To offload or not? an analysis of big data offloading strategies from edge to cloud
Abstract
Large reductions in completion times can result from transfer of Big Data tasks from edge nodes to cloud resources, which can reduce the completion times by up to 97 % and meet client deadlines for computational tasks with responsive and agile solutions. Using scientific programs of varying computational complexity to model resource-intensive tasks, we demonstrate that the task complexity of the computational jobs, the Wide Area Network (WAN) speed and the potential overload of edge servers (as reflected by CPU workloads) are crucial for achieving total reductions in task completion time edge-cloud orchestrators are situated in edge nodes. With continuous access to the parameters of Wireless Local Area Network (WLAN) speed (for data exchanges between client and edge resources), WAN speed (for data exchanges between edge and cloud resources) edge server CPU workload and the complexities in Big Data analytics requirements, accurate edge-to-cloud offloading decisions can be made to minimise total task completion time by the use of cloud computing resources. This work supports the major research efforts have been recently made to develop novel resource orchestration solutions to flexibly link edge nodes with centralised cloud resources so as to maximise the efficiency with which such a continuum of resources can be accessed by users.
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Cloud computing has key problems with security, service speed, slow connections, and low bandwidth/high latency and jitter when mobile devices offload processing to cloud services.
“However, this technology faces key issues: security, speed of services and slow connections, which are often combined as low bandwidth/high latency and jitter as mobile devices offload computational and processing capacity to cloud computing services [3,4].”
From Edge AI: A survey (2023) · cites this paper as [4]
Cites
A Survey on the Edge Computing for the Internet of Things
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520 · cited 1× · see citing works
“THE relatively close proximity of Edge Computing nodes to users in comparison with the distance to consolidated data centres in Cloud Computing has generated considerable recent interest in understanding how Internet of Things (IoT) devices could benefit from accessing edge servers rather than relying solely on cloud…”
BackgroundBilal, K., Khalid, O., Erbad, A., & Khan, S. U. (2018) · Computer Networks · doi:10.1016/j.comnet.2017.10.002 · Cited by 334 · see citing works
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Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Self-citation
- Authors
- Singh, R., Kovacs, J., & Kiss, T.
- Affiliations
- University of Westminster (GB)
- University of Westminster,Centre for Parallel Computing,Department of Computer Science and Engineering,London,UK
- ORCID
- Singh, Raghubir
- Venue
- 2022 IEEE World AI IoT Congress (AIIoT)
- Publisher
- IEEE
- Published
- 2022-06-06
- Pages
- 046-052
- OpenAlex
- W4285407550
- Cited by
- 17
- References
- 2
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 6 · 2023: 6 · 2022: 1
- FWCI
- 3.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE World AI IoT Congress (AIIoT) (publishedVersion)
- WestminsterResearch (University of Westminster) (acceptedVersion)
- SZTAKI Publication Repository (Hungarian Academy of Sciences) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge computing, cloud computing, task offloading, big data analytics, task completion time, resource orchestration, deadline-constrained tasks, task complexity
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