Energy, Performance, and Cost-efficient Resource Management in Large Scale Datacenters
[As the demand for data processing and storage continues to skyrocket in the digital era, large-scale datacenters have become pivotal hubs of computational activity. Efficiently managing the available resources within these datacenters is imperative to ensure optimal performance, minimize energy consumption, improve environmental sustainability, and control operational costs. This research aims to develop novel strategies and frameworks for resource management that strike a harmonious balance between energy efficiency, computational performance, and cost-effectiveness.]
In the rapidly evolving landscape of information technology, this research topic focuses on several interconnected domains that are critical for shaping the future of computing systems. My current research interests include cloud computing, mobile edge clouds (MECs), Internet of Things (IoT), energy efficiency, algorithm design, and resource management. Through a holistic and interdisciplinary approach, I aim to contribute to the development of innovative solutions that address the challenges and opportunities presented by these dynamic fields. I aim to enhance the efficiency and responsiveness of cloud-based services by investigating novel architectures, resource allocation strategies, and edge computing paradigms. This involves addressing issues such as latency, bandwidth constraints, and scalability to create a seamless computing experience for users. Sustainability is a key concern in modern computing systems. I am committed to designing energy-efficient algorithms and architectures that minimize the environmental impact of computing infrastructure. This involves exploring techniques for dynamic power management, optimizing energy consumption in data centers, and developing green computing strategies to promote a more sustainable future. By leveraging advanced technologies, including machine learning algorithms, predictive analytics, and intelligent workload scheduling, my aim is to design and implement a comprehensive resource management system. This system will dynamically allocate resources based on workload demands, optimizing energy consumption while ensuring high-performance computing and cost-effectiveness. My research findings have been published in high-impact venues, including IEEE transactions.
I am a senior member of the IEEE, a professional member of the ACM, and a member of the ACS (Australian Computer Society). I served as a TPC member of a few prestigious international conferences, including CCGrid, GECON, ICCCI, FIT, and UCC, and as an associate editor of IEEE Access, Springer's Journal of Cloud Computing, and Springer's Journal of Cluster Computing. Apart from this, I am a member of the plagiarism committee for IEEE Access and a member of the IEEE TPDS reproducibility review board. I am the program director of iFuture, a leading research group at AWKUM, which has research collaboration with the CLOUDS Lab at the University of Melbourne, Australia, and the IoT Lab at Cardiff University, UK. I have produced 3 PhDs and several master-level students in cloud computing, MECs, and algorithm design for resource management. I have been included in the world's top 2% scientist list for four consecutive years, i.e., 2021, 2022, 2023, and 2024. I have experience writing funding proposals and have submitted proposals for approval to the Higher Education Commission (HEC), Pakistan, and TRC (The Research Council), Oman. I also have experience in reviewing funding proposals, and I am a member of the review board for NRPU (National Research Program for Universities) and CPEC-Collaborative Research Grants, HEC, Pakistan.
Cloud computing
Energy efficient scheduling
Resource management
Platform and infrastructure
Mobile edge computing
Internet of Things
Machine Learning
Performance aware computing
CBILab: Cloud computing, Big data, and Intelligence Laboratory, iFuture
iFuture is affiliated with the Melbourne CLOUDS Lab and IoT Lab, Cardiff University, UK