Big data analytics proceedings of CSI 2015

Advances in Intelligent Systems and Computing 654 V.B. Aggarwal Vasudha Bhatnagar Durgesh Kumar Mishra Editors Big Data Analytics Proceedings of CSI 2015 Advances in Intelligent Systems and Computing Volume 654 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: kacprzyk@ibspan.waw.pl About this Series The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered The list of topics spans all the areas of modern intelligent systems and computing The publications within “Advances in Intelligent Systems and Computing” are primarily textbooks and proceedings of important conferences, symposia and congresses They cover significant recent developments in the field, both of a foundational and applicable character An important characteristic feature of the series is the short publication time and world-wide distribution This permits a rapid and broad dissemination of research results Advisory Board Chairman Nikhil R Pal, Indian Statistical Institute, Kolkata, India e-mail: nikhil@isical.ac.in Members Rafael Bello Perez, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba e-mail: rbellop@uclv.edu.cu Emilio S Corchado, University of Salamanca, Salamanca, Spain e-mail: escorchado@usal.es Hani Hagras, University of Essex, Colchester, UK e-mail: hani@essex.ac.uk László T Kóczy, Széchenyi István University, Győr, Hungary e-mail: koczy@sze.hu Vladik Kreinovich, University of Texas at El Paso, El Paso, USA e-mail: vladik@utep.edu Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwan e-mail: ctlin@mail.nctu.edu.tw Jie Lu, University of Technology, Sydney, Australia e-mail: Jie.Lu@uts.edu.au Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexico e-mail: epmelin@hafsamx.org Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazil e-mail: nadia@eng.uerj.br Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Poland e-mail: Ngoc-Thanh.Nguyen@pwr.edu.pl Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Kong e-mail: jwang@mae.cuhk.edu.hk More information about this series at http://www.springer.com/series/11156 V.B Aggarwal Vasudha Bhatnagar Durgesh Kumar Mishra • Editors Big Data Analytics Proceedings of CSI 2015 123 Editors V.B Aggarwal Jagan Institute of Management Studies New Delhi, Delhi India Durgesh Kumar Mishra Microsoft Innovation Centre Sri Aurobindo Institute of Technology Indore, Madhya Pradesh India Vasudha Bhatnagar Department of Computer Science University of Delhi New Delhi, Delhi India ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-10-6619-1 ISBN 978-981-10-6620-7 (eBook) https://doi.org/10.1007/978-981-10-6620-7 Library of Congress Control Number: 2017952513 © Springer Nature Singapore Pte Ltd 2018 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface The last decade has witnessed remarkable changes in IT industry, virtually in all domains The 50th Annual Convention, CSI-2015, on the theme “Digital Life” was organized as a part of CSI-2015, by CSI at Delhi, the national capital of the country, during December 02–05, 2015 Its concept was formed with an objective to keep ICT community abreast of emerging paradigms in the areas of computing technologies and more importantly looking at its impact on the society Information and Communication Technology (ICT) comprises of three main components: infrastructure, services, and product These components include the Internet, infrastructure-based/infrastructure-less wireless networks, mobile terminals, and other communication mediums ICT is gaining popularity due to rapid growth in communication capabilities for real-time-based applications New user requirements and services entail mechanisms for enabling systems to intelligently process speech- and language-based input from human users CSI-2015 attracted over 1500 papers from researchers and practitioners from academia, industry and government agencies, from all over of the world, thereby making the job of the Programme Committee extremely difficult After a series of tough review exercises by a team of over 700 experts, 565 papers were accepted for presentation in CSI-2015 during the days of the convention under ten parallel tracks The Programme Committee, in consultation with Springer, the world’s largest publisher of scientific documents, decided to publish the proceedings of the presented papers, after the convention, in ten topical volumes, under ASIC series of the Springer, as detailed hereunder: Volume Volume Volume Volume # # # # 1: 2: 3: 4: ICT Based Innovations Next Generation Networks Nature Inspired Computing Speech and Language Processing for Human-Machine Communications Volume # 5: Sensors and Image Processing Volume # 6: Big Data Analytics v vi 10 Preface Volume Volume Volume Volume # # # # 7: Systems and Architecture 8: Cyber Security 9: Software Engineering 10: Silicon Photonics and High Performance Computing We are pleased to present before you the proceedings of the Volume # on “Big Data Analytics” The title “Big Data Analytics” discusses the new models applied for Big Data Analytics It traces the different business interests in the field of Big Data Analytics from the perspective of decision-makers The title also evaluates the uses of data analytics in understanding the need of customer base in various organizations Big data is a new buzzword due to the generation of data from a diversity of sources The volume, variety and velocity of data coming into an organization from both structured and unstructured data sources continue to reach unprecedented levels This phenomenal growth implies that one must not only understand the big data in order to decipher the information that truly counts, but one must also understand the possibilities and opportunities of data analytics Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions With big data analytics, data scientists and others can analyse huge volumes of data that conventional analytics and business intelligence solutions cannot touch The title “Big Data Analytics” analyses the different aspects of big data research and how the same is being applied across organizations to handle their data for decision-making and different types of analytics for different business strategies This volume is designed to bring together researchers and practitioners from academia and industry to focus on extending the understanding and establishing new collaborations in these areas It is the outcome of the hard work of the editorial team, who have relentlessly worked with the authors and steered up the same to compile this volume It will be a useful source of reference for the future researchers in this domain Under the CSI-2015 umbrella, we received over 500 papers for this volume, out of which 74 papers are being published, after a rigorous review processes, carried out in multiple cycles On behalf of organizing team, it is a matter of great pleasure that CSI-2015 has received an overwhelming response from various professionals from across the country The organizers of CSI-2015 are thankful to the members of Advisory Committee, Programme Committee and Organizing Committee for their all-round guidance, encouragement and continuous support We express our sincere gratitude to the learned Keynote Speakers for support and help extended to make this event a grand success Our sincere thanks are also due to our Review Committee Members and the Editorial Board for their untiring efforts in reviewing the manuscripts, giving suggestions and valuable inputs for shaping this volume We hope that all the participants/delegates will be benefitted academically and wish them all the best for their future endeavours Preface vii We also take the opportunity to thank the entire team from Springer, who have worked tirelessly and made the publication of the volume a reality Last but not least, we thank the team from Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, for their untiring support, without which the compilation of this huge volume would not have been possible New Delhi, India New Delhi, India Indore, India March 2017 V.B Aggarwal Vasudha Bhatnagar Durgesh Kumar Mishra The Organization of CSI-2015 Chief Patron Padmashree Dr R Chidambaram, Principal Scientific Advisor, Government of India Patrons Prof S.V Raghavan, Department of Computer Science, IIT Madras, Chennai Prof Ashutosh Sharma, Secretary, Department of Science and Technology, Ministry of Science and Technology, Government of India Chair, Programme Committee Prof K.K Aggarwal, Founder Vice Chancellor, GGSIP University, New Delhi Secretary, Programme Committee Prof M.N Hoda, Director, Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi Advisory Committee • Padma Bhushan Dr F.C Kohli, Co-Founder, TCS • Mr Ravindra Nath, CMD, National Small Industries Corporation, New Delhi • Dr Omkar Rai, Director General, Software Technological Parks of India (STPI), New Delhi • Adv Pavan Duggal, Noted Cyber Law Advocate, Supreme Courts of India • Prof Bipin Mehta, President, CSI • Prof Anirban Basu, Vice President—cum- President Elect, CSI • Shri Sanjay Mohapatra, Secretary, CSI • Prof Yogesh Singh, Vice Chancellor, Delhi Technological University, Delhi • Prof S.K Gupta, Department of Computer Science and Engineering, IIT, Delhi ix x The Organization of CSI-2015 • Prof P.B Sharma, Founder Vice Chancellor, Delhi Technological University, Delhi • Mr Prakash Kumar, IAS, Chief Executive Officer, Goods and Services Tax Network (GSTN) • Mr R.S Mani, Group Head, National Knowledge Networks (NKN), NIC, Government of India, New Delhi Editorial Board • • • • • • • • • • A.K Nayak, CSI A.K Saini, GGSIPU, New Delhi R.K Vyas, University of Delhi, Delhi Shiv Kumar, CSI Vishal Jain, BVICAM, New Delhi S.S Agrawal, KIIT, Gurgaon Amita Dev, BPIBS, New Delhi D.K Lobiyal, JNU, New Delhi Ritika Wason, BVICAM, New Delhi Anupam Baliyan, BVICAM, New Delhi SWOT Analysis of Cloud Computing Environment 735 Table Mapping of weakness and opportunities (W-O) Weakness Opportunities W1 + W6 + W9 W2 + W3 + W5 W4 W7 W8 W10 + W11 O3 O5 O1 O7 O6 O5 + + + + + O5 + O9 + O11 + O12 + O14 + O19 O7 + O8 + O9 + O13 + O14 + O19 O2 + O3 + O9 + O10 + O12 + O14 + O19 O10 + O12 + O13 + O17 O8 + O9 + O12 + O14 + O15 + O19 It can be summarized that many opportunities will be open for the betterment of a cloud computing environment By analyzing the mentioned Strengths and Threats in Table 4, it is analyzed that to reduce the vulnerabilities to external threats we have to concentrate on some strengths of cloud like • • • • • • Security of the cloud should be improved Data sharing, scalability, and availability should be simplified Business agility should be increased Cost should be reduced SLA guarantees the services Heterogeneous environment should be enhanced It can be summarized that improving all the above mentioned threats in turn cloud computing environment will more popular By analyzing the above-mentioned Weakness with Threats in Table 5, it can be said that to establish a self-protective plan to thwart the organization’s weaknesses from making it highly susceptible to external threats are listed below Table Mapping of strength with threats (S-T) Strengths Threats S5 + S6 + S8 + S11 + S14 + S21 S1 + S2 + S3 + S19 + S20 S14 + S17 + S21 S2 + S4 + S8 + S17 S6 + S14 + S15 + S17 S5 + S17 + S18 + S20 S21 S7 + S15 + S16 + S18 S21 S6 + S13 + S20 S10 + S13 S21 T1 T2 T3 T4 T5 T7 T8 T9 T10 T11 T12 + T13 T14 736 S Dubey et al Table Mapping of weakness with threats (W-T) Weaknesses Threats W1 + W6 + W9 W2 + W3 + W5 W4 W7 W10 + W11 + W13 W12 T1 T2 T2 T1 T1 T2 • • • • + + + + + + T4 T3 T4 T2 T2 T4 + + + + + + T12 T9 + T7 + T4 + T3 + T7 + T12 + T13 T13 T5 + T7 + T12 + T13 T5 + T7 T12 Communication between the clouds should be established Global fixed standard should be introduced Bandwidth should be improved Maintenance model should be enhanced It can be summarized that then most of the threats will be vanished for the betterment of a cloud computing environment Conclusion After above-mentioned marathon analysis and discussion on Strength (S), Weakness (W), Opportunity (O), and Threat (T) of cloud computing It can be concluded that cloud computing has yet not reached a maturity that leads it into a fruitful stage However the majority of the key problems with cloud computing have been solved to a certain point that Cloud computing has become very popular for commercial and organizational utilization This does not mean that all the problems listed above have been completely resolved, only that the accordingly risks can be accepted to a certain point Cloud computing is thus still a very popular area for research It can be summarized that although there are few threats and weaknesses in cloud computing, but on the contrary, there are various strengths and opportunities thus this technology is getting popular and the future is cloud This technology will get better day by day as researches will take place References Demarest, G., Wang, R.: Oracle cloud computing An Oracle white paper http://www.oracle com/us/technologies/cloud/oracle-cloud-computing-wp-076373.pdf (2010) Mell, P., Grance, T.: The NIST definition of cloud computing NIST (National Institute of Standards and Technlogy) Special Publication 800-145 Sourabh, J., et al.: SWOT Analysis—Definition, Advantages and Limitations http://www managementstudyguide.com/swot-analysis.htm SWOT Analysis of Cloud Computing Environment 737 Eversoll, L.: SWOT (Strengths, weaknesses, opportunities, threats) http://www.lizeversoll com/2011/02/13/swot-strengths-weaknesses-opportunities-threats/ (2011) http://www.quickmba.com/strategy/swot (2010) Dimitrios, Z., Dimitrios, L.: Addressing cloud computing security issues Future Gener Comput Syst 28, 583–592 (2012) (Elsevier) Tanzim Khorshed, Md., et al.: A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing Future Gener Comput Syst 28, 833–851 (2012) (Elsevier) Khan, A.N., et al.: Towards secure mobile cloud computing: a survey Future Gener Comput Syst 29, 1278–1299 (2013) (Elsevier) Changa, V., et al.: The development that leads to the cloud computing business framework Int J Inf Manag (2013) 10 Marston, S., et al.: Cloud computing—the business perspective Decis Support Syst 51, 176– 189 (2011) A Review on Quality of Service in Cloud Computing Geeta and Shiva Prakash Abstract Cloud Computing is a computing technology that uses remote control servers and Internet to maintain applications and data It is an emerging technology Today’s Cloud computing is a wide area in research and industry It is a term, which involves networking, virtualization, software, web services and distributed computing In cloud computing environment there are various challenges like efficient load balancing, real benefits/business outcome, resource scheduling, datacenter energy consumption, etc Quality of Service (QoS) plays an important role in distributed computing for multimedia and other essential applications Aim of this paper to provide a survey of the QoS modeling approaches and other frame works suitable for cloud systems and describe their implementation details, merits and demerits This paper supports new researchers to be able to understand main techniques used and their limitations in environment of cloud computing for providing QoS Keywords Cloud computing parameters Á Quality of service Á Load balancing Á QoS Introduction Cloud Computing [1] is a computing technology that uses remote control servers and Internet to maintain applications and data It is an emerging technology Cloud provides resources over Internet using multi-tenancy, virtualization technology, web services [2], etc Multi-tenancy is important for developing software as a service (SaaS) application Multi-tenancy allows the same software platform to be Geeta (&) UPTU, Lucknow, India e-mail: geetasingh02@gmail.com S Prakash Department of Computer Science & Engineering, MMMUT, Gorakhpur, India e-mail: shiva.plko@gmail.com © Springer Nature Singapore Pte Ltd 2018 V.B Aggarwal et al (eds.), Big Data Analytics, Advances in Intelligent Systems and Computing 654, https://doi.org/10.1007/978-981-10-6620-7_72 739 740 Geeta and S Prakash shared by multiple applications Virtualization provides abstraction of independent hardware access to each virtual machine applications communicate over the Internet using web services There are three models of cloud computing [3, 4] Software as a Service (SaaS): it is also known as on demand service It is an application that can be accessed from anywhere, any time on the world as long as you can have a computer with an internet connection Platform as a service (PaaS): PaaS providers offer a predefined combination of Operating System and application servers It is a platform for developers to write and create their own SaaS Infrastructure as a Service (IaaS): The Cloud service providers provide computers, as physical or more often as virtual machines Some common examples are Amazon, GoGrid, etc There are four deployment of cloud service-Public Cloud: Public cloud makes services such as computing, storage, application, etc available to general public These services may be free or offered as payment as per public usage Major public cloud providers are Amazon, Google, Microsoft, etc Private Cloud: Private cloud is a cloud infrastructure operated only for a single organization It is not available to general public Community Cloud: Community cloud shared infrastructure between several organization with common concerns such as jurisdiction, compliance, etc Hybrid Cloud: It is a combination of two or more clouds (public, private, or community) In Cloud Computing the term QoS denotes the levels of availability, reliability and performance offered by the infrastructure and by the platform and or an application that hosts it It is fundamental for cloud consumers, who expect cloud providers to deliver the quality features, and for cloud providers, who requisite to find the correct tradeoffs between operational costs and QoS levels However, it is a difficult decision problem to find optimal tradeoff, often enraged by the presence of service level agreements specifying Quality of Service targets and economical penalties associated to violations of Service level agreement (SLA) [5] Rest of paper organized as Sect presents detailed literature review of related work of QoS in environment of cloud computing Section presents comparative study of main works of QoS in environment of cloud computing Finally, presents conclusion in Sect Related Works of Quality of Service in Cloud Computing Quality of Services plays an important role in making the cloud services acceptable to customers in cloud computing Cloud computing systems may crowd thousands of internationally dispersed users at any given time These users may access dissimilar types of services that have different requirements depending on the type of users, resources and services involved [6] Several Authors have put forward their ideas for innovative and new solutions for handling this imperative area is management of resources A Review on Quality of Service in Cloud Computing 741 In [7] Alhamazani et al have described the importance of dynamically monitoring the Quality of Service of virtualized services The Researchers explain the monitoring of the services which would help both the application developer and cloud provider to maximize there turn of their investments in terms of keeping hosted applications and the cloud services operating at peak efficiency, detecting changes in service and application performance, SLA violations, failures of cloud services and other dynamic configuration changes Researchers mainly concentrate on SNMP based QoS monitoring which is a paper describing work in progress The effect of different factors on the QoE of multimedia users have presented by Mushtaq, and Mellouk et al [8] in a cloud computing network (CCN) The Researchers have grouped the factors that affect the Quality of Experience (QoE) into four groups These four groups are characteristics of videos, network parameters, types of user’s profiles and terminal characteristics The data collected through different methods have been classified using machine learning techniques such as Naive Bayes, Support Vector Machines, K-Nearest Neighbors, Decision Tree, Random Forest and Neural Networks and they have determined the best method for Quality of Service (QoS)/Quality of Experience (QoE) correlation after evaluating them The QoE/QoS correlation is used to evaluate the machine learning techniques Hence it can be concluded that this paper describes the capabilities of machine learning techniques In [9], Stoicuta have described a client application for monitoring cloud Quality of Service on iOS5 This application can be used to control the performance of their cloud provider by the client But the designed application has been focusing on available transfer rate and one way delay very narrowly Hence the application has limited applications In [10], Li have proposed a novel for cloud workflow scheduling model The authors have incorporated trust in this model in addition to the QoS targets In order to analyze the user’s requirements and design a customized schedule, the authors proposed two stage workflow model where the macro multiworkflowstage is based on trust and micro single workflow stage classifies workflows into time sensitive and cost sensitive based on QoS demands The fuzzy clustering technique is used to classify the workflows The model restricts the Quality of Service parameters considered to bandwidth, storage, response time, reliability and cost In this model the delivery of QoS is limited only to average values and no guarantee of service delivery is provided at least in terms of a predetermined confidence level This is a strong limitation of the proposed model as the consumers not have the ability to select their own Quality of Service parameters and no guarantee of the Quality of Service delivery at least a statistical validation In [11], Goyal have proposed a trust management model based on QoS In this proposed model Author explains how to use multiple QoS attribute to calculate the trust value, but there is no prioritization between attributes and also there is no clear explanation how these attributes are combined Emeakaroha et al [12] have presented a schedule that takes many SLA attributes for application deployments in the Cloud environment The attributes considered in this application includes network bandwidth, storage capacity, and CPU time for 742 Geeta and S Prakash deploying applications These attributes have limited application in real world as they require to be considered during deployment When the applications have been ready for user access once, the users would be more interested in performance attributes such as processing time, response time etc Hence, in real world business this model may not have much practical significance An optimization framework proposed by Kouki, Ledoux, and Sharrock et al [13] for cross layer cloud services The proposed framework is acceptable for salespersons advertising gathering of facilities and also takes the changeable nature of cloud environment An optimization across various layers has been carried out enforcing the Service Level Agreement dependencies between them The proposal currently faces the problem of run time management of QoS performance Iyer and Veeravalli have proposed and formulated a resource allocation strategy for cloud infrastructure based on bargaining [14] They have combined the Raiffa Bargaining Solution and Nash Bargaining Solution to appear at an optimal allocation strategy This proposal monitors the dynamic environment of cloud very well during run time but it does not permit to manage resources from multiple sources Hence if a single service supplier cannot meet all the desires of the user, he will be demanded to settle a sub optimal allocation of resources Chen and Zhang [15] have proposed a workflow scheduling algorithm which is based on Particle Swarm Optimization (PSO) The proposed method can optimize up to seven attributes specified the users and compared to traditional optimization methods that allow only for the workflow execution time The weakness of the proposed method is lacking of monitoring scheme for catching Quality of Service violations den Bossche et al [16], presented set of heuristics for scheduling deadline constrained applications It is in a cost effective manner in a hybrid cloud system This mechanism attempts to maximize local resources along with minimize the external resources without compromising the QoS requirements of the applications This set of heuristics takes the cost of both data transfer and computation along with the estimated data transfer times The main criteria in optimization is the maximization of cost saving The effect of different cost factors and workload characteristics on the cost savings have been analyzed along with the sensitivity of the results to the different runtime estimates The advantages of the proposed methodology are that an optimized set of resources can be selected from both in private and public cloud systems for meeting the QoS requirements But it suffers from certain weaknesses Though it is considered only the deadline concerned applications, it does not consider the failures that may occur after the scheduling has done The failure will affect the application in terms of quality and increase the cost of execution A generic QoS framework have proposed by Liu et al [17] for Cloud workflow systems which is consists of four components such as QoS aware service selection, QoS requirement specification, QoS violation handling and QoS consistency monitoring However the knowledge sharing and data communication between the components for different Quality of Service dimensions is not suitable for solving difficult problems such as monitoring, multi based service selection and violation handling A Review on Quality of Service in Cloud Computing 743 Some algorithms to equivalence the cost of hardware and SLA violations for resource allocation in cloud for SaaS providers have proposed by Buyya et al [18] These proposed algorithms takes certain Quality of Service attributes such as service initiation time and response time for satisfying the customer’s while minimizing the use of hardware resources All these algorithms are established to reuse the already created Virtual Machines in order to reduce cost and it may create security problems for users as the residual information in the VMs can be used against them QoS ranking prediction frameworks have been proposed for Cloud services by Zheng et al [19], by taking past service usage experiences of users This framework is used to avoid the time consuming, expensive real world service invocations and requires no extra invocations of Cloud facilities when making Quality of Service ranking prediction In this framework Collaborative filtering technique is used to predict Quality of Service for web services, it can is also be used for cloud services This framework is used Pearson Correlation Coefficient to calculate the similarity between users Garg have described a framework in [20], to measure the quality and prioritize Cloud service This framework makes significant impact and creates fresh competition among Cloud providers to satisfy their SLA and enhance their QoS They proposed an Analytical Hierarchical Process based ranking method that can be used to evaluate the Cloud services which is based on various applications depending on Quality of Services requirements The presented method is used for quantifiable QoS attributes such as Assurance of Service, Cost, Accountability, Agility, Performance, Usability, Security, and Privacy It is not suitable for non-quantifiable Quality of Service attributes such as Service Response Time, Transparency, Interoperability, Sustainability, Suitability, Accuracy, Availability, Reliability and Stability A service selection algorithms based on QoS aware have proposed by Ruozhou et al [21] for composing diverse services offered by a Cloud Using virtualization technology various types of resources require to be virtualized as a set of Cloud services Customized Cloud services that occupy not only diverse types of computing services but also computing services of interconnecting networks in the Cloud End-users So, the networking services and Cloud computing services has been modeled as combined customized Cloud service Ani [22–24] considered some QoS constraints, such as deadline, file size, budget, requested length, penalty and rate ratio Penalty Rate Ratio is a ratio for consumer’s compensation if the Software as Service provider misses the deadline The maximum time a consumer would like to wait for the result is called Deadline The size of input file provided by consumers called the Input File Size Budget is the amount customer wishes to pay for the resources Request Length is the Millions of Instructions required to be executed to server request A new system called Cloud Monitoring System (CMS) have described by Chitra et al [25] This is used to improve Quality of Service during Service Level Agreement negotiation The negotiation between users and cloud Service provider’s periodic polling is administered and reports are accomplished in an absolute 744 Geeta and S Prakash manner After detecting the local corrections, each element of network has to eject alarms in order to assure that global parameters are not violated In this monitoring system the failed node can be noticed with the help of monitoring and it gradually improves the efficiency of the cloud and attracts the users More QoS parameters can be considered An algorithm is proposed by Chen et al [26], to help choose data centers and cloud providers in a various cloud environment for example a video service manager Performances are evaluated with various video service workloads Compared with using only one cloud provider, dynamically deploying services in multi cloud is better in aspects of both QoS and cost Comparative Study of Research Work This section summaries main works of QoS in environment of cloud computing as the work discussed above in literature review on the basis of techniques used and their merits and demerits Table Comparison study of research work Sl No Author and reference Proposed model/framework Merits Demerits Buyya [7] SLA oriented resource provisioning for cloud and service computing It does not integrates in combined manner of IaaS, PaaS and SaaS Liu [8] A Generic QoS framework for cloud workflow systems den Bossche [9] A set of Heuristics scheduling deadline constrained workloads on hybrid cloud system Chen [10] Trust-based and QoS demand clustering analysis customizable cloud workflow scheduling strategies It integrates the market based resource provisioning with virtualization technologies for flexible resource allocation Author covers all four stages of cloud workflow in this framework It takes the cost of both data transfer and computation along with estimated data transfer times, different cost factors and workload characteristics In these strategies multiple parameter optimizations are possible In this framework QoS metrics are not identified In this method, failures may occur after the scheduling does not consider No monitoring mechanism is implemented for catching violations (continued) A Review on Quality of Service in Cloud Computing 745 Table (continued) Sl No Author and reference Proposed model/framework Merits Demerits Iyer [11] Resource allocation in a compute cloud through bargaining approach It handles the dynamic nature of cloud during run time Kouki [12] An optimization framework for cross layer cloud services Emeakaroha [13] A heuristic scheduling that takes multiple SLA parameters when deploying applications in cloud Goyal [14] Li and Zhang [15] A QoS based trust management model for cloud infrastructure as a service A set based discrete PSO for cloud workflow scheduling with user defined QoS constraints Suitable for vendors selling products across multiple layers dynamic nature of cloud has been considered Considers deployment attributes as storage capacity network bandwidth and CPU time, before installation of applications in the cloud system It can be used multiple QoS parameters It may lead to sub optimum solutions from a customer’s perspective, if a single provider cannot meet all the requirements There is a problem in the run time management of QoS performance 10 Stoicuta [16] 11 Mustaq [17] 12 Alhamazani [18] An OpenNet Inf-based cloud solution for cross layer quality of service monitoring part using iOS terminal Empirical study based on machine learning method to access the QoS/QoE correlation Cloud monitoring for optimizing the quality of service of hosted applications It does not consider performance parameter as response time, performance time There is no possibility to prioritize the parameters The workflow scheduling mechanism breaking up into multiple stages and grouping the requests of the user requirements It can be used by clients to monitor There is absence of QoS delivery guarantees It has been studied using a selected set of machine learning method The Qos/QoE correlation is a scheme for evaluating the machine learning methods There is no evaluation This work is based on concept and idea only It focuses only on available transfer rate and one way delay as QoS parameters (continued) 746 Geeta and S Prakash Table (continued) Sl No Author and reference Proposed model/framework Merits Demerits 13 Zheng [19] 14 Garg [20] Yu [21] Outperformed rating based schemes and greedy method In this attributes are explained for consumers and providers Virtualization increases QoS by monitoring system It has to be considered accuracy of ranking method In this framework Non quantifiable QoS attributes are not used 15 QoS ranking prediction for cloud services A framework for ranking and comparing cloud services QoS aware service selection in virtualization based cloud computing It is considered single QoS parameter As per Table we see that each of the proposed model/framework have its merit and demerit Explanation of each work is described in Sect Conclusion In this paper we have reviewed current proposed framework in workload and system modeling and applications to cloud QoS management Managing QoS is a difficult job in making such an innovative method to larger customers The findings of the Researchers in terms of the merits and demerits of the reviewed work have been presented in the table given above to find the references easily It can be seen from Table that there is still a lot future work in this exciting and challenging area References Buyya, R.K., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: reality, vision, and hype for delivering computing as the 5th utility J Future Gener Comput Syst 25(6), 599–616 (2009) Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing Mag Commun ACM 53 (4), 50–58 (2010) Buyya, R., Vecchiola, C., Selvi, S.T.: Cloud Computing Architecture, pp 111–140 (2013) Ding, S., Yang, S., Zhang, Y., Liang, C., Xia, C.: Combining Quality of Service prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems Knowl Based Syst 56, 216–225 (2014) Genez, TAL., Bittencourt, L.F., Madeira, E.R.M.: Workflow scheduling for SaaS/PaaS cloud providers considering two SLA levels In: The IEEE/IFIP NOMS (2012) Firdhous, M., Hassan, S., Ghazali, O.: A comprehensive survey on Quality of Service implementations in cloud computing Int J Sci Eng Res (IJSER) 4(5) (2013) A Review on Quality of Service in 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level agreement in Cloud Int J Emerg Technol Adv Eng (IJETAE) 3(3) (2013) 748 Geeta and S Prakash 26 Chen, W., Cao, J.: Quality of service aware virtual machine scheduling for video streaming services in multi cloud Tsinghua Sci Technol 18(1), 308–317 (2013) 27 Chhabra, A., Singh, G., Waraich, E., Sidhu, B., Kumar, G.: Qualitative parametric comparison of load balancing algorithms in parallel and distributed computing environment In: Proceedings of World Academy of Science, Engineering and Technology (PWASET), vol 16 (2006) Association Rule Mining for Finding Admission Tendency of Engineering Student with Pattern Growth Approach Rashmi V Mane and V.R Ghorpade Abstract Association Rule Mining is one of the important techniques in data mining Generation of the rule involves two phases where the first phase finds the frequent itemsets and second phase generates the rule Many algorithms are specified to find frequent item set from the sequential patterns There are mainly two approaches for finding frequent item sets First approach is with candidate sequence generation, i.e., Apriori approach and second is the pattern growth method If the sequence length is less, pattern growth method performs better than that of Apriori approach In this paper, we have analyzed the pattern growth approach for the database of an engineering student With finding associations among the attributes we can find the tendency of taking admission and prioritizing an engineering branch To find strong and valid association rules, different measures like minInterest, lift, leverage, and conviction are considered during finding rules Keywords Association rule mining Á Pattern growth Á Constraint Á Measure Introduction Data mining is an important process where intelligent methods are applied to extract necessary and needed data patterns Data mining is also called as KDD Knowledge discovery in DB is mainly aimed to develop methodologies and tools which can retrieve useful information and knowledge from data required for analysis purpose and for decision making It can provide tools for automation of data analysis R.V Mane (&) Department of Technology, Shivaji University, Kolhapur, Maharashtra, India e-mail: rvm_tech@unishivaji.ac.in V.R Ghorpade D.Y.Patil College of Engineering, Kolhapur, Maharashtra, India e-mail: vijayghorpade@hotmail.com © Springer Nature Singapore Pte Ltd 2018 V.B Aggarwal et al (eds.), Big Data Analytics, Advances in Intelligent Systems and Computing 654, https://doi.org/10.1007/978-981-10-6620-7_73 749 750 R.V Mane and V.R Ghorpade Association rule mining plays a vital role in discovering useful information in most of the applications containing too large data for manual analysis Finding really useful patterns from the data is hard for decision makers Association rule mining is mainly developed to identify the relationships strongly associated among frequent itemsets Association analysis is widely used in transaction data analysis for direct marketing, catalog design, and other decision making Mining association rule can be done in two steps The first step is to generate a set of all frequent itemsets and generate all rules from frequent itemsets This uses a support-confidence framework Frequent pattern mining is one of the widely used techniques for finding frequent subsequence as patterns from a sequence database It has got a wide variety and range of application in various areas as market basket analysis, stock market analysis, biomedical, DNA sequence, telephonic network, etc This problem was first introduced by Agrawal and Srikant [1, 2] Many recent studies have given a number of different ways for mining sequence patterns Frequent pattern mining techniques aim to find all frequent subsequence with input as source sequence and minSupport value as the threshold From these, frequent subsequences relationship among a set of items can be found In this paper, we have analyzed pattern growth approach for finding frequent subsequence The input data is of first year engineering students From the given set of sequence, rules are generated to state the tendency of the student for admitting in a particular branch of engineering Problem Definition For a database D containing a set of transactions with sequences where each element of the sequence represents an attribute of a relation The length of all sequence is same as each attribute represents one item of a sequence Each tuple of a database is with where Sid as Student_id and S represents a sequence with attributes as From the given sequence database, with a minimum support ðminSupÞ as threshold, frequent subsequence patterns are to be mined with pattern growth approach Constraints are pushed in the algorithm to minimize the time Any sequence is said to be frequent if it satisfies Support ð X Þ ! minSup where X is a subsequence and Support ðX Þ represents with which percentile the sequence is present in a given number of transactions or the frequencies of occurring patterns Rules are generated from the set of frequent itemsets For a rule, X ! Y if it satisfies Supp ðX [ Y Þ ! minSup and Supp ðX [ Y Þ Supp ðX Þ ! minConf then it can be extracted as a valid rule ... pleased to present before you the proceedings of the Volume # on Big Data Analytics The title Big Data Analytics discusses the new models applied for Big Data Analytics It traces the different... field of Big Data Analytics from the perspective of decision-makers The title also evaluates the uses of data analytics in understanding the need of customer base in various organizations Big data. .. Mishra • Editors Big Data Analytics Proceedings of CSI 2015 123 Editors V.B Aggarwal Jagan Institute of Management Studies New Delhi, Delhi India Durgesh Kumar Mishra Microsoft Innovation Centre