Towards end-to-end QoS and cost-aware resource scaling in cloud-based IoT data processing pipelines
Date
2018
Authors
Samant, S.S.
Chhetri, M.B.
Vo, Q.B.
Kowalczyk, R.
Nepal, S.
Editors
Bilof, R.
Advisors
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Conference paper
Citation
Proceedings 2018 IEEE International Conference on Services Computing (IEEE SCC 2018), 2018 / Bilof, R. (ed./s), vol.nothing, pp.287-290
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2018 IEEE International Conference on Services Computing IEEE SCC 2018 (2 Jul 2018 - 7 Jul 2018 : California, USA)
Abstract
Ensuring cost-effective end-to-end QoS in a multilayer, multi-service, IoT data processing pipeline is a non-trivial challenge. The uncertainties surrounding the 3Vs of streaming data - variety, velocity and volume - impose dynamic QoS-driven resource requirements on each component (or service) of the pipeline and make adaptive resource management a complex task. Our overall research objective is to develop appropriate resource scaling strategies that dynamically adjust the resources allocated to each component in the pipeline so as to ensure end-to-end QoS fulfillment while optimizing the associated costs.
To this end, in this paper, we present our work in progress on a model for end-to-end QoS and cost-aware resource allocation for IoT data processing pipelines. We base our model on the well-established unbounded knapsack problem, which offers a simple yet powerful abstraction of constraint-based decision-making. We intend to develop resource scaling strategies on top of this model that can exploit resource and contract heterogeneity to achieve cost-optimal end-to-end QoS-aware resource allocations.
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Copyright 2018 IEEE