Rajiv Ranjan - Publications#


All of Rajiv's papers are available at http://rajivranjan.net/publications. He has published over 300 journal and conference papers across the areas of Cloud Computing, Internet of Things, and Big Data Analytics. Rajiv's total number of publications to date are: 12 edited books, 15 book chapters, 200 journal papers, and 66 refereed conference papers.

The papers appeared in top journals and conferences in his field. The importance/esteem of these journals/conferences is demonstrated by their relative impact factors (IF) according to SCI (Web of Science Citation Index), Google Scholar H5-index, and CORE (http://www.core.edu.au/) ranking in the field.

Some of these highly regarded journals, where he has published, include IEEE Transactions on Parallel and Distributed Systems (IF: 4.181, H5-index 106, CORE A*), IEEE Transactions on Cloud Computing (IF: 7.928, H5-index 77), IEEE Transactions on Knowledge and Data Engineering (IF: 3.438, H5-index 117, CORE A), IEEE Transactions on Computers (IF: 2.916, H5-index 97, CORE A*), IEEE Transactions on Services Computing (IF: 5.8, H5-index 86), IEEE Transactions on Industrial Informatics (IF: 6.764, H5-index 120), Future Generation Computer Systems (IF: 6.1, H5-index 127), IEEE Network (IF: 8.8, H5-index 103), IEEE Internet of Things (IF: 9.9, H5-index 140), IEEE Communications Surveys and Tutorials (IF: 22.97, H5-index 248), ACM Computing Surveys (IF: 6.13, H5-index 141, CORE A*), among others. The relative importance of these journals are also established by the fact that they are in ranked in the top 10 percent in the “Computer Science” Subject Area according to Scopus.

A sample of the prestigious conferences that he has presented his work at are: IEEE/ACM Intl. World-Wide Web Conf. (H5-index 113, CORE A*), International Conference on Very Large Databases (H5-index 102, CORE A*), IEEE International Conference on Distributed Computing Systems (H5-index 58, CORE A), IEEE International Conference on Parallel Processing (CORE A), IEEE International Conference on Services Computing (CORE A), Symposium on Reliable Distributed Systems (SRDS, CORE A), and ACM International Conference on Information and Knowledge Management (CIKM, CORE A). The relative importance of these conferences are measured by the acceptance rates between 15%-25%.

His publications are highly cited, with more than 19,800 citations (more than 13,000 since 2016, Google Scholar). He has an h-index of 58 (g-index of 135), which is a high value in his discipline. Listed below is a number of journal and conference papers that are related to his contributions to "Resource and Data Management in Distributed Computing Systems". These papers have been responsible for his international recognition.


1. R. Ranjan, A. Harwood, and R. Buyya, 'Peer-to-Peer Based Discovery of Grid Resource Information: A Tutorial', IEEE Communication Surveys and Tutorials (COMST), Volume 10, Issue 2, Pages 6-33, Second Quarter 2008, IEEE Comm. Society. [IF: 22.973, 154 Google Scholar Citations, Google H5-Index 248]
* Winner of 2009 Outstanding Paper on New Communication Topic Awarded by IEEE Communications Society. IEEE Communications Surveys and Tutorial is ranked the #1 journal in computer science and electronics for 2016, 2017, 2018, 2019, and 2020 (http://www.guide2research.com/journals/) based on its impact factor (22.973). Google Scholar Ranks this journal #1 based on its H5-Index (248) in the Computer Networks and Wireless Communication research area.

2. M. Menzel and R. Ranjan, 'CloudGenius: Decision Support for Web Service Cloud Migration', Main Scientific Track, International ACM Conference on World Wide Web (WWW 2012), Lyon, France, 16-20 April 2012, ACM Press. [CORE A* Ranking, 194 Google Scholar Citations, H5-index 113]
* Seminal work that proposes SLA-optimised algorithm for migration of web servers to public clouds. Google Scholar ranks WWW #2 in Database and Information Systems based on its H5-index.

3. S. Garg, K. Kaur, N. Kumar, G. Kaddoum, A. Y. Zomaya and R. Ranjan, "A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks," IEEE Transactions on Network and Service Management, Volume 16, Issue 3, Pages 924-935, Sept. 2019, doi: 10.1109/TNSM.2019.2927886. [IF: 3.8, 81 Google Scholar Citations]
*For enhancing the reliability of typically bug-prone Cloud Decentre networks, an international group of authors introduced an anomaly detection technique. Unlike its antecedents, the technique fused grey wolf optimisation (GWO) and convolutional neural network (CNN). This proved to be more computationally efficient and suffered fewer false positives than alternatives. It improved the exploration, exploitation and initial population generation capabilities of GWO while revamping the drop-out functionality in CNN.

4. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, 'CloudSim: A Toolkit for the Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms', in the Journal of Software: Practice and Experience (SPE), Volume 41, Issue 1, Pages 23-50, JAN 2011, Wiley Press. [CORE A, IF: 1.7, >5000 Google Citations]
* CloudSim (https://www.cloudbus.org/cloudsim) is the world's most widely adopted cloud computing simulation, benchmarking, and performance evaluation toolkit. The CloudSim software has over 8,000 downloads, and is used globally by IBM Labs, and prestigious universities and research institutions.

5. L. Wang, J. Tao, R. Ranjan, H. Marten, A. Streit, Jingying Chen and Dan Chen, 'G-Hadoop: MapReduce across Distributed Data Centers for Data-intensive Computing', Future Generation Computer Systems (FGCS) Journal, Volume 29, Issue, 3, Pages 739-750, March 2013, Elsevier Press. [IF: 6.1, H5-index 127, 367 Google Scholar Citations]
* One of the first programming frameworks that extended the MapReduce model for big data analysis across multiple datacentres. One of the most cited papers in FGCS since 2010 and was selected as one of the featured paper (total 24 papers selected out of thousands of papers) by Elsevier highlighting top Computer Science research outputs from China in the period 2012-2017.

6. M. Menzel, R. Ranjan, L. Wang, S. Khan, and J. Chen, 'CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds', IEEE Transactions on Computers, Volume 64, Issue 5, Pages 1336-1348, May 2015, IEEE Computer Society Press. [CORE A*, IF: 2.916, H5-index 97, 78 Google Scholar citations]
* One of the first to propose a hybrid technique combining evolutionary optimisation, multi-criteria decision making and the Hadoop programming model to enable optimized and flexible cloud services selection. Google Scholar ranks this journal as #1 in Computer Hardware Design subject category based on its H5-Index (97).

7. L. Wang, Y. Ma, A. Zomaya, R. Ranjan, and D. Chen, 'A Parallel File System with Application-Aware Data Layout Policies for Massive Remote Sensing Image Processing in Digital Earth', IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 26, Issue 6, Pages 1497-1508, June 2015, IEEE Computer Society Press. [CORE A*, IF: 4.181, H5-index 106, 64 Google Scholar Citations]
* Proposed a novel distributed file system that efficiently indexes remote sensing big data. Selected as the featured article for the June 2015 issue of IEEE TPDS journal. This paper has >900 downloads through IEEE Xplore. IEEE TPDS is Ranked #3 in Computing Systems area based on its H5-index (106) by Google Scholar.

8. Y. Ma. L. Wang, A. Zomaya, D. Chen, and R. Ranjan, 'Task-Tree based Large-Scale Mosaicking for Remote Sensed Imageries with Dynamic DAG Scheduling', IEEE Transactions on Parallel and Distributed Systems, Volume 25, Issue 8, Pages 2126-2137, August 2014, IEEE Computer Society. [CORE A*, IF: 4.181, H5-index 106, 55 Google Scholar Citations]
* This paper proposed a dynamic scheduling technique for mosaicking remote sensing big data on clouds. It has >600 downloads through IEEE Xplore.

9. A. Khoshkbarforoushha, R. Ranjan, R. Gaire, E. Abbasnejad, L. Wang and A. Y. Zomaya, "Distribution Based Workload Modelling of Continuous Queries in Clouds," IEEE Transactions on Emerging Topics in Computing, Volume 5, Issue 1, Pages 120-133, 1 Jan.-March 2017, IEEE Computer Society. [IF: 6.0, 39 Google Scholar Citations]
* The paper examined, for the first time, new conditional probability density functions based estimation techniques for representing the resource usage statistics (e.g., CPU and Memory usage) of continuous queries. Rather than estimating the target value as single point value, they argued that a probability density function gives information that is more comprehensive. The approach employed Mixture Density Network (MDN), a special type of artificial neural network (ANN), in which the target (e.g. CPU usage) is represented as a conditional pdf. An MDN fuses a mixture model with an ANN. The paper has >400 downloads through IEEE Xplore.

10. R. N. Calheiros, E. Masoumi, R. Ranjan and R. Buyya, "Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS," in IEEE Transactions on Cloud Computing, Volume 3, Issue 4, Pages 449-458, 1 Oct.-Dec. 2015, doi: 10.1109/TCC.2014.2350475. [IF: 7.928, H5-index 77, 367 Google Scholar Citations]
*This highly cited paper presents the design and realization of a workload prediction technique that can dynamically decide on the allocation of resources to the cloud applications with the aim of lowering the cost for the application providers and at the same time delivering good quality of service to the end-users. Experimental evaluations, using real traces of workload from the Wikimedia Foundation, show that the proposed model can achieve up to 91% accuracy. The most cited paper, according to Scopus (254 citations), among 613 papers published with IEEE TCC (IF: 7.9), since its inception in 2013.

Imprint Privacy policy « This page (revision-4) was last changed on Friday, 2. July 2021, 18:31 by System
  • operated by