Varshney, P and Ramesh, S and Chhabra, S and Khochare, A and Simmhan, Y (2022) Resilient Execution of Data-triggered Applications on Edge, Fog and Cloud Resources. In: 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022, 16 - 19 May 2022, Taormina, pp. 473-483.
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Abstract
Internet of Things (loT) is leading to the pervasive availability of streaming data about the physical world, coupled with edge computing infrastructure deployed as part of smart cities and 5G rollout. These constrained, less reliable but cheap resources are complemented by fog resources that offer feder-ated management and accelerated computing, and pay-as-you-go cloud resources. There is a lack of intuitive means to deploy application pipelines to consume such diverse streams, and to execute them reliably on edge and fog resources. We propose an innovative application model to declaratively specify queries to match streams of micro-batch data from stream sources and trigger the distributed execution of data pipelines. We also design a resilient scheduling strategy using advanced reservation on reliable fogs to guarantee dataflow completion within a deadline while minimizing the execution cost. Our detailed experiments on over 100 virtual loT resources and for 10k task executions, with comparison against baseline scheduling strategies, illustrates the cost-effectiveness, resilience and scalability of our framework.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | 5G mobile communication systems; Cost effectiveness; Edge computing; Fog; Pipelines; Scheduling, Cheap resources; Cloud-computing; Computing infrastructures; Data triggered; Declarative; Edge computing; Physical world; Scheduling; Scheduling strategies; Streaming data, Fog computing |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 30 Aug 2022 11:58 |
| Last Modified: | 30 Aug 2022 11:58 |
| URI: | https://eprints.iisc.ac.in/id/eprint/76282 |
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