2018 IEEE International Conference on Services Computing (SCC) (2018)
San Francisco, CA, USA
Jul 2, 2018 to Jul 7, 2018
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.
cloud computing, Internet of Things, knapsack problems, pipeline processing, quality of service, resource allocation
S. S. Samant, M. Baruwal Chhetri, Q. Bao Vo, R. Kowalczyk and S. Nepal, "Towards End-to-End QoS and Cost-Aware Resource Scaling in Cloud-Based IoT Data Processing Pipelines," 2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2018, pp. 287-290.