Peter Garraghan

2.6k total citations · 1 hit paper
51 papers, 1.8k citations indexed

About

Peter Garraghan is a scholar working on Computer Networks and Communications, Information Systems and Electrical and Electronic Engineering. According to data from OpenAlex, Peter Garraghan has authored 51 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Computer Networks and Communications, 45 papers in Information Systems and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Peter Garraghan's work include Cloud Computing and Resource Management (43 papers), IoT and Edge/Fog Computing (29 papers) and Distributed and Parallel Computing Systems (10 papers). Peter Garraghan is often cited by papers focused on Cloud Computing and Resource Management (43 papers), IoT and Edge/Fog Computing (29 papers) and Distributed and Parallel Computing Systems (10 papers). Peter Garraghan collaborates with scholars based in United Kingdom, China and Australia. Peter Garraghan's co-authors include Jie Xu, Paul Townend, Sukhpal Singh Gill, Renyu Yang, Ismael Solís Moreno, Rajkumar Buyya, Zhenyu Wen, Michael Rovatsos, Tao Lin and Xue Ouyang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Intelligent Transportation Systems and Future Generation Computer Systems.

In The Last Decade

Peter Garraghan

49 papers receiving 1.7k citations

Hit Papers

Transformative effects of IoT, Blockchain and Artificial ... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peter Garraghan United Kingdom 21 1.4k 1.2k 234 232 194 51 1.8k
Minxian Xu China 23 1.4k 1.0× 1.1k 0.9× 335 1.4× 299 1.3× 190 1.0× 54 1.9k
Blesson Varghese United Kingdom 17 1.7k 1.2× 1.1k 0.9× 367 1.6× 371 1.6× 336 1.7× 81 2.2k
Bahman Javadi Australia 21 1.3k 1.0× 1.2k 1.0× 208 0.9× 191 0.8× 97 0.5× 104 1.8k
David Carrera Spain 22 1.4k 1.0× 1.1k 0.9× 196 0.8× 234 1.0× 168 0.9× 83 1.7k
Fang Dong China 22 951 0.7× 703 0.6× 225 1.0× 383 1.7× 143 0.7× 193 1.5k
Jianli Pan United States 18 1.5k 1.1× 621 0.5× 514 2.2× 293 1.3× 195 1.0× 47 2.0k
Long Cheng China 23 835 0.6× 735 0.6× 254 1.1× 369 1.6× 161 0.8× 108 1.6k
Valentin Cristea Romania 19 1.1k 0.8× 657 0.5× 251 1.1× 196 0.8× 88 0.5× 164 1.6k
Chunlin Li China 24 1.4k 1.0× 880 0.7× 310 1.3× 217 0.9× 170 0.9× 121 1.7k
Eunmi Choi South Korea 15 910 0.6× 932 0.8× 126 0.5× 233 1.0× 111 0.6× 83 1.4k

Countries citing papers authored by Peter Garraghan

Since Specialization
Citations

This map shows the geographic impact of Peter Garraghan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter Garraghan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Garraghan more than expected).

Fields of papers citing papers by Peter Garraghan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Garraghan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter Garraghan. The network helps show where Peter Garraghan may publish in the future.

Co-authorship network of co-authors of Peter Garraghan

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Garraghan. A scholar is included among the top collaborators of Peter Garraghan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peter Garraghan. Peter Garraghan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lu, Yao, Lu Liu, John Panneerselvam, et al.. (2025). CECF: A DNN-Based Energy-Efficient Cloud-Edge Collaboration Framework for Intelligent Workload Scheduling in 6G-Enabled Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 26(10). 17889–17900.
2.
Garraghan, Peter, et al.. (2024). Energy-adaptive Network Switching via Intra-device Scaling. 2. 2853–2858.
3.
Garraghan, Peter, et al.. (2023). A material social view on data center waste heat: Novel uses and metrics. SHILAP Revista de lepidopterología. 3. 2 indexed citations
4.
Tuli, Shreshth, Sukhpal Singh Gill, Minxian Xu, et al.. (2022). HUNTER: : AI based holistic resource management for sustainable cloud. Lancaster EPrints (Lancaster University). 52 indexed citations
5.
Friday, Adrian, et al.. (2022). Trimmer: Cost-Efficient Deep Learning Auto-tuning for Cloud Datacenters. 374–384. 3 indexed citations
6.
Gill, Sukhpal Singh, et al.. (2021). The evolution of distributed computing systems: from fundamental to new frontiers. Computing. 103(8). 1859–1878. 19 indexed citations
7.
Garraghan, Peter, et al.. (2021). Controller-in-the-Middle. Lancaster EPrints (Lancaster University). 63–68. 3 indexed citations
8.
Yang, Renyu, Chunming Hu, Peter Garraghan, et al.. (2020). Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters. IEEE Transactions on Parallel and Distributed Systems. 31(7). 1499–1517. 21 indexed citations
9.
Bulman, James C. & Peter Garraghan. (2020). A Cloud Gaming Framework for Dynamic Graphical Rendering Towards Achieving Distributed Game Engines. Lancaster EPrints (Lancaster University). 4 indexed citations
10.
Friday, Adrian, et al.. (2020). Towards GPU Utilization Prediction for Cloud Deep Learning. Lancaster EPrints (Lancaster University). 8 indexed citations
11.
Gill, Sukhpal Singh, Shreshth Tuli, Minxian Xu, et al.. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things. 8. 100118–100118. 284 indexed citations breakdown →
12.
Garraghan, Peter, et al.. (2018). A Cross-Virtual Machine Network Channel Attack via Mirroring and TAP Impersonation. Bristol Research (University of Bristol). 606–613. 4 indexed citations
13.
Li, Xiang, Xiaohong Jiang, Peter Garraghan, & Zhaohui Wu. (2017). Holistic energy and failure aware workload scheduling in Cloud datacenters. Future Generation Computer Systems. 78. 887–900. 41 indexed citations
14.
Garraghan, Peter, et al.. (2016). Straggler Root-Cause and Impact Analysis for Massive-scale Virtualized Cloud Datacenters. IEEE Transactions on Services Computing. 12(1). 91–104. 53 indexed citations
15.
Garraghan, Peter, et al.. (2016). Tolerating Transient Late-Timing Faults in Cloud-Based Real-Time Stream Processing. 108–115. 3 indexed citations
16.
Ouyang, Xue, et al.. (2016). Straggler Detection in Parallel Computing Systems through Dynamic Threshold Calculation. 414–421. 19 indexed citations
17.
Wu, Zhaohui, Xiang Li, Peter Garraghan, et al.. (2016). Virtual Machine Level Temperature Profiling and Prediction in Cloud Datacenters. 735–736. 3 indexed citations
18.
Garraghan, Peter, Xue Ouyang, Paul Townend, & Jie Xu. (2015). Timely Long Tail Identification through Agent Based Monitoring and Analytics. 13. 19–26. 15 indexed citations
19.
Moreno, Ismael Solís, Peter Garraghan, Paul Townend, & Jie Xu. (2014). Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud. IEEE Transactions on Cloud Computing. 2(2). 208–221. 112 indexed citations
20.
Garraghan, Peter, et al.. (2013). Using Byzantine fault-tolerance to improve dependability in federated cloud computing. Lancaster EPrints (Lancaster University). 7. 221–237. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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