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Serverless computing is a promising new event- driven programming model that was designed by cloud vendors to expedite the development and deployment of scalable web services on cloud computing systems. Using the model, developers write applications that consist of simple, independent, stateless functions that the cloud invokes on-demand (i.e. elastically), in response to system-wide events (data arrival, messages, web requests, etc.). In this work, we present STOIC (Serverless TeleOperable HybrId Cloud), an application scheduling and deployment system that extends the serverless model in two ways. First, it uses the model in a distributed setting and schedules application functions across multiple cloud systems. Second, STOIC sup- ports serverless function execution using hardware acceleration (e.g. GPU resources) when available from the underlying cloud system. We overview the design and implementation of STOIC and empirically evaluate it using real-world machine learning applications and multi-tier (e.g. edge-cloud) deployments. We find that STOIC’s combined use of edge and cloud resources is able to outperform using either cloud in isolation for the applications and datasets that we consider.
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Towards Optimal System Deployment for Edge Computing: A Preliminary Study," 2020 29th International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, 2020, pp. 1-6, doi: 10.1109/ICCCN49398.2020.9209754.
In this preliminary study, we consider the server allocation problem for edge computing system deployment. Our goal is to minimize the average turnaround time of application requests/tasks, generated by all mobile devices/users in a geographical region. We consider two approaches for edge cloud deployment: the flat deployment, where all edge clouds co-locate with the base stations, and the hierarchical deployment, where edge clouds can also co-locate with other system components besides the base stations. In the flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other. We also show that the hierarchical deployment approach has great potentials in minimizing the system’s average turnaround time. We conduct various simulation studies using the CloudSim Plus platform to verify our theoretical results. The collective findings trough theoretical analysis and simulation results will provide useful guidance in practical edge computing system deployment.
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- Award ID(s):
- 1909824
- PAR ID:
- 10212792
- Date Published:
- Journal Name:
- International Conference on Computing Communication and Networking Technologies
- ISSN:
- 2473-7674
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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