Outputs (32)

Optimal controller selection and migration in large scale software defined networks for next generation internet of things (2023)
Journal Article
Shahzad, M., Liu, L., Belkout, N., & Antonopoulos, N. (2023). Optimal controller selection and migration in large scale software defined networks for next generation internet of things. SN Applied Sciences, 5(12), Article 309. https://doi.org/10.1007/s42452-023-05535-0

The substantial amount of IoT traffic, coupled with control messages, places a heavy burden on SDN controllers, which compromises their capacity. We investigate how SDN can revolutionize the conventional approach, aiming to overcome the limitations o... Read More about Optimal controller selection and migration in large scale software defined networks for next generation internet of things.

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics (2021)
Journal Article
Ali, H., Tariq, U. U., Hardy, J., Zhai, X., Lu, L., Zheng, Y., Bensaali, F., Amira, A., Fatema, K., & Antonopoulos, N. (2021). A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics. Computer Science Review, 41, Article 100416. https://doi.org/10.1016/j.cosrev.2021.100416

Internet-of-Things (IoT) is an appealing service to revolutionise Smart City (SC) initiatives across the globe. IoT interconnects a plethora of digital devices known as Sensor Nodes (SNs) to the Internet. Due to their high performance and exceptional... Read More about A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics.

A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G (2019)
Journal Article
Lu, L., Liu, L., Panneerselvam, J., Yuan, B., Gu, J., & Antonopoulos, N. (2020). A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G. IEEE Transactions on Cognitive Communications and Networking, 6(2), 486-498. https://doi.org/10.1109/tccn.2019.2954388

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extr... Read More about A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G.

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters (2019)
Journal Article
Lu, Y., Liu, L., Panneerselvam, J., Zhai, X., Sun, X., & Antonopoulos, N. (2020). Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters. IEEE Transactions on Sustainable Computing, 5(3), 308-318. https://doi.org/10.1109/TSUSC.2019.2905728

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess ene... Read More about Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters.

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence (2019)
Journal Article
Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019). An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effect... Read More about An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence.

Cloud-based video analytics using convolutional neural networks (2018)
Journal Article
Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019). Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of... Read More about Cloud-based video analytics using convolutional neural networks.

Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds (2018)
Journal Article
Yaseen, M. U., Anjum, A., Rana, O., & Antonopoulos, N. (2019). Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds. IEEE Transactions on Systems, Man and Cybernetics: Systems, 49(1), 253-264. https://doi.org/10.1109/TSMC.2018.2840341

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infr... Read More about Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds.

An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. (2018)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. IEEE Transactions on Emerging Topics in Computing, https://doi.org/10.1109/TETC.2018.2794328

Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes incre... Read More about An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity..

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres (2018)
Journal Article
Panneerselvam, J., Liu, L., Lu, Y., & Antonopoulos, N. (2018). An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres. Future Generation Computer Systems, 83, 239-249. https://doi.org/10.1016/j.future.2017.12.064

Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tasks, such tasks usually exhibit increased level of behavioural heterogeneity... Read More about An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres.

InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments (2017)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems, 80, 322-341. https://doi.org/10.1016/j.future.2017.05.022

Cloud Computing has emerged as a low cost anywhere anytime computing paradigm. Given the energy consumption characteristics of the Cloud resources, service providers are under immense pressure to reduce the energy implications of the datacentres. For... Read More about InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.