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i-manager's Journal on Cloud Computing (JCC)

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An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
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Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

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Volume 2 Issue 4 August - October 2015

Research Paper

Energy Efficient Resource Scheduling Framework for Cloud Computing

Kamalpreet Kaur* , Kanwalvir Singh Dhindsa**

* PG Scholar, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.

** Associate Professor, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.

Kaur, K., and Dhindsa, K. S. (2015). Energy Efficient Resource Scheduling Framework for Cloud Computing. i-manager’s Journal on Cloud Computing, 2(4), 1-15. https://doi.org/10.26634/jcc.2.4.4904

Abstract

Cloud computing has an intricate connection to grid computing. Cloud is a large collection of simply functional and available virtualized resources. Resource Scheduling is a way of determining the schedule on which, the activities are performed. Resource scheduling is a complicated task in a cloud environment because of heterogeneity of the computing resources. The most important objective of the cloud scheduler is scheduling the resources successfully and economically. There are two existing techniques for resource scheduling i.e. power-aware and non-power aware. Power aware technique minimizes the power consumption as compared to the non-power aware technique. The proposed technique is used to overcome the limitations of the existing techniques. The proposed technique gives a better result by reducing the total execution time, power consumption and the number of SLA violation as compared to the existing techniques.

References

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[3]. Sukhpal Singh and Inderveer Chana, (2015). “QoSaware Autonomic Resource Management in Cloud Computing: A Systematic Review”. ACM Computing Surveys , Vol. 48, No. 3, pp. 1-46.

[4]. Beloglazov A., Abawajy J., and Buyya R., (2012). “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing”. Future Generation Computer Systems, Vol. 28, pp. 755- 768.

[5]. Sukhpal Singh and Inderveer Chana, (2015). “Qaware: Quality of service based cloud resource provisioning”. Computers & Electrical Engineering Journals, Elsevier, Vol. 47, pp.138-160. DOI: http:// dx.doi.org/10.1016/j. compeleceng.2015.02.003.

[6]. Sukhpal Singh and Inderveer Chana, (2015). “QRSF: QoS-aware resource scheduling framework in cloud computing”, The Journal of Supercomputing, Vol. 71, No. 1, pp. 241-292.

[7]. Calheiros R.N., Ranjan R., Rose C.A.F., and Buyya.R, (2011). “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms”, Vol. 41, No. 1, pp. 23- 50.

[8]. Chen W., and Cao J., (2013). “QoS-aware Virtual Machine Scheduling for Video Streaming Services in Multi- Cloud”, Tsingua Science and Technology, Vol.18, No.1, pp. 308-317.

[9]. Choudhary S.K., Jadoun R.S., Mandoriya H.L., and Kumar A., (2014). “Latest development of cloud computing technology, characteristics, challenge, services & applications”, IOSR Journal of Computer Engineering, Vol. 6, No. 6, pp. 57-68.

[10]. Sukhpal Singh and Inderveer Chana, (2016). “A Survey on Resource Scheduling in Cloud Computing Issues and Challenges”. Journal of Grid Computing, Vol. 14, No. 2, pp. 217-264.

[11]. Sukhpal Singh and Inderveer Chana, (2016). “Resource Provisioning and Scheduling in Clouds: QoS Perspective”. The Journal of Supercomputing, Vol. 72, No. 3, pp. 926-960.

[12]. Sukhpal Singh and Inderveer Chana, (2015). “EARTH: Energy-aware Autonomic Resource Scheduling in Cloud Computing”. Journal of Intelligent and Fuzzy Systems Systems, Preprint: 1-17, IOS Press DOI: http://dx.doi.org/10.3233/IFS-151866, Vol. 30, No. 3, pp. 1581-1600.

[13]. Han H., Deyu Q., Zheng W, and Bin F., (2013). “A QoS Guided task Scheduling Model in cloud computing environment”. Fourth International Conference on Emerging Intelligent Data and Web Technologies, China, pp. 72-76.

[14]. Sukhpal Singh, Inderveer Chana and Rajkumar Buyya, (2015). “Agri-Info: Cloud Based Autonomic System for Delivering Agriculture as a Service”. Technical Report CLOUDS-TR-2015-2, Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, pp. 1-31.

[15]. Kumar R., Sahoo G., (2014). “Cloud Computing Simulation Using CloudSim”. International Journal of Engineering Trends and Technology, Vol. 8, No. 2, pp.1-5.

[16]. Inderveer Chana and Sukhpal Singh, (2014). “Quality of Service and Service Level Agreements for Cloud Environments: Issues and Challenges”. Cloud Computing-Challenges, Limitations and R&D Solutions, pp. 51-72, Springer International Publishing.

[17]. Li W., Wu J., Zhang Q., Hu K, and Li J., (2014). “Trustdriven and QoS demand clustering analysis based cloud workflow scheduling strategies”. Springer Science+ Business Media, New York, pp.1013-1030.

[18]. Sukhpal Singh and Inderveer Chana, (2016). “Cloud Resource Provisioning: Survey, Status and Future Research Directions”. Knowledge and Information Systems, pp. 1-65.

[19]. Sukhpal Singh and Inderveer Chana, (2015). “QoS aware Autonomic Cloud Computing for ICT”. In the Proceedings of International Conference on Information and Communication Technology for Sustainable Development (ICT4SD - 2015), Ahmedabad, India, Vol. 3, No. 4 July, 2015, Springer International Publishing.

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[32]. Kamalpreet Kaur and Kanwalvir Singh Dhindsa, (2015). “Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing”. imanager’s Journal on Cloud Computing, 2(3), May-Jul 2015, Print ISSN 2349-6835, E-ISSN 2350-1308, pp. 8-18.

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Research Paper

An Enhanced Framework to Design Elastic and Reliable Content Based Publish/Subscribe System

Suresh* , P.R. Rajesh, lalitha.B*

* PG Scholar, Department of Computer Science Engineering, SKU College of Engineering, Anantapur, India.

Lecturer, Department of Computer Science Engineering, SKU College of Engineering, Anantapur, India.

* Assistant Professor, Department of Computer Science Engineering, JNTUA College of Engineering, Anantapur, India.

Suresh, M., Rajesh, P. R., and Lalitha (2015). An Enhanced Framework to Design Elastic and Reliable Content Based Publish/Subscribe System. i-manager’s Journal on Cloud Computing.,2(4), 16-22. https://doi.org/10.26634/jcc.2.4.4905

Abstract

Publish/subscribe systems implemented as a service in cloud computing infrastructure provides elasticity and simplicity in composing distributed applications. Appropriate service provisioning in distributed computing infrastructure is an exigent task. Due to the dynamic changes in the rate of the live content arrival in the large scale subscription, it presents a challenge to the existing publish/subscribe systems. This paper proposes ESCC (Elastic and Scalable Content based Cloud Pub/Sub System) technique that presents a framework to design elastic and reliable Content based publish/subscribe system that the uses a single hop lookup overlay to reduce the latency in a cloud computing environment. ESCC dynamically adjust the scale of the servers depending on the churn workloads. ESCC achieves high throughput rate when compared to various workloads.

References

[1]. M. Gjoka, M. Kurant, C.T. Butts, and A. Markopoulou, (2010). “Walking in Facebook: A Case Study of Unbiased Sampling of OSNs”. In International Conference on Computer Communications, INFOCOM, pp. 1–9.

[2]. P.T. Eugster, P. Felber, R. Guerraoui, and A.-M. Kermarrec, (2003). “The Many Faces of publish/ subscribe”. ACM Computing Surveys (CSUR), Vol. 35, No. 2, pp. 114–131.

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[8]. L. Opyrchal, M. Astley, J. Auerbach, G. Banavar, R. Strom, and D. Sturman, (2000). “Exploiting IP Multicast in Content-based Publish–Subscribe Systems”. In IFIP/ACM International Conference on Distributed Systems Platforms, pp. 185–207.

[9]. A. Riabov, Z. Liu, J.L. Wolf, P.S. Yu, and L. Zhang, (2002). “ Clustering Algorithms for Content-based n d Publication–Subscription Systems”. In IEEE 22 International Conference on Distributed Computing Systems, ICDCS, pp. 133–142.

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[12]. A. Carzaniga, M.J. Rutherford, and A.L. Wolf, (2004). “A Routing Scheme for Content-based Networking”. In IEEE International Conference on Computer Communications, INFOCOM.

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Research Paper

GridSim Installation and Implementation Process

Neeraj Kumar Rathore*

*Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India.

Rathore, N. (2015). GridSim Installation and Implementation Process. i-manager’s Journal on Cloud Computing, 2(4), 23-34. https://doi.org/10.26634/jcc.2.4.4906

Abstract

GridSim is a famous Java-based grid simulator with a clear focus on Grid environment. This simulator is based on entities: Grid users, brokers (bargaining on behalf of users) and resources. These entities can have customized characteristics. In this paper, the author has discussed about how to create a Grid Resource, Users, Gridlets and Entities in GridSim to start the simulation as well as the submission and retrieving of Gridlets (job/task) to Grid Resources in GridSim. The author has also introduced some enhancements in the GridSim. Machine Entity (ME) is treated as a dump entity object in GridSim 4.0 and is not able to participate in any decision making activities. The author has proposed that, the ME should be active and participate in load balancing at its level. In order to implement Load balancing model, the author has developed an application which uses the simulated the Grid environment i.e, GridSim. It has been implemented in the application by using Java programming language over the GridSim 5-2_2, to run the application completely on GridSim package.

References

[1]. Rajkumar Buyya and Manzur Murshed, (2002). “GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing”. The Journal of Con currency and Computations: Practice and Experience, Vol. 14, No.13, pp.1175-1220.

[2]. Rathore, N. K., and I. Chana, (2011). "A cogitative analysis of load balancing technique with job migration in grid environment”. IEEE Proceedings Paper, pp. 77-82.

[3]. Rathore, Neeraj, and Inderveer Chana, (2015). "Variable threshold-based hierarchical load balancing technique in Grid." Engineering with Computers, Vol. 31, No. 3, pp. 597-615.

[4]. Rathore, Neeraj and Inderveer Chana, (2013). “A sender initiate based hierarchical load balancing technique for grid using variable threshold value”. Signal Processing, Computing and Control (ISPCC), 2013 IEEE International Conference on, IEEE.

[5]. Rathore, Neeraj, and Inderveer Chana, (2014). “Load balancing and job migration techniques in grid: a survey of recent trends”. Wireless Personal Communications, Vol. 79, No. 3, pp. 2089-2125.

[6]. Neeraj Rathore and Inderveer Chana, (2013). “A Report on Hierarchical Load Balancing Technique In Grid Environment”. i-manager's Journal on Information Technology, 2(4), Sep-Nov 2013, Print ISSN 2277-5110, EISSN 2277-5250, pp. 26-40.

[7]. Sharma, Vishal, Rajesh Kumar, and Neeraj Rathore, (2015). "Topological Broadcasting Using Parameter Sensitivity-Based Logical Proximity Graphs in Coordinated Ground-Flying Ad Hoc Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), Vol. 6, No. 3, pp. 54-72.

[8]. Rathore, Neeraj Kumar, and Inderveer Channa, (2010). "Checkpointing Algorithm in Alchemi .NET." Pragyaan: Journal of Information Technology, Vol. 8, No. 1, pp. 32-38. Retrieved from http://www.iuu.ac/ pragyaan/Pragyaan_IT_ June10.pdf

[9]. Neeraj Kumar Rathore and Inderveer Chana, (2008). “Comparative Analysis of Checkpointing”. PIMR Third National IT Conference, IT Enabled Practices and Emerging Management Paradigm book and category is Communication Technologies and Security Issues, pp no.-32-35, Topic No/Name-46, Prestige Management and Research, Indore, (MP) India. Retrieved from https:// books.google.co.in/books?hl=en&lr=&id=5qb9sCXdB QYC&oi=fnd&pg=PA321&ots=Ht0sJ23gh5&sig=3h_ro m31i5LMVcXV54KYN_6J4ng&redir_esc=y#v=onepage &q&f=false

[10]. Neeraj Rathore, (2015). “Efficient Agent Based Priority Scheduling and Load Balancing Using Fuzzy Logic in Grid Computing”. i-manager's Journal on Computer Science, 3(3) Sep-Nov 2015 Print ISSN: 2347–2227, E-ISSN: 2347–6141, pp. 11-22.

[11]. Rathore, Neeraj, and Inderveer Chana, (2014). "Job migration with fault tolerance based QoS scheduling using hash table functionality in social Grid computing." Journal of Intelligent & Fuzzy Systems, Vol. 27, No. 6, pp. 2821-2833.

[12]. Neeraj Rathore, (2015). “Map Reduce Architecture for Grid”. i-manager's Journal on Software Engineering, 10(1), Jul-Sep 2015, Print ISSN 0973-5151, E-ISSN 2230- 7168, pp.21-30.

[13]. Neeraj Rathore, “Implementing Checkpointing Algorithm in Alchemi .NET”. ME Thesis, Thapar University, Patiala. Retrieved from http://hdl.handle.net /10266/658 http: // dspace. thapar. edu:8080/dspace /bitstream/10266/658/3/T658.pdf

[14]. N. Rathore and I. Chana, (2016). “Job Migration Policies for Grid Environment”. Wireless Personal Communication, Springer Publication-New-York (USA), ISSN print 0929-6212, ISSN online: 1572-834X, IF -0.979, DOI: 10.1007/s11277-016-3264-2

[15]. Neeraj Rathore, (2016). “Ethical Hacking and Security Against Cyber Crime”. i-manager's Journal on Information Technology, 5(1), Dec 2015-Feb 2016, Print ISSN: 2277-5110, E-ISSN: 2277-5250, pp.7-11.

[16]. Neeraj Kumar Rathore and Inderveer Chana, (2010). “Checkpointing Algorithm in Alchemi.NET" in Lambert Academic Publication House (LBA), Germany ISBN-10: 3843361371, ISBN-13: 978-3843361378 in 22- Oct-2010. Retrieved from https://www.lap-publishing. com/catalog/details/store/gb/book/978-3-8433-6137- 8/checkpointing-algorithm-for-alchemi-net-in-grid

[17]. Neeraj Kumar Rathore and Anuradha Sharma, (2015). Efficient Dynamic Distributed Load Balancing Technique. Lambert Academic Publication House, Germany, Project ID: 127478, ISBN no-978-3-659-78288-6 in 19-Oct-2015. Retrieved from https://www.lappublishing. com/catalog/details//store/gb/book/978-3- 659-78288-6/efficient-dynamic-distributed-loadbalancing- technique

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Review Paper

Genetic Algorithm Using MapReduce - A Critical Review

Palak Sachar* , Vikas Khullar**

* PG Scholar, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India.

** Assistant Professor, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India.

Sachar, P., and Khullar, V. (2015). Genetic Algorithm Using MapReduce-A Critical Review. i-manager's Journal on Cloud Computing, 2(4), 35-42. https://doi.org/10.26634/jcc.2.4.4907

Abstract

Now-a-days, to achieve an optimized solution for hard problems is a big challenge. Scientists are putting their best efforts to introduce a best algorithm to optimize the problem to a great extent. Genetic Algorithm is one of the stepping stones in the challenge and is an evolutionary algorithm inspired by Darwin's theory of evolution. Using this algorithm, with MapReduce, makes it efficient and user friendly. Users can build more scalable applications with MapReduce, since it provides a better abstraction to the genetic algorithm in lesser time. To parallelize the process of any project, MapReduce plays a vital role on Hadoop platform. The platform may vary from Hadoop to cloud which affect the performance significantly. Parallelizing of a genetic algorithm is convenient with the help of MapReduce. The major objective of the study is to know the behavior of Genetic Algorithm under the paradigm of Hadoop MapReduce. The various applications show different trends influenced by this platform. Also literature review strongly depicts the advantages of Hadoop MapReduce platform over other platforms. Moreover, the difference between various paradigms of Parallelisation is given in the paper to make decisions regarding its implementation of future work.

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Review Paper

A Survey on Big Data Analytics

Ravi Kumar* , Bharti Nagpal**

* Student, Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India.

** Assistant Professor, Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India.

Kumar, R., and Nagpal, B. (2015). A Survey on Big Data Analytics. i-manager’s Journal on Cloud Computing, 2(4), 43-48. https://doi.org/10.26634/jcc.2.4.4908

Abstract

We live in the age of big data, where every data is linked to some data source. Digitally, it is difficult to calculate the amount of data. Big data refers to volumes of data in the range of petabytes and beyond. This amount of data exceeds the capacity of online storage and the processing systems. This data creation will cross the zettabyte/year range. The data mainly comes from twitter tweets, Facebook comments and so on. This type of data is normally in the form of images, video and different documents that are in unstructured form. For analysing this large amount of data, Hadoop platform is used which is fast and cost effective. In this paper, the authors have mainly focused on literature review and their challenges.

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[10]. Vivien Marx, (2013). “The Big Challenges of Big Data”. Nature, Vol. 498, No. 7453, pp. 255-260.

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i-manager's Journal on Cloud Computing (JCC)

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Volume 2 Issue 4 August - October 2015

Energy Efficient Resource Scheduling Framework for Cloud Computing

Kamalpreet Kaur* , Kanwalvir Singh Dhindsa**

* PG Scholar, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India. ** Associate Professor, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.

Kaur, K., and Dhindsa, K. S. (2015). Energy Efficient Resource Scheduling Framework for Cloud Computing. i-manager’s Journal on Cloud Computing, 2(4), 1-15. https://doi.org/10.26634/jcc.2.4.4904

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An Enhanced Framework to Design Elastic and Reliable Content Based Publish/Subscribe System

Suresh* , P.R. Rajesh**, lalitha.B***

Suresh, M., Rajesh, P. R., and Lalitha (2015). An Enhanced Framework to Design Elastic and Reliable Content Based Publish/Subscribe System. i-manager’s Journal on Cloud Computing.,2(4), 16-22. https://doi.org/10.26634/jcc.2.4.4905

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GridSim Installation and Implementation Process

Neeraj Kumar Rathore*

*Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India.

Rathore, N. (2015). GridSim Installation and Implementation Process. i-manager’s Journal on Cloud Computing, 2(4), 23-34. https://doi.org/10.26634/jcc.2.4.4906

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Genetic Algorithm Using MapReduce - A Critical Review

Palak Sachar* , Vikas Khullar**

* PG Scholar, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India. ** Assistant Professor, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India.

Sachar, P., and Khullar, V. (2015). Genetic Algorithm Using MapReduce-A Critical Review. i-manager's Journal on Cloud Computing, 2(4), 35-42. https://doi.org/10.26634/jcc.2.4.4907

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A Survey on Big Data Analytics

Ravi Kumar* , Bharti Nagpal**

Kumar, R., and Nagpal, B. (2015). A Survey on Big Data Analytics. i-manager’s Journal on Cloud Computing, 2(4), 43-48. https://doi.org/10.26634/jcc.2.4.4908

Abstract

References

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* PG Scholar, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.

** Associate Professor, Department of Computer Science and Engineering, BBSBEC, Fatehgarh Sahib, Punjab, India.

Suresh* , P.R. Rajesh, lalitha.B*

* PG Scholar, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India.

** Assistant Professor, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India.