This page has only limited features, please log in for full access.
This paper provides a comprehensive measurement study on three video streaming websites with social media features - ‘TED Talks’, ‘xHamster’ and ‘XVideos’. We have analysed 2685 TED videos from 2006 to 2018 to characterise the service. For xHamster and XVideos, active measurements were used to collect unique metadata on almost 3405 and 6721 channels from 2012 to 2019 respectively, which were then analysed. Through these characterisations we gained insight into the main players of the websites – viewers, uploaders and website owners. Our analysis involved the studying of video streaming characteristics such as views, number of uploads, ratings, tags etc. By this we aim to give an overview of the services' current state and compare them with other traditional video streaming services. Our results showed some similar trends to be observed in all three websites such as TED videos and adult channels getting a high number of views despite low injection rate, maintaining a power-law behaviour due to front page recommendations and ratings being underutilised as a feature.Other observations include adult streaming services having a higher number of subscribers per channel. The characterisation results obtained are of value to network operators, content providers, and protocol designers. These results can also be used by content providers to measure what type of content is being watched on their websites. Our study provides a glimpse at how video streaming services function today and the trends they seem to follow.
Saif Ahmed Adib; Aniket Mahanti; Ranesh Kumar Naha. Characterisation and comparative analysis of thematic video portals. Technology in Society 2021, 67, 101690 .
AMA StyleSaif Ahmed Adib, Aniket Mahanti, Ranesh Kumar Naha. Characterisation and comparative analysis of thematic video portals. Technology in Society. 2021; 67 ():101690.
Chicago/Turabian StyleSaif Ahmed Adib; Aniket Mahanti; Ranesh Kumar Naha. 2021. "Characterisation and comparative analysis of thematic video portals." Technology in Society 67, no. : 101690.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
Abdullah Patwary; Ranesh Naha; Saurabh Garg; Sudheer Battula; Anwarul Kaium Patwary; Erfan Aghasian; Muhammad Amin; Aniket Mahanti; Mingwei Gong. Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control. Electronics 2021, 10, 1171 .
AMA StyleAbdullah Patwary, Ranesh Naha, Saurabh Garg, Sudheer Battula, Anwarul Kaium Patwary, Erfan Aghasian, Muhammad Amin, Aniket Mahanti, Mingwei Gong. Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control. Electronics. 2021; 10 (10):1171.
Chicago/Turabian StyleAbdullah Patwary; Ranesh Naha; Saurabh Garg; Sudheer Battula; Anwarul Kaium Patwary; Erfan Aghasian; Muhammad Amin; Aniket Mahanti; Mingwei Gong. 2021. "Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control." Electronics 10, no. 10: 1171.
In cloud storage systems, users must be able to shut down the application when not in use and restart it from the last consistent state when required. BlobSeer is a data storage application, specially designed for distributed systems, that was built as an alternative solution for the existing popular open-source storage system-Hadoop Distributed File System (HDFS). In a cloud model, all the components need to stop and restart from a consistent state when the user requires it. One of the limitations of BlobSeer DFS is the possibility of data loss when the system restarts. As such, it is important to provide a consistent start and stop state to BlobSeer components when used in a Cloud environment to prevent any data loss. In this paper, we investigate the possibility of BlobSeer providing a consistent state distributed data storage system with the integration of checkpointing restart functionality. To demonstrate the availability of a consistent state, we set up a cluster with multiple machines and deploy BlobSeer entities with checkpointing functionality on various machines. We consider uncoordinated checkpoint algorithms for their associated benefits over other alternatives while integrating the functionality to various BlobSeer components such as the Version Manager (VM) and the Data Provider. The experimental results show that with the integration of the checkpointing functionality, a consistent state can be ensured for a distributed storage system even when the system restarts, preventing any possible data loss after the system has encountered various system errors and failures.
Laskhmi Talluri; Ragunathan Thirumalaisamy; Ramgopal Kota; Ram Sadi; Ujjwal Kc; Ranesh Naha; Aniket Mahanti. Providing Consistent State to Distributed Storage System. Computers 2021, 10, 23 .
AMA StyleLaskhmi Talluri, Ragunathan Thirumalaisamy, Ramgopal Kota, Ram Sadi, Ujjwal Kc, Ranesh Naha, Aniket Mahanti. Providing Consistent State to Distributed Storage System. Computers. 2021; 10 (2):23.
Chicago/Turabian StyleLaskhmi Talluri; Ragunathan Thirumalaisamy; Ramgopal Kota; Ram Sadi; Ujjwal Kc; Ranesh Naha; Aniket Mahanti. 2021. "Providing Consistent State to Distributed Storage System." Computers 10, no. 2: 23.
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.
Hossein Chegini; Ranesh Naha; Aniket Mahanti; Parimala Thulasiraman. Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy. IoT 2021, 2, 92 -118.
AMA StyleHossein Chegini, Ranesh Naha, Aniket Mahanti, Parimala Thulasiraman. Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy. IoT. 2021; 2 (1):92-118.
Chicago/Turabian StyleHossein Chegini; Ranesh Naha; Aniket Mahanti; Parimala Thulasiraman. 2021. "Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy." IoT 2, no. 1: 92-118.
This paper analyses an experimental path planning performance between the Iterative Equilateral Space Oriented Visibility Graph (IESOVG) and conventional Visibility Graph (VG) algorithms in terms of computation time and path length for an autonomous vehicle. IESOVG is a path planning algorithm that was proposed to overcome the limitations of VG which is slow in obstacle-rich environment. The performance assessment was done in several identical scenarios through simulation. The results showed that the proposed IESOVG algorithm was much faster in comparison to VG. In terms of path length, IESOVG was found to have almost similar performance with VG. It was also found that IESOVG was complete as it could find a collision-free path in all scenarios.
Sanjoy Kumar Debnath; Rosli Omar; Nor Badariyah Abdul Latip; Susama Bagchi; Elia Nadira Sabudin; Abdul Rashid Omar Mumin; Abdul Majid Soomro; Marwan Nafea; Bashir Bala Muhammad; Ranesh Kumar Naha. COMPUTATIONALLY EFFICIENT PATH PLANNING ALGORITHM FOR AUTONOMOUS VEHICLE. Jurnal Teknologi 2020, 83, 133 -143.
AMA StyleSanjoy Kumar Debnath, Rosli Omar, Nor Badariyah Abdul Latip, Susama Bagchi, Elia Nadira Sabudin, Abdul Rashid Omar Mumin, Abdul Majid Soomro, Marwan Nafea, Bashir Bala Muhammad, Ranesh Kumar Naha. COMPUTATIONALLY EFFICIENT PATH PLANNING ALGORITHM FOR AUTONOMOUS VEHICLE. Jurnal Teknologi. 2020; 83 (1):133-143.
Chicago/Turabian StyleSanjoy Kumar Debnath; Rosli Omar; Nor Badariyah Abdul Latip; Susama Bagchi; Elia Nadira Sabudin; Abdul Rashid Omar Mumin; Abdul Majid Soomro; Marwan Nafea; Bashir Bala Muhammad; Ranesh Kumar Naha. 2020. "COMPUTATIONALLY EFFICIENT PATH PLANNING ALGORITHM FOR AUTONOMOUS VEHICLE." Jurnal Teknologi 83, no. 1: 133-143.
In the file sharing ecosystem, One-Click File Hosting Services (FHS) such as Rapidgator and Uploaded, the previously Rapidshare and Megaupload, provide a platform for users to share copyrighted content. We present a publisher-side analysis of FHS file sharing dynamics through data collected from active measurement by crawling Warez-BB. The website is essentially a forum where publishers can share links to content they have uploaded on file hosting services. Consumers can use the website to gain access to content shared on the website, often free of charge. We primarily analyse various characteristics of file sharing with respect to view count as the evaluation metric.
Marcus Chan; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. Piracy on the Internet: Publisher-side Analysis on File Hosting Services. 2020 International Symposium on Networks, Computers and Communications (ISNCC) 2020, 1 -7.
AMA StyleMarcus Chan, Mingwei Gong, Ranesh Kumar Naha, Aniket Mahanti. Piracy on the Internet: Publisher-side Analysis on File Hosting Services. 2020 International Symposium on Networks, Computers and Communications (ISNCC). 2020; ():1-7.
Chicago/Turabian StyleMarcus Chan; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. 2020. "Piracy on the Internet: Publisher-side Analysis on File Hosting Services." 2020 International Symposium on Networks, Computers and Communications (ISNCC) , no. : 1-7.
Cultural venues like museums increasingly seek to harness the value of data analytics to make data driven decisions related to exhibitions duration, marketing campaigns, resource planning, and revenue optimization. One key priority is the need to understand the influencing factors behind visitor attendance. Using data collected from a large museum, we investigated whether the weather has a significant impact on visitor attendance or that other factors are more important. We applied the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to perform the research, developed and built four different types of regression models using R and its machine learning packages to model visitor attendance. The models were trained and evaluated. Predictions of visitor attendance were then generated from each of the four models and forecast accuracy was measured. The extreme gradient boost model was the best model with the highest average forecast accuracy of 93% and lowest forecast variability when benchmarked against the actual visitor attendance from the test data set. The weather was not considered to be as significant in predicting visitor trends and numbers to the museum compared to factors like time of the day, day of the week and school holidays. However, it was still measured to have a slight impact as excluding weather variables resulted in a model with a poorer fit. Weather can potentially have a more marked impact on cultural attractions in more extreme weather environments and outdoor venues.
Norman Yap; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. Machine Learning-based Modelling for Museum Visitations Prediction. 2020 International Symposium on Networks, Computers and Communications (ISNCC) 2020, 1 -7.
AMA StyleNorman Yap, Mingwei Gong, Ranesh Kumar Naha, Aniket Mahanti. Machine Learning-based Modelling for Museum Visitations Prediction. 2020 International Symposium on Networks, Computers and Communications (ISNCC). 2020; ():1-7.
Chicago/Turabian StyleNorman Yap; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. 2020. "Machine Learning-based Modelling for Museum Visitations Prediction." 2020 International Symposium on Networks, Computers and Communications (ISNCC) , no. : 1-7.
This paper aims to develop a real-time Plant Environment Simulator (PES), which simulates a corrugated plant effectively and realistically. The resultant solution of this work can be used to provide factory workers or new developers with a responsive, simulated learning environment on teaching how to use existing software correctly. The work is carried out for a large cardbox maker that can be used to test new prototypes without using the actual plant facilities, so it will economically and efficiently contribute to the creation of new robust software products for the corrugated plant.
Jeongwon Seo; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. A Realistic and Efficient Real-time Plant Environment Simulator. 2020 International Symposium on Networks, Computers and Communications (ISNCC) 2020, 1 -6.
AMA StyleJeongwon Seo, Mingwei Gong, Ranesh Kumar Naha, Aniket Mahanti. A Realistic and Efficient Real-time Plant Environment Simulator. 2020 International Symposium on Networks, Computers and Communications (ISNCC). 2020; ():1-6.
Chicago/Turabian StyleJeongwon Seo; Mingwei Gong; Ranesh Kumar Naha; Aniket Mahanti. 2020. "A Realistic and Efficient Real-time Plant Environment Simulator." 2020 International Symposium on Networks, Computers and Communications (ISNCC) , no. : 1-6.
Fog computing is an emerging computing paradigm which expands cloud-based computing services near the network edge. With this new computing paradigm, new challenges arise in terms of security and privacy. These concerns are due to the distributed ownership of Fog devices. Because of the large scale distributed nature of devices at the Fog layer, secure authentication for communication among these devices is a major challenge. The traditional authentication methods (password-based, certificate-based and biometric-based) are not directly applicable due to the unique architecture and characteristics of the Fog. Moreover, the traditional authentication methods consume significantly more computation power and incur high latency, and this does not meet the key requirements of the Fog. To fill this gap, this article proposes a secure decentralised location-based device to device (D2D) authentication model in which Fog devices can mutually authenticate each other at the Fog layer by using Blockchain. We considered an Ethereum Blockchain platform for the Fog device registration, authentication, attestation and data storage. We presented the overall system architecture, various participants and their transactions and message interaction between the participants. We validated the proposed model by comparing it with the existing method; results showed that the proposed authentication mechanism was efficient and secure. From the performance evaluation, it was found that the proposed method is computationally efficient and secure in a highly distributed Fog network.
Abdullah Al-Noman Patwary; Anmin Fu; Sudheer Kumar Battula; Ranesh Kumar Naha; Saurabh Garg; Aniket Mahanti. FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain. Computer Communications 2020, 162, 212 -224.
AMA StyleAbdullah Al-Noman Patwary, Anmin Fu, Sudheer Kumar Battula, Ranesh Kumar Naha, Saurabh Garg, Aniket Mahanti. FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain. Computer Communications. 2020; 162 ():212-224.
Chicago/Turabian StyleAbdullah Al-Noman Patwary; Anmin Fu; Sudheer Kumar Battula; Ranesh Kumar Naha; Saurabh Garg; Aniket Mahanti. 2020. "FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain." Computer Communications 162, no. : 212-224.
Matrix operations are fundamental to a wide range of scientific applications such as Graph Theory, Linear Equation System, Image Processing, Geometric Optics, and Probability Analysis. As the workload in these applications has increased, the sizes of matrices involved have also significantly increased. Parallel execution of matrix operations in existing cluster-based systems performs effectively for relatively small matrices but significantly suffers as matrices become larger due to limited resources. Cloud Computing offers scalable resources to handle this limitation; however, the benefits of having access to almost-infinite scalable resources in the Cloud also come with challenges of ensuring time and resource-efficient matrix operations. To the best of our knowledge, there is no specific Cloud service that optimizes the efficiency of matrix operations on Cloud infrastructure. To address this gap and offer convenient service of matrix operations, the paper proposes a novel scalable service framework called Scalable Matrix Operation as a Service. Our framework uses Dynamic Matrix Partition techniques, based on matrix operation and sizes, to achieve efficient work distribution, and scales based on demand to achieve time and resource-efficient operations. The framework also embraces the basic features of security, fault tolerance, and reliability. Experimental results show that the adopted dynamic partitioning technique ensures faster and better performance when compared to the existing static partitioning technique.
Kc Ujjwal; Sudheer Kumar Battula; Saurabh Garg; Ranesh Kumar Naha; Anwarul Kaium Patwary; Alexander Brown. SMOaaS: a Scalable Matrix Operation as a Service model in Cloud. The Journal of Supercomputing 2020, 77, 3381 -3401.
AMA StyleKc Ujjwal, Sudheer Kumar Battula, Saurabh Garg, Ranesh Kumar Naha, Anwarul Kaium Patwary, Alexander Brown. SMOaaS: a Scalable Matrix Operation as a Service model in Cloud. The Journal of Supercomputing. 2020; 77 (4):3381-3401.
Chicago/Turabian StyleKc Ujjwal; Sudheer Kumar Battula; Saurabh Garg; Ranesh Kumar Naha; Anwarul Kaium Patwary; Alexander Brown. 2020. "SMOaaS: a Scalable Matrix Operation as a Service model in Cloud." The Journal of Supercomputing 77, no. 4: 3381-3401.
This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists.
Sanjoy Kumar Debnath; Rosli Omar; Susama Bagchi; Marwan Nafea; Ranesh Kumar Naha; Elia Nadira Sabudin. Energy Efficient Elliptical Concave Visibility Graph Algorithm for Unmanned Aerial Vehicle in an Obstacle-rich Environment. 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) 2020, 129 -134.
AMA StyleSanjoy Kumar Debnath, Rosli Omar, Susama Bagchi, Marwan Nafea, Ranesh Kumar Naha, Elia Nadira Sabudin. Energy Efficient Elliptical Concave Visibility Graph Algorithm for Unmanned Aerial Vehicle in an Obstacle-rich Environment. 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). 2020; ():129-134.
Chicago/Turabian StyleSanjoy Kumar Debnath; Rosli Omar; Susama Bagchi; Marwan Nafea; Ranesh Kumar Naha; Elia Nadira Sabudin. 2020. "Energy Efficient Elliptical Concave Visibility Graph Algorithm for Unmanned Aerial Vehicle in an Obstacle-rich Environment." 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) , no. : 129-134.
In today’s modern era, the multi-path communication paradigm is becoming prominent in supporting multi-media applications due to its dazzling features of improved network’s resilience, reliability, and performance. Specifically, wireless (environments) networks are intended to become typically reliant on the idea of multi-pathing for efficient traffic balancing which ultimately helps in achieving good performance. Although single-path communication is a promising paradigm for supporting multi-media applications, due to its incompetence in providing significant fault-tolerance demands of such applications, it is unable to satisfy the good Quality of Service (QoS) and Quality of Experience (QoE) requirements for such applications. Hence, traffic allocation and provisioning in the Mobile Ad-hoc Networks (MANETs) environment, considering the dynamic and dissimilar wireless channel characteristics of paths’ and inadequate offered resources (i.e., buffer) in nodes over those paths’, is a challenging job. Nevertheless, current works on traffic allocation addresses the data scheduling provision without considering the dissimilar wireless channel characteristics and background traffic of paths’ respectively. To address the problem of abrupt data scheduling policy, we propose, a novel Adaptive-Congestion Aware Fibonacci Sequence based Data Scheduling Policy (A-CAFDSP) which takes care of each path’s data carrying capacity, dissimilar characteristics and background traffic intensity respectively and ultimately makes data scheduling adaptation decisions to select the efficient paths for concurrent transmissions. Indeed, A-CAFDSP includes the paradigm to concurrently distribute the data packets over multiple available network paths using Fibonacci sequence wisely and regulate the traffic of each available network path individually. Moreover, current works simply adopts and have evaluated their approach on the idealized propagation (radio) prototype without considering fading effects. However, we have evaluated our proposed method in fading environment and all the competent multiple available paths are within the interference ranges of each other hence, the inter-flow interference does exist in our simulation, which certainly gives us the correct idea that how our proposed method works in realistic wireless environment. The simulation results indicate that the performance of A-CAFDSP is better with existing conventional (single-path) and multi-path approaches in terms of average throughput, packet delivery ratio (PDR) and normalized load.
Varun Kumar Sharma; Lal Pratap Verma; Mahesh Kumar; Ranesh Kumar Naha; Aniket Mahanti. A-CAFDSP: An Adaptive-Congestion Aware Fibonacci Sequence based Data Scheduling Policy. Computer Communications 2020, 158, 141 -165.
AMA StyleVarun Kumar Sharma, Lal Pratap Verma, Mahesh Kumar, Ranesh Kumar Naha, Aniket Mahanti. A-CAFDSP: An Adaptive-Congestion Aware Fibonacci Sequence based Data Scheduling Policy. Computer Communications. 2020; 158 ():141-165.
Chicago/Turabian StyleVarun Kumar Sharma; Lal Pratap Verma; Mahesh Kumar; Ranesh Kumar Naha; Aniket Mahanti. 2020. "A-CAFDSP: An Adaptive-Congestion Aware Fibonacci Sequence based Data Scheduling Policy." Computer Communications 158, no. : 141-165.
Devki Nandan Jha; Khaled Alwasel; Areeb Alshoshan; Xianghua Huang; Ranesh Kumar Naha; Sudheer Kumar Battula; Saurabh Garg; Deepak Puthal; Philip James; Albert Zomaya; Schahram Dustdar; Rajiv Ranjan. IoTSim‐Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments. Software: Practice and Experience 2020, 50, 844 -867.
AMA StyleDevki Nandan Jha, Khaled Alwasel, Areeb Alshoshan, Xianghua Huang, Ranesh Kumar Naha, Sudheer Kumar Battula, Saurabh Garg, Deepak Puthal, Philip James, Albert Zomaya, Schahram Dustdar, Rajiv Ranjan. IoTSim‐Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments. Software: Practice and Experience. 2020; 50 (6):844-867.
Chicago/Turabian StyleDevki Nandan Jha; Khaled Alwasel; Areeb Alshoshan; Xianghua Huang; Ranesh Kumar Naha; Sudheer Kumar Battula; Saurabh Garg; Deepak Puthal; Philip James; Albert Zomaya; Schahram Dustdar; Rajiv Ranjan. 2020. "IoTSim‐Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments." Software: Practice and Experience 50, no. 6: 844-867.
Fog computing is a promising computing paradigm in which IoT data can be processed near the edge to support time-sensitive applications. However, the availability of the resources in the computation device is not stable since they may not be exclusively dedicated to the processing in the Fog environment. This, combined with dynamic user behaviour, can affect the execution of applications. To address dynamic changes in user behaviour in a resource limited Fog device, this paper proposes a Multi-Criteria-based resource allocation policy with resource reservation in order to minimise overall delay, processing time and SLA violation which considers Fog computing-related characteristics, such as device heterogeneity, resource constraint and mobility, as well as dynamic changes in user requirements. We employ multiple objective functions to find appropriate resources for execution of time-sensitive tasks in the Fog environment. Experimental results show that our proposed policy performs better than the existing one, reducing the total delay by 51%. The proposed algorithm also reduces processing time and SLA violation which is beneficial to run time-sensitive applications in the Fog environment.
Ranesh Kumar Naha; Saurabh Garg. Multi-Criteria-based Dynamic User Behaviour Aware Resource Allocation in Fog Computing. 2019, 1 .
AMA StyleRanesh Kumar Naha, Saurabh Garg. Multi-Criteria-based Dynamic User Behaviour Aware Resource Allocation in Fog Computing. . 2019; ():1.
Chicago/Turabian StyleRanesh Kumar Naha; Saurabh Garg. 2019. "Multi-Criteria-based Dynamic User Behaviour Aware Resource Allocation in Fog Computing." , no. : 1.
The Fog computing paradigm is becoming prominent in supporting time-sensitive applications that are related to the smart Internet of Things (IoT) services, such as smart city and smart healthcare. Although Cloud computing is a promising paradigm for IoT in data processing, due to the high latency limitation of the Cloud, it is unable to satisfy the requirements for time-sensitive applications. Resource allocation and provisioning in the Fog-Cloud environment, considering dynamic changes in user requirements and limited available resources in Fog devices, is a challenging task. Among dynamic changes in the parameters of user requirements, the deadline is the most important challenge in the Fog computing environment. Current works on Fog computing address the resource provisioning without considering the dynamic changes in users’ requirements. To address the problem of satisfying deadline-based dynamic user requirements, we propose resource allocation and provisioning algorithms by using resource ranking and provision of resources in a hybrid and hierarchical fashion. The proposed algorithms are evaluated in a simulation environment by extending the CloudSim toolkit to simulate a realistic Fog environment. The experimental results indicate that the performance of the proposed algorithms is better compared with existing algorithms in terms of overall data processing time, instance cost and network delay, with the increasing number of application submissions. The average processing time and cost are decreased by 12% and 15% respectively, compared with existing solutions.
Ranesh Kumar Naha; Saurabh Garg; Andrew Chan; Sudheer Kumar Battula. Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment. Future Generation Computer Systems 2019, 104, 131 -141.
AMA StyleRanesh Kumar Naha, Saurabh Garg, Andrew Chan, Sudheer Kumar Battula. Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment. Future Generation Computer Systems. 2019; 104 ():131-141.
Chicago/Turabian StyleRanesh Kumar Naha; Saurabh Garg; Andrew Chan; Sudheer Kumar Battula. 2019. "Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment." Future Generation Computer Systems 104, no. : 131-141.
Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm.
Sudheer Kumar Battula; Saurabh Garg; Ranesh Kumar Naha; Parimala Thulasiraman; Ruppa Thulasiram. A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment. Sensors 2019, 19, 2954 .
AMA StyleSudheer Kumar Battula, Saurabh Garg, Ranesh Kumar Naha, Parimala Thulasiraman, Ruppa Thulasiram. A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment. Sensors. 2019; 19 (13):2954.
Chicago/Turabian StyleSudheer Kumar Battula; Saurabh Garg; Ranesh Kumar Naha; Parimala Thulasiraman; Ruppa Thulasiram. 2019. "A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment." Sensors 19, no. 13: 2954.
Saving energy is an important issue for cloud providers to reduce energy cost in a data center. With the increasing popularity of cloud computing, it is time to examine various energy reduction methods for which energy consumption could be reduced and lead us to green cloud computing. In this paper, our aim is to propose a virtual machine selection algorithm to improve the energy efficiency of a cloud data center. We are also presenting experimental results of the proposed algorithm in a cloud computing based simulation environment. The proposed algorithm dynamically took the virtual machines' allocation, deallocation, and reallocation action to the physical server. However, it depends on the load and heuristics based on the analysis placement of a virtual machine which is decided over time. From the results obtained from the simulation, we have found that our proposed virtual machine selection algorithm reduces the total energy consumption by 19% compared to the existing one. Therefore, the energy consumption cost of a cloud data center reduces and also lowers the carbon footprint. Simulation-based experimental results show that the proposed heuristics which are based on resource provisioning algorithms reduce the energy consumption of the cloud data center and decrease the virtual machine's migration rate.
Nasrin Akhter; Mohamed Othman; Ranesh Kumar Naha. Energy-aware virtual machine selection method for cloud data center resource allocation. 2018, 1 .
AMA StyleNasrin Akhter, Mohamed Othman, Ranesh Kumar Naha. Energy-aware virtual machine selection method for cloud data center resource allocation. . 2018; ():1.
Chicago/Turabian StyleNasrin Akhter; Mohamed Othman; Ranesh Kumar Naha. 2018. "Energy-aware virtual machine selection method for cloud data center resource allocation." , no. : 1.
In this paper, a re-evaluation undertaken for dynamic VM consolidation problem and optimal online deterministic algorithms for the single VM migration in an experimental environment. We proceeded to focus on energy and performance trade-off by planet lab workload traces, which consists of a thousand Planetlab VMs with widespread simulation environments. All experiments are done in a simulated cloud environment by the CloudSim simulation tool. A new paradigm of utility-oriented IT services is cloud computing, which offers a pay-as-you-go model. In recent years, there has been increasing interest among many users from business, scientific, engineering and educational territories in cloud computing. There is increasing concern that high energy consumption issues are a disadvantage for various institutions. However, so far too little attention has been given to the various methods to reduce energy consumption in cloud environments while ensuring performance. Besides the evaluation of energy-efficient data center management algorithms in the cloud, we proposed a further research directed toward the development of energy efficient algorithms. By the experimental evaluation of the current proposal for the competitive analysis of dynamic VM consolidation and optimal online deterministic algorithms for the single VM migration, we found different results for different algorithm combinations. Cloud-based data centers` consume massive energy, which has a negative effect on the environment and operational cost, this work contributes to the energy consumption reduction in the cloud environment.
Nasrin Akhter; Mohamed Othman; Ranesh Kumar Naha. Evaluation of Energy-efficient VM Consolidation for Cloud Based Data Center - Revisited. 2018, 1 .
AMA StyleNasrin Akhter, Mohamed Othman, Ranesh Kumar Naha. Evaluation of Energy-efficient VM Consolidation for Cloud Based Data Center - Revisited. . 2018; ():1.
Chicago/Turabian StyleNasrin Akhter; Mohamed Othman; Ranesh Kumar Naha. 2018. "Evaluation of Energy-efficient VM Consolidation for Cloud Based Data Center - Revisited." , no. : 1.
Emerging technologies that generate a huge amount of data such as the Internet of Things (IoT) services need latency aware computing platforms to support time-critical applications. Due to the on-demand services and scalability features of cloud computing, Big Data application processing is done in the cloud infrastructure. Managing Big Data applications exclusively in the cloud is not an efficient solution for latency-sensitive applications related to smart transportation systems, healthcare solutions, emergency response systems and content delivery applications. Thus, the Fog computing paradigm that allows applications to perform computing operations in-between the cloud and the end devices has emerged. In Fog architecture, IoT devices and sensors are connected to the Fog devices which are located in close proximity to the users and it is also responsible for intermediate computation and storage. Most computations will be done on the edge by eliminating full dependencies on the cloud resources. In this chapter, we investigate and survey Fog computing architectures which have been proposed over the past few years. Moreover, we study the requirements of IoT applications and platforms, and the limitations faced by cloud systems when executing IoT applications. Finally, we review current research works that particularly focus on Big Data application execution on Fog and address several open challenges as well as future research directions.
Ranesh Kumar Naha; Saurabh Garg; Andrew Chan. Fog Computing Architecture: Survey and Challenges. 2018, 1 .
AMA StyleRanesh Kumar Naha, Saurabh Garg, Andrew Chan. Fog Computing Architecture: Survey and Challenges. . 2018; ():1.
Chicago/Turabian StyleRanesh Kumar Naha; Saurabh Garg; Andrew Chan. 2018. "Fog Computing Architecture: Survey and Challenges." , no. : 1.
Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.
Ranesh Kumar Naha; Saurabh Garg; Dimitrios Georgakopoulos; Prem Prakash Jayaraman; Longxiang Gao; Yong Xiang; Rajiv Ranjan. Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions. IEEE Access 2018, 6, 47980 -48009.
AMA StyleRanesh Kumar Naha, Saurabh Garg, Dimitrios Georgakopoulos, Prem Prakash Jayaraman, Longxiang Gao, Yong Xiang, Rajiv Ranjan. Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions. IEEE Access. 2018; 6 ():47980-48009.
Chicago/Turabian StyleRanesh Kumar Naha; Saurabh Garg; Dimitrios Georgakopoulos; Prem Prakash Jayaraman; Longxiang Gao; Yong Xiang; Rajiv Ranjan. 2018. "Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions." IEEE Access 6, no. : 47980-48009.