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2016 | Book

Teaching Computer Science in the Community Read chapter Read first chapter

Distributed Computing and Internet Technology

12th International Conference, ICDCIT 2016, Bhubaneswar, India, January 15-18, 2016, Proceedings

Editors: Nikolaj Bjørner, Sanjiva Prasad, Laxmi Parida

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book constitutes the proceedings of the 12th International Conference on Distributed Computing and Internet Technology, ICDCIT 2016, held in Bhubaneswar, India, in January 2016.

The 6 full papers, 7 short papers and 11 poster papers presented in this volume were carefully reviewed and selected from 129 submissions. The ICDCIT conference focusses on distributed computing, internet technologies, and societal applications. The book also contains 3 full paper invited talks.

Table of Contents

Frontmatter

Invited Talks

Frontmatter
Teaching Computer Science in the Community
Abstract
The School of Computer Science at Tel Aviv University, Israel, has initiated and carried out a project titled “Teaching Computer Science in the Community". The project aims to introduce scientific thinking and basic computer science concepts in an informal setting to school children from low socio-economic background. The project is implemented as a single semester undergraduate elective course, in which third year computer science students teach in schools and community centers. Here, we describe the spirit, content, and structure of the course and discuss insight we have gained over the last four years of teaching it.
Benny Chor, Assaf Zaritsky
Attacks in the Resource-as-a-Service (RaaS) Cloud Context
Abstract
The Infrastructure-as-a-Service (IaaS) cloud is evolving towards the Resource-as-a-Service (RaaS) cloud: a cloud which requires economic decisions to be taken in real time by automatic agents. Does the economic angle introduce new vulnerabilities? Can old vulnerabilities be exploited on RaaS clouds from different angles? How should RaaS clouds be designed to protect them from attacks? In this survey we analyze relevant literature in view of RaaS cloud mechanisms and propose directions for the design of RaaS clouds.
Danielle Movsowitz, Orna Agmon Ben-Yehuda, Assaf Schuster
Trustworthy Self-Integrating Systems
Abstract
Patients in intensive care often have a dozen or more medical devices and sensors attached to them. Each is a self-contained system that operates in ignorance of the others, and their integrated operation as a system of systems that delivers coherent therapy is performed by doctors and nurses. But we can easily imagine a scenario where the devices recognize each other and self-integrate (perhaps under the guidance of a master “therapy app”) into a unified system. Similar scenarios can be (and are) envisaged for vehicles and roads, and for the devices and services in a home. These self-integrating systems have the potential for significant harm as well as benefit, so as they integrate they should adapt and configure themselves appropriately and should construct an “assurance case” for the utility and safety of the resulting system. Thus, trustworthy self-integration requires autonomous adaptation, synthesis, and verification at integration time, and this means that embedded automated deduction (i.e., theorem provers) will be the engine of integration.
John Rushby

Contributed Papers

Frontmatter
HiRE - A Heuristic Approach for User Generated Record Extraction
Abstract
User Generated Content extraction is the extraction of user posts, viz., reviews and comments. Extraction of such content requires the identification of their record structure, so that after the content is extracted, proper filtering mechanisms can be applied to eliminate the noises. Hence, record structure identification is an important prerequisite step for text analytics. Most of the existing record structure identification techniques search for repeating patterns to find the records. In this paper, a heuristic based approach is proposed. This method uses the implicit logical organization present in the records and outputs the record structure.
S. Chandrakanth, P. Santhi Thilagam
Optimization of Service Rate in a Discrete-Time Impatient Customer Queue Using Particle Swarm Optimization
Abstract
This paper investigates a discrete-time balking, reneging queue with Bernoulli-schedule vacation interruption. Particle swarm optimization which is a biologically inspired optimization technique mimicking the behavior of birds flocking or fish schooling is implemented to determine the optimum service rate that minimizes the total expected cost function per unit time. A potential application of the considered queueing problem in an inbound email contact center is also presented.
Pikkala Vijaya Laxmi, Kanithi Jyothsna
A Wait-Free Stack
Abstract
In this paper, we describe a novel algorithm to create a concurrent wait-free stack. To the best of our knowledge, this is the first wait-free algorithm for a general purpose stack. In the past, researchers have proposed restricted wait-free implementations of stacks, lock-free implementations, and efficient universal constructions that can support wait-free stacks. The crux of our wait-free implementation is a fast pop operation that does not modify the stack top; instead, it walks down the stack till it finds a node that is unmarked. It marks it but does not delete it. Subsequently, it is lazily deleted by a cleanup operation. This operation keeps the size of the stack in check by not allowing the size of the stack to increase beyond a factor of W as compared to the actual size. All our operations are wait-free and linearizable.
Seep Goel, Pooja Aggarwal, Smruti R. Sarangi
Influential Degree Heuristic for RankedReplace Algorithm in Social Networks
Abstract
Influence maximization is to identify a subset of nodes at which if the information is released, the information spread can be maximized. Faisan and Bhavani [7] proposed incorporating greedy selection in the initialization step of RankedReplace algorithm of Charu Aggarwal et al. which would speed up the algorithm. We propose to improve this algorithm further by considering novel heuristic called influential degree for selection of the initial set. The experiments are carried out on small as well as large data sets like DBLP and the results show that RRID and its variations perform quite well on all the data sets quite efficiently reducing the time taken and retaining, and in a few cases, obtaining much better influence spread than the original RankedReplace algorithm.
Jatharakonda Mallesham, S. Durga Bhavani
An Efficient Task Consolidation Algorithm for Cloud Computing Systems
Abstract
With the increasing demand of cloud computing, energy consumption has drawn enormous attention in business and research community. This is also due to the amount of carbon footprints generated from the information and communication technology resources such as server, network and storage. Therefore, the first and foremost goal is to minimize the energy consumption without compromising the customer demands or tasks. On the other hand, task consolidation is a process to minimize the total number of resource usage by improving the utilization of the active resources. Recent studies reported that the tasks are assigned to the virtual machines (VMs) based on their utilization value on VMs without any major concern on the processing time of the tasks. However, task processing time is also equal important criteria. In this paper, we propose a multi-criteria based task consolidation algorithm that assigns the tasks to VMs by considering both processing time of the tasks and the utilization of VMs. We perform rigorous simulations on the proposed algorithm using some randomly generated datasets and compare the results with two recent energy-conscious task consolidation algorithms, namely random and MaxUtil. The proposed algorithm improves about 10 % of energy consumption than the random algorithm and about 5 % than the MaxUtil algorithm.
Sanjaya K. Panda, Prasanta K. Jana
Storage Load Control Through Meta-Scheduler Using Predictive Analytics
Abstract
The gap between computing capability of servers and storage systems is ever increasing. Genesis of I/O intensive applications capable of generating Gigabytes to Exabytes of data has led to saturation of I/O performance on the storage system. This paper provides an insight on the load controlling capability on the storage system through learning algorithms in a Grid Computing environment. Storage load control driven by meta schedulers and the effects of load control on the popular scheduling schemes of a meta-scheduler are presented here. Random Forest regression is used to predict the current response state of the storage system and Auto Regression is used to forecast the future response behavior. Based on the forecast, time-sharing of I/O intensive jobs is used to take proactive decision and prevent overloading of individual volumes on the storage system. Time-sharing between multiple synthetic and industry specific I/O intensive jobs have shown to have superior total completion time and total flow time compared to traditional approaches like FCFS and Backfilling. Proposed scheme prevented any down time when implemented with a live NetApp storage system.
Kumar Dheenadayalan, V. N. Muralidhara, Gopalakrishnan Srinivasaraghavan
A Distributed Approach Based on Maximal Far-Flung Scalar Premier Selection for Camera Actuation
Abstract
The article proposes a distributed approach inspired by maximal far-flung scalar premier selection for actuation of cameras. This manner of scalar premier selection reduces the possible overlapping among the field of views of cameras, thereby minimizing the amount of redundant data transmission due to it. The scalar premiers communicate their corresponding cameras regarding the occurring event information and the cameras collaboratively decide which among them are to be actuated. Experimental results obtained from the investigation validate the significance of our proposed algorithm as compared to three other methods proposed in the literature.
Sushree Bibhuprada B. Priyadarshini, Suvasini Panigrahi
An Extension to UDDI for the Discovery of User Driven Web Services
Abstract
Service registries are used by web service providers to publish services and registries are used by requestors to find them in an SOA (Service Oriented Architecture). The following drawbacks are presented in the main existing service registry specifications, UDDI (Universal Description, Discovery and Integration). First, only abstract, unscalable and inefficient definition of the web services publications is present in all UBR (Universal Business Registry) nodes. Second, it matches only the business name and service name given in the WSDL document to collect service information. In order to overcome these difficulties, author have proposed an efficient and effective UDDI architecture called E-UDDI, which extends the UDDI design by incorporating a QoS in additional bag in the business entity data structure. Moreover, to enable service customer for easily finding more appropriate service information, an effective service matching mechanism is adopted in the E-UDDI so that the user can take the decisions. Service discovery and publishing is improved considerably in the proposed system by means of an effective UDDI registry with flexible and more suitable service searching facility.
Anu Saini
Long Wire Length of Midimew-Connected Mesh Network
Abstract
Minimal DIstance MEsh with Wrap-around links (Midimew) connected Mesh Network (MMN) is a hierarchical interconnection network consists of several Basic Modules (BM), where the BM is a 2D-mesh network and the higher level network is a midimew network. In this paper, we present the architecture of MMN and evaluate the number of long wires, length of a long wire, and the total length for the long wire of MMN, TESH, and torus networks. It is shown that the proposed MMN possesses simple structure and moderate wire length. The long wire length of MMN is slightly higher than TESH network and far lower than that of 2D torus network. Overall performance suggests that, MMN is a good choice for future generation massively parallel computers.
M. M. Hafizur Rahman, Rizal Mohd Nor, Md. Rabiul Awal, Tengku Mohd Bin Tengku Sembok, Yasuyuki Miura
K-means and Wordnet Based Feature Selection Combined with Extreme Learning Machines for Text Classification
Abstract
The incredible increase of online documents in digital form on the Web, has renewed the interest in text classification. The aim of text classification is to classify text documents into a set of pre-defined categories. But the poor quality of features selection, extremely high dimensional feature space and complexity of natural languages become the roadblock for this classification process. To address these issues, here we propose a k-means clustering based feature selection for text classification. Bi-Normal Separation (BNS) combine with Wordnet and cosine-similarity helps to form a quality and reduce feature vector to train the Extreme Learning Machine (ELM) and Multi-layer Extreme Learning Machine (ML-ELM) classifiers. For experimental purpose, 20-Newsgroups and DMOZ datasets have been used. The empirical results on these two benchmark datasets demonstrate the applicability, efficiency and effectiveness of our approach using ELM and ML-ELM as the classifiers over state-of-the-art classifiers.
Rajendra Kumar Roul, Sanjay Kumar Sahay
Language Identification and Disambiguation in Indian Mixed-Script
Abstract
The algorithm that has been proposed in this paper tries to segregate words from various languages (namely Hindi, English, Bengali and Gujarati) and provide relevant replacements for the misspelled or unknown words in a given query. Thus, generating a relevant query in which the original language of each word is known. First, the words are matched directly with the dictionaries of each language transliterated into English. And then, for those that do not match, a set of probable words from all the dictionaries taking words that are closest to the given spelling is shortlisted using the Levenshtein algorithm. After this, to achieve a higher level of generalization, we use a list of probabilities of doublets and triplets of words occurring together that are computed from a training database. The probabilities computed further determine the relevance of those words in the given text allowing us to pick the most relevant match.
Bhumika Gupta, Gaurav Bhatt, Ankush Mittal
A Dynamic Priority Based Scheduling Scheme for Multimedia Streaming Over MANETs to Improve QoS
Abstract
In MANETs, delay and loss of packets need to be reduced in order to provide a good quality of multimedia data transmission over MANETs. To achieve this, we propose a Priority Based Mapping Method, which provides priority in the order of I (intra coded), P (predictive coded) and B (bidirectional predictive coded) frame packets. In addition, our approach handles the expiry time of the packets as well as damaged acknowledgement of the packets/frames. We validate our approach through simulations.
Syed Jalal Ahmad, V. S. K. Reddy, A. Damodaram, P. Radha Krishna
Improved Bug Localization Technique Using Hybrid Information Retrieval Model
Abstract
The need of bug localization tools and increased popularity of text based IR models to locate the source code files containing bugs is growing continuously. Time and cost required for fixing bugs can be considerably minimized by improving the techniques of reducing the search space from few thousand source code files to a few files. The main contribution of this paper is to propose a Hybrid model based on two existing IR models (VSM and N-gram) for bug localization. In the proposed hybrid model performance is further improved by using word based bigrams. We have also introduced a weighing factor beta β to calculate the weighted sum of unigram and bigram and analyzed its accuracy for values ranging from (0–1). Using TopN, MRR and MAP measures, we have conducted experiments which show that the proposed hybrid model outperforms some existing state-of-art bug localization techniques.
Alpa Gore, Siddharth Dutt Choubey, Kopal Gangrade
HGASA: An Efficient Hybrid Technique for Optimizing Data Access in Dynamic Data Grid
Abstract
Grid computing uses computers that are distributed across various geographical locations in order to provide enormous computing power and massive storage. Scientific applications produce large quantity of sharable data which requires efficient handling and management. Replica selection is one of the data management techniques in grid computing and is used for selecting data from large volumes of distributed data. Replica selection is an interesting data access problem in data grid. Genetic Algorithms (GA) and Simulated Annealing (SA) are two popularly used evolutionary algorithms which are different in nature. In this paper, a hybrid approach which combines Genetic Algorithm with Simulated Annealing, namely, HGASA, is proposed to solve replica selection problem in data grid. The proposed algorithm, HGASA, considers security, availability of file, load balance and response time to improve the performance of the grid. GridSim simulator is used for evaluating the performance of the proposed algorithm. The results show that the proposed algorithm, HGASA, outperforms Genetic Algorithms (GA) by 9 % and Simulated Annealing (SA) by 21 % and Ant Colony Optimization (ACO) by 50 %.
R. Kingsy Grace, R. Manimegalai
Energy Efficient SNR Based Clustering in Underwater Sensor Network with Data Encryption
Abstract
In Under Water Sensor Network (UWSN), design of clustering protocol is challenging due to the energy constrained sensor nodes. Therefore, energy saving is considered to be an important issue. In this paper, a new clustering protocol is proposed which is named as energy efficient SNR based clustering in UWSN with Data Encryption (EESCDE). Using this, one percent improvement in the residual energy as compared to the algorithm ESRPSDC is achieved.
Sahu Bandita, Khilar Pabitra Mohan
Collaborative Access Control Mechanism for Online Social Networks
Abstract
Online Social Networks (OSNs) offer an attractive mean for digital social interactions and information sharing among the users. At the same time, it raises a number of security and privacy issues. Especially, there is no efficient mechanism to enforce privacy over data associated with multiple users. This paper proposes a privacy preserving mechanism to allow the users to control access of their shared resources in a collaborative manner. We have developed a Facebook application “msecure” and made a ‘survey based user study’ of the app with a user base of (n = 50). The results of the study reveals popularity of it among users. The study indicates that users are still concerned about the privacy of their shared contents and they believe that a tool like “msecure” could be useful for managing their shared images and other shared contents.
Nemi Chandra Rathore, Prashant Shaw, Somanath Tripathy
i-TSS: An Image Encryption Algorithm Based on Transposition, Shuffling and Substitution Using Randomly Generated Bitmap Image
Abstract
In the digitalized era, an enormous amount of digital images are being shared over the different networks and also available in different storage mediums. Internet users enjoy this convenient way of sharing images and at the meantime, they need to face the consequences like chosen plain-text, statistical, differential attacks, and brute-force attack. These attacks and noises create the need of enhancing the image information security. An image encryption algorithm needs to be robust. An image encryption algorithm (i-TSS) based on transposition, shuffling, and substitution is presented in this paper, that provides better security to the image. This algorithm is implemented using Java. By assessing the results of image quality metrics, this algorithm proves to be secured and robust against the external attacks.
Kanagaraj Narayanasamy, Padmapriya Arumugam
A Type System for Counting Logs of Multi-threaded Nested Transactional Programs
Abstract
We present a type system to estimate an upper bound for the resource consumption of nested and multi-threaded transactional programs. The resource is abstracted as transaction logs. In comparison to our previous work on type and effect systems for Transactional Featherweight Java, this work exploits the natural composition of thread creation to give types to sub-terms. As a result, our new type system is simpler and more effective than our previous one. More important, it is more precise than our previous type system. We also show a type inference algorithm that we have implemented in a prototype tool.
Anh-Hoang Truong, Dang Van Hung, Duc-Hanh Dang, Xuan-Tung Vu
Proactive Resource Provisioning Model for Cloud Federation
Abstract
Cloud federation addresses the resource scalability issue by enabling infrastructure sharing among multiple clouds. We propose a proactive resource provisioning model for federation based on sliding window prediction technique. We compare the results of the proposed prediction mechanism with the commonly used time series prediction algorithm ARIMA. We developed a simulation environment for cloud federation to investigate the impact of workload prediction based resource provisioning in cloud federation. Finally we compare it with that of resource provisioning without prediction in a federated environment, evaluate the profit and resource utilization associated with both the cases.
Geethapriya Ramakrishnan, Prashant Anantharaman, Saswati Mukherjee
A Multiclass SVM Classification Approach for Intrusion Detection
Abstract
As the number of threats to the computer network and network-based applications is increasing, there is a need for a robust intrusion detection system that can ensure security against threats. To detect and defend against a specific attack, the pattern of the attack should be known a priori. Classification of attacks is a useful way to identify the unique patterns of different type of attack. As a result, KDDCup99, NSLKDD and GureKDD datasets are used in this experiment to improve the learning process and study different attack patterns thoroughly. This paper proposed a multi-class Support Vector Machine classifier(MSVM), using one versus all method, to identify one attack uniquely, which in turn helps to defend against the known as well as unknown attacks. Experimentally, the proposed scheme provides better detection accuracy, fewer false positives, and lesser training and generalization error in comparison to the existing approach.
Santosh Kumar Sahu, Sanjay Kumar Jena
Dynamic Data Replication Across Geo-Distributed Cloud Data Centres
Abstract
Cloud computing is being used for data-intensive computing for enterprise and scientific applications that process large data sets originating from globally distributed data centers. In this work, we propose a system model for multiple data centers cooperating to serve a client’s request for data and to identify data centers which can provide the fastest response time to a client. Further, dynamic data replication strategy across geo-distributed data centers based on popularity is detailed. Simulation results are presented and the performance evaluation shows that our method consistently maintains the replica count to an optimal value.
D. S. Jayalakshmi, T. P. Rashmi Ranjana, Srinivasan Ramaswamy
Trust Based Target Coverage Protocol for Wireless Sensor Networks Using Fuzzy Logic
Abstract
Wireless sensor network constitute a class of real time embedded systems having limited resources. Target coverage problem is concerned with the continuous monitoring of a set of targets such that the network lifetime is maximized with the consideration of resource constraints. In this paper we propose a node scheduling protocol for target coverage problem on the basis of node contribution, coverage probability and trust values, where the set covers are computed dynamically using time stamping. The time stamping is a factor of threshold of the coverage level. We have evaluated the performance of the proposed protocol by varying the number of nodes and targets. The results show that the proposed scheme improves the network lifetime in terms of energy consumption and the reliability of the data communicated in comparison to the naïve approach in which all the nodes are activated at once. The results show that the network lifetime is proportional to the energy savings under a constant environment.
Pooja Chaturvedi, A. K. Daniel
An Internet of Things Based Software Framework to Handle Medical Emergencies
Abstract
A software framework is a reusable design that requires various software components to function, almost out of the box. To specify a framework, the creator must specify the different components that form the framework and how to instantiate them. Also, the communication interfaces between these various components must be defined. In this paper, we propose such a framework based on the Internet of Things (IoT) for developing applications for handling emergencies of some kind. We demonstrate the usage of our framework by explaining an application developed using it. The application is a system for tracking the status of autistic students in a school and also for alerting their parents/care takers in case of an emergency.
K. G. Srinivasa, Kartik S. Gayatri, Maaz Syed Adeeb, Nikhil N. Jannu
Minimal Start Time Heuristics for Scheduling Workflows in Heterogeneous Computing Systems
Abstract
Heterogeneous computing systems require efficient task-to-processor mapping for attaining high performance. Scheduling workflows on heterogeneous environments is shown to be NP-Complete. Several heuristics were developed to attain minimum schedule lengths. However, these algorithms employ level-wise approach of scheduling tasks. This indirectly assigns higher priority to the tasks at lower levels than those at higher levels. Further, the start time of tasks at higher levels is constrained by the completion times of tasks at lower levels. The present work proposes a novel heuristic based global scheduling algorithm namely Minimal Start Time (MST) algorithm for workflows. The proposed approach focuses on minimizing the start times of tasks which are dependent on the tasks at lower levels to generate shorter span schedules. The primary merit of this scheme is due to the elimination of level constraints whenever there are no dependency constraints. The performance of MST algorithm is evaluated in terms of normalized makespan, speedup, efficiency and improvement of 5–20 % in 80 % of the cases is achieved in comparison to the earlier work.
D. Sirisha, G. VijayaKumari
FC-LID: File Classifier Based Linear Indexing for Deduplication in Cloud Backup Services
Abstract
Data deduplication techniques are optimal solutions for reducing both bandwidth and storage space requirements for cloud backup services in data centers. During deduplication process, maintaining an index in RAM is a fundamental operation. Very large index needs more storage space. It is hard to put such a large index totally in RAM and accessing large disk also decreases throughput. To overcome this problem, index system is developed based on File classifier based Linear Indexing Deduplication called FC-LID which utilizes Linear Hashing with Representative Group (LHRG). The proposed Linear Index structure reduces deduplication computational overhead and increases deduplication efficiency.
P. Neelaveni, M. Vijayalakshmi
Backmatter
Metadata
Title
Distributed Computing and Internet Technology
Editors
Nikolaj Bjørner
Sanjiva Prasad
Laxmi Parida
Copyright Year
2016
Electronic ISBN
978-3-319-28034-9
Print ISBN
978-3-319-28033-2
DOI
https://doi.org/10.1007/978-3-319-28034-9

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