We consider a set of network users (nodes) that generate latency-constrained service requests requiring the execution of computing tasks on servers located either in the cloud or at the network edge. We explore the efficiency of a distributed server selection strategically performed by individual nodes. In an earlier analysis, we argued for a stateless centralized allocation based on a probabilistic selection between edge and cloud servers. In that proposal, the optimal share of edge and cloud tasks was computed according to static network characteristics, with no knowledge of the actual network state. In this new study, we perform an analysis based on game theory, where we compare the globally optimal allocation performed at a central level against a distributed server selection driven by the selfish objectives of individual nodes. The inefficiency of the selfish allocation can be computed as the price of anarchy, which is shown to be very small, thus justifying a distributed strategic implementation of stateless policies. This insight is precious for designing algorithms for server selection and quantitatively proves the efficiency of distributed selfish approaches.

Efficiency of Distributed Selection of Edge or Cloud Servers under Latency Constraints

Castagno, Paolo;Sereno, Matteo;
2023-01-01

Abstract

We consider a set of network users (nodes) that generate latency-constrained service requests requiring the execution of computing tasks on servers located either in the cloud or at the network edge. We explore the efficiency of a distributed server selection strategically performed by individual nodes. In an earlier analysis, we argued for a stateless centralized allocation based on a probabilistic selection between edge and cloud servers. In that proposal, the optimal share of edge and cloud tasks was computed according to static network characteristics, with no knowledge of the actual network state. In this new study, we perform an analysis based on game theory, where we compare the globally optimal allocation performed at a central level against a distributed server selection driven by the selfish objectives of individual nodes. The inefficiency of the selfish allocation can be computed as the price of anarchy, which is shown to be very small, thus justifying a distributed strategic implementation of stateless policies. This insight is precious for designing algorithms for server selection and quantitatively proves the efficiency of distributed selfish approaches.
2023
21st Mediterranean Communication and Computer Networking Conference (MedComNet 2023)
Island of Ponza, Italy
13-15, June, 2023
Proceedings of 21st Mediterranean Communication and Computer Networking Conference (MedComNet 2023)
IEEE
158
166
979-8-3503-3884-3
https://ieeexplore.ieee.org/document/10168861
Edge computing; Radio access network; Game theory; Distributed policies; Performance evaluation
Mancuso, Vincenzo; Badia, Leonardo; Castagno, Paolo; Sereno, Matteo; Ajmone Marsan, Marco
File in questo prodotto:
File Dimensione Formato  
Gandalph.pdf

Accesso aperto con embargo fino al 01/01/2025

Tipo di file: PREPRINT (PRIMA BOZZA)
Dimensione 335.42 kB
Formato Adobe PDF
335.42 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1947402
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
social impact