Abstract
Serverless computing allows developers to break their code into small components, known as functions. Being lightweight and modular, serverless functions have been increasingly employed in edge computing, where quick responses and adaptability are key to meeting strict latency requirements. In particular, edge nodes are intrinsically resource-constrained, and efficient resource allocation strategies are crucial for optimizing their usage. Different approaches exist in the literature, but they often overlook the dependencies among functions, that is, how and when functions invoke other functions, and obtain suboptimal results. This paper presents NEPTUNE\(^{+}\), a dependency-aware resource (CPU cores) allocation solution for serverless functions deployed on edge infrastructures. The approach extends NEPTUNE, an existing framework for managing edge infrastructures, with a new theoretical model and control algorithm that take function dependencies into account. We evaluated NEPTUNE\(^{+}\) by using three applications and it is able to allocate up to 42% fewer cores compared to NEPTUNE.
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Notes
- 1.
The work presented in this paper is partially supported by project ICTD4Dev, funded by AICS, and by project EMELIOT, funded by the MUR under the PRIN 2020 program (Contract 2020W3A5FY).
- 2.
Note that our approach allows a function \(f_i\) to invoke another function \(f_j\) both in sequential and parallel mode by having multiple, properly annotated, edges between i and j (as in multigraphs [1]). Here, we do not focus on such edge cases to keep our formalization as simple as possible.
- 3.
Note that an invocation is external with respect to the execution environment of the invoked function (as for the use of “external” in external response time).
- 4.
Source code available at https://github.com/deib-polimi/RAS/tree/dependencies.
- 5.
- 6.
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Baresi, L., Quattrocchi, G., Ticongolo, I.G. (2023). Dependency-Aware Resource Allocation for Serverless Functions at the Edge. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14419. Springer, Cham. https://doi.org/10.1007/978-3-031-48421-6_24
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