Recent Update from my Research Group

@IoTSim Release!!!

We are happy to release the source code of IoTSim-Edge and IoTSim-Stream. IoTSim Release :

These simulators can be used to study the performance and modelling of IoT applications in cloud and edge computing environments. Currently, it supports the modelling of edge-native IoT applications (IoTSim-Edge) and cloud-native stream processing IoT applications (IoTSim-Stream)

@Internet of Things and Data Science Research: The Next Grand Challenges 

In the last decade, we have been transitioning from a data-poor to a data-rich world with the promise of unparalleled intelligence. Such transition will definitely require significant investments in every aspect in our societies including social, political, economic and cultural. Much of the (unprecedented) increase in data generation can be attributed to the abundance of mobile devices and wearables, the increase of instrumentation in every industry vertical, the mass adoption of social networks and the digitization of every aspect of our lives. Generically, the bulk of such data collection falls under the Internet of Things (IoT). IoT data comes from a variety of sources that can be classified into (a) machine-based (e.g., environmental, weather, air quality, water quality, flows, traffic speeds, people flows and GPS location) or (b) people-based (e.g., social media, crowd sourced data collection, and simple text messaging) providing data and situational observations associated with events.

This article (available from) written by leading IoT and Data Science experts discusses research challenges related to devising a new IoT programming paradigm for orchestrating IoT applications’ composition and data processing across heterogeneous computing infrastructure (Cloud, Edge, and Things). Snapshot of the research challenges covered in the paper is given below.


Article Ref: R. Ranjan et al., “The Next Grand Challenges: Integrating the Internet of Things and Data Science,” in IEEE Cloud Computing, vol. 5, no. 3, pp. 12-26, May./Jun. 2018. doi: 10.1109/MCC.2018.032591612

@Cloud Computing

Whereas significant emphasis has been placed on (mobile) cloud offloading (whereby software applications can be offloaded from a mobile device to a datacenter), there’s also a need for reverse offloading—that is, movement of functionality from the cloud to the edge devices, to counter for latency-sensitive applications and to minimize data sizes that must be transferred over a network. To this end, we propose new computing paradigm called Osmotic Computing.


The paper reporting CloudSim Toolkit: “CloudSim: A Toolkit for the Modeling and Simulation of Cloud Resource Management and Application Provisioning Techniques“, has been listed in the top 20 most cited papers (#19, 993 citations) since 2010 by Scopus in the Computer Science subject area.

@ Journal of Software Practice and Experience 

Checkout our recent work on Analytics-as-a-Service in a Multi-Cloud Environment through Semantically enabled Hierarchical Data Processing.

@IEEE Transactions on Emerging Topics in Computing

Checkout our novel work on Probabilistic IoT Workload Modelling for Stream Processing Big Data Systems with Prof. Albert Zomaya (University of Sydney). This work was recently accepted by IEEE Transactions on Emerging Topics in Computing.

@ IEEE Cybernetics for Cyber-Physical Systems

Checkout our recent vision paper with Prof. Rajkumar Buyya (University of Melbourne) on  End-to-End QoS Specification and Monitoring in the Internet of Things.

@IEEE Cloud Computing

Check out our recent vision paper with Prof. Schahram Dustdar (TU Wein) that discusses fundamental research issues involved with Migrating Smart City Applications to the Cloud Dacentres.

The paper is selected as highlight paper by IEEE Cloud Computing.

@New Vision Paper on Thrests to Networking Edge Datacentre with Cloud Datacentre

The big data analytics lifecycle, which starts with raw data collection and moves to data analytics and decision making, requires intelligent coordination of activities between tiny IoT sensors, IoT gateways, and in-transit network devices in an edge datacenter (EDC), with the big data processing frameworks and hardware resources hosted in large cloud datacenter (CDC) farms. Such coordination of data analytics activities raises a new set of technical challenges from the perspective of ensuring end-to-end security and privacy of data as it travels from EDC to CDC (or vice versa). Although a number of research activities have addressed securing data in the CDC, in our recent vision paper we outline the new research challenges in the area of secure networking of data and devices that spans from EDC to CDC.

This paper is selected as the cover feature.

@ PhD Scholarships in Cloud Computing and Big Data 2016

Applications are invited from outstanding UK/EU and International students (exceptional circumstances) for our EPSRC funded doctoral training centre on cloud computing and big data. The scholarship will fund:

  • £14,000+ per year for a maximum of 4 years as living expense
  • Full Fee Waiver (worth £16,000 per year)

Deadline: 10 June 2016.

For further information contact: Rajiv Ranjan (

@IEEE Computer

One of our seminal paper on the topic “Cloud Resource Orchestration Frameworks” selected as the feature article for February 2016 issue of IEEE Computer. The paper titled “Dimensions for Evaluating Cloud Resource Orchestration Frameworks” can be downloaded from following link.

@IET Book Series on Big Data

We have launched the IET’s flagship book series on Big Data. Proposals for coherently integrated International multi-author edited books and handbooks, research monographs, reference books, and advanced level textbooks will be considered for the Series. Each proposal will be reviewed by the Series Editor and/or the Series editorial board members with additional reviews from independent reviewers where appropriate

If you are interested in writing book then please contact me on: raj.ranjan at


At Newcastle University, United Kingdom (in collaboration with Australian National University, Royal Melbourne Institute of Technology, and University of Tasmania – Australia) we have  designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. The related publication can be downloaded from here. The source code will be available for public use in next few weeks. The link to GitHub will be posted here!!

@IEEE Cloud Computing

5 articles authored/co-authored by me in the research areas of Internet of Things and Big Data since 2014 appears at (#7, #9, #10, #11, #12) in the most popular article list of IEEE Cloud Computing– January 2016 (

@ Newcastle – Singapore Studentships 2016

Applications are invited from outstanding UK/EU and International students for our 2016 Singapore Studentship on cloud computing and big data. Project details are available here. The scholarship will fund:

  • £20,000 per year for a maximum of 4 years
  • Contributes towards PhD tuition fees, living costs and 1 return flight to Singapore each year

Deadline: 26 Feb 2016.

For further information contact: Rajiv Ranjan (

@Newcastle University – 2PhD Scholarships in Big Data and Cloud Computing

NERC DREAM (Data, Risk and Environmental Analytical Methods) CDT Big Data Spatial Risk Analytics PhD opportunities at Newcastle University. The NERC DREAM  Centre for Doctoral training on Risk and Mitigation using BIG Data is a consortium of Newcastle, Cranfield, Cambridge and Birmingham Universities. We will be awarding 10 full PhD studentships to start in October 2016. Applications from eligible students are now sought for the Newcastle University DREAM projects relating to Big Data Spatial Risk Analytics for an October 2016 start. Newcastle projects available for October 2016 can be found here.

Deadline: 26 Feb 2016.

For further information contact: Rajiv Ranjan ( or Stuart Barr (

@IEEE Cloud Computing

Four articles authored/co-authored by my research group in last 12 months appears in IEEE Cloud Computing top 10 popular (#1, #2, #5 and #8)  most cited article list – December 2015.

@IEEE Transactions on Computers 

Our paper titled “MuR-DPA: Top-Down Levelled Multi-Replica Merkle Hash Tree Based Secure Public Auditing for Dynamic Big Data Storage on Cloud” that was published in IEEE Transactions on Computers appeared at #4  and #5 for November 2015 and December 2015 respectively in the Top 50 most popular article list available at:

@IEEE Cloud Computing

Three articles authored/co-authored by me in last 12 months appears in IEEE Cloud Computing top 10 popular (#5, #7, and #11) article list – November 2015 (

@IEEE Cloud Computing

Our paper titled “End-to-End Privacy for Open Big Data Markets” was nominated as the highlight paper ( for September-October 2015 by the IEEE Cloud Computing.

@Cloud of Things Ecosystem: Research Challenges

Our recent position papers [1] [2] analyse the state of the art in  tools and technologies available for facilitating QoS-aware  management of and inter-operation across the Sensor (IoT) Network layer, Big Data processing layer, and Cloud Computing layer.


At Newcastle University (United Kingdom), We have launched new project IoTSim which will investigate and develop novel simulation techniques and frameworks to counter for the new challenges of Big Data and Internet of Things Era.

@Future Generation Computer Systems (FGCS)

Our work on distributed big data processing  – G-Hadoop –  (in collaboration with Prof. Lizhe Wang from Chinese Academy of Sciences) has been cited 107 times since September 2012. Further, Google Scholar ( reports that paper ( describing G-Hadoop is one of the top 20 papers contributing to the overall impact factor and h5-index of the FGCS Journal since 2010.

@IEEE Cloud Computing

Our paper titled “Processing Distributed Internet of Things of Data on Clouds” was nominated as the highlight paper ( for May-June-July 2015 by the IEEE Cloud Computing.

@IEEE Transactions on Parallel and Distributed Systems

Two articles  authored/co-authored by me in last 12 months appears in IEEE TPDS most cited (#11 and #25) article list – June 2015. Available at: TPDS June 2015.

@IEEE Cloud Computing

Three articles authored/co-authored by me in last 12 months appears in IEEE Cloud Computing top 10 popular (#4, #5, and #7) article list – June 2015 (

@IEEE Transactions on Computers 

My paper titled “CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds, doi: 10.1109/TC.2014.2317188” that was published in IEEE Transactions on Computers appeared at #7  in the May 2015 and #10 in June 2015 in the Top 50 most popular article list available at:

@New PhD Scholarships/Fundings

I am looking to recruit high calibre and dedicated PhD students in the area of cloud computing, big data, Internet of Things, and cyber physical systems. Funding is available to cover both tuition fees and living expenses.

Interested students please contact via email: rranjans *-at-*

@IEEE Systems: New paper on “A Cloud Infrastructure Service Recommendation System for Optimizing Real-time QoS Provisioning Constraints”

In this paper, we  present an  approach and algorithm for enabling cloud service selection based on  the real-time (run-time) QoS (end-to-end message latency, end-to-end message throughput)  metrics. Hosting of next generation big data applications in domain of on-line interactive gaming, large scale sensor analytics, and real-time mobile applications on cloud services necessitates optimization of such real-time QoS constraints for meeting Service Level Agreements (SLAs). To this end, we present a real-time QoS aware multi-criteria decision making technique that builds over well known Analytics Hierarchy Process (AHP) method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute possible best fit combinations of cloud services at IaaS layer. We conducted extensive experiments to prove the feasibility of our approach.

Further Reading:  Optimizing Real-time QoS provisioning Constraints for Big Data Applications

@IEEE Computer: New paper on “Capability Analysis of Cloud Resource Orchestration Frameworks”

Since the inception of cloud computing in mid 2000, academic groups and industry vendors have developed a number of Cloud Re-source Orchestration Frameworks (CROFs) for simplifying the application management. The CROF aids software engineers, scien-tists, and infrastructure administrators to migrate and manage their in-house applications to cloud environments. Despite the higher level of technical maturity of such CROFs, there is clear lack of study that can help cloud application administrators in understanding and analyzing the features of CROFs against a common set of concepts and dimensions. Therefore, this study presents a set of generic technical dimensions for clearly analyzing the capabilities of the overriding CROFs. A concise survey and classification of most prominent research work is also presented.

Further Reading: Resource Orchestration Frameworks

@IEEE Internet Computing: A seminal paper titled “Cloud Resource Orchestration programming: overview, issues and directions”

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers, load-balancers, data processing frameworks, etc.) resources. The pervasiveness and power of cloud computing alleviates some of the problems application administrators face in their existing hardware- and locally managed software- environments. However, the rapid increase in scale, dynamicity, heterogeneity, and diversity of cloud resources necessitates the need of having expert knowledge about programming complex orchestration operations (e.g, selection, deployment, monitoring, and run-time control) on those resources to achieve desired Quality of Service (QoS). This article provides an overview of the key cloud resource types, resource orchestration operations, with special focus on research issues involved in programming those operations.

Further Reading:

Preprint available at: Cloud Resource Orchestration

@IEEE Cloud Computing: New BlueSkies Column on Cloud Interoperability Challenge

Welcome to the second installment of “Blue Skies.” This department, which will appear four times a year, will provide in-depth analyses of the most recent and influential research related to cloud technologies and innovations. In this issue, I’ll overview research issues and directions related to cloud interoperability. In the cloud computing landscape, “cloud  interoperability” typically refers to the ability to seamlessly deploy, migrate, and manage application workloads across heterogeneous hardware and software resources provided by multiple datacenter cloud providers (such as Amazon and GoGrid).

Further Reading: R. Ranjan,  “The Cloud Interoperability Challenge“, IEEE Cloud Computing,  Volume, Issue 2, Pages 20-24, July 2014, doi: 10.1109/MCC.2014.41

@IEEE Transactions on Cloud Computing: New paper on “Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS”

As companies shift from desktop applications to Cloud-based Software as a Service (SaaS) applications deployed on public Clouds, the competition for end-users by Cloud providers offering similar services grows. In order to survive in such a competitive market, Cloud-based companies must achieve good Quality of Service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost-effective amount of resources is challenging because workloads experience variation over time. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a Cloud workload prediction module for SaaS providers based on the Autoregressive Integrated Moving Average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to web servers.We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91%, which leads to efficiency in resource utilization with minimal impact on the QoS.

Further Reading: Autonomic Workload Prediction

@IEEE Cloud Computing: BlueSkies Column on Stream Big Processing in Datacentre Clouds

This paper is part of  BlueSkies Department of IEEE’s Flagship Cloud Computing magazine. This column (which will appear 4 times a year) intends to undertake in-depth analysis of most recent and influential research related to cloud technologies and innovations. Providing in-depth overview of research issues and directions related to “Streaming Big Data Processing in the Datacentre Clouds” is the goal of this BlueSkies column. Additionally, this column also welcomes high quality position, survey and review papers from cloud computing and other related research areas. Future contributions could also be related to in-depth report on innovative research projects in academia and research institutions, cloud and big data challenges at leading international conferences, and open source cloud computing projects.

Further Reading:

Rajiv Ranjan, Streaming Big Data Processing in Datacenter Clouds. IEEE Cloud Computing 1(1): 78-83 (2014)

@IEEE Transactions on Computers: New Journal Paper on Automating Migration of Multi-Component Web Applications to Public Clouds

With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and VM (Virtual Machine) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multicriteria- based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius’ applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA.

Further Reading:

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, IEEE Computer Society Press. [ERA A* Journal, ISI Impact Factor 1.2] (Accepted March 5, 2014 and to appear)