach of structure index partitioning and vertical partitioning - SIVP and demonstrates the implementation of SIVP. The paper presents five metrics to measure and analyze performance of SIVP store. SIVP is experimented on FOAF and SwetoDBLP datasets. SIVP store have shown an average of 34% gain over vertical partitioning for FOAF dataset. For SwetoDBLP dataset, SIVP have shown an average of 26% gain over VP. SIVP is better than vertical partitioning provided extra time needed in SIVP, which consists of lookup time and merge time, is compensated by frequency of a query higher than breakeven point for that query.

Faster Query Execution for Partitioned RDF Data

This work demonstrates use of Materialized Views to enhance query performance for partitioned RDF data. Given a query, our system determines which views or combinations thereof can be used to answer it. Break- even analysis for the proposed system has been done based on view materialization and refreshment costs. The system performance was evaluated for 7 query types, 3 having Sub-Obj joins. It shows that our approach reduces query response time by an average of 26% for all query types w.r.t response time using just vertical partitioning. Specifically, for queries with Sub-Obj joins, the average reduction is by 37%. On scaling data up 8 times, the reduction changed from 37% to 79% for queries with Sub-Obj joins, and from 26% to 51% on an average for all query types. With the proposed technique, Semantic Web Applications shall be more interactive since queries having Sub-Obj. joins are expected for them.

Semantic search using constrained spread activation for semantic digital library

The work presented includes a prototype that demonstrates semantic search using constrained spread activation for relationship inference for semantic digital library domain. A java applet forms the basic user interface for this prototype. The user submits search query which is expanded using semantic digital library domain ontology. Based on the expanded query, initial nodes are activated and the activation is made to travel to other nodes using the constrained spread algorithm. The spread is constrained by making use of the distance constraint, which is supplied by the user. . The semantic search proposed here is the combination of spread activation techniques with traditional search engines techniques to obtain its results.