OpenFaaS is a popular open-source serverless platform in the academic and industrial world. Based on Ku-bernetes, OpenFaaS includes a simple scheduling policy that spreads functions on cluster computing resources. As such, it is not well-suited for managing latency-sensitive applications in a geo-distributed environment, where network latencies are nonnegligible and negatively affect the application response time. To overcome this issue, in this paper we present GOFS (Geo-distributed Scheduling in OpenFaaS), which extends OpenFaaS with network-aware scheduling capabilities. GOFS addresses the serverless application scheduling in a geo-distributed environment by either solving a suitable integer linear programming problem or using a greedy network-aware heuristic. However, its modular architecture facilitates the integration of other custom scheduling policies. A wide set of prototype-based results shows the advantages of the proposed network-aware solutions over other benchmark scheduling policies.
Rossi, F., Falvo, S., Cardellini, V. (2021). GOFS: Geo-distributed Scheduling in OpenFaaS. In 2021 IEEE Symposium on Computers and Communications (ISCC) (pp.1-6). IEEE [10.1109/ISCC53001.2021.9631492].
GOFS: Geo-distributed Scheduling in OpenFaaS
Cardellini Valeria
2021-01-01
Abstract
OpenFaaS is a popular open-source serverless platform in the academic and industrial world. Based on Ku-bernetes, OpenFaaS includes a simple scheduling policy that spreads functions on cluster computing resources. As such, it is not well-suited for managing latency-sensitive applications in a geo-distributed environment, where network latencies are nonnegligible and negatively affect the application response time. To overcome this issue, in this paper we present GOFS (Geo-distributed Scheduling in OpenFaaS), which extends OpenFaaS with network-aware scheduling capabilities. GOFS addresses the serverless application scheduling in a geo-distributed environment by either solving a suitable integer linear programming problem or using a greedy network-aware heuristic. However, its modular architecture facilitates the integration of other custom scheduling policies. A wide set of prototype-based results shows the advantages of the proposed network-aware solutions over other benchmark scheduling policies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.