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Energy Efficiency Support for Software Defined Networks: a Serverless Computing Approach

Fatemeh Banaie, Karim Djemame, Abdulaziz Alhindi, Vasilios Kelefouras

TL;DR

This paper is the first to realise the concept of modular Software-Defined Networks based on serverless functions in an energy-aware environment by adopting Function as a Service, resulting in cost reduction through fine resource provisioning granularity.

Abstract

Automatic network management strategies have become paramount for meeting the needs of innovative real-time and data-intensive applications, such as in the Internet of Things. However, meeting the ever-growing and fluctuating demands for data and services in such applications requires more than ever an efficient and scalable network resource management approach. Such approach should enable the automated provisioning of services while incentivising energy-efficient resource usage that expands throughout the edge-to-cloud continuum. This paper is the first to realise the concept of modular Software-Defined Networks based on serverless functions in an energy-aware environment. By adopting Function as a Service, the approach enables on-demand deployment of network functions, resulting in cost reduction through fine resource provisioning granularity. An analytical model is presented to approximate the service delivery time and power consumption, as well as an open-source prototype implementation supported by an extensive experimental evaluation. The experiments demonstrate not only the practical applicability of the proposed approach but significant improvement in terms of energy efficiency.

Energy Efficiency Support for Software Defined Networks: a Serverless Computing Approach

TL;DR

This paper is the first to realise the concept of modular Software-Defined Networks based on serverless functions in an energy-aware environment by adopting Function as a Service, resulting in cost reduction through fine resource provisioning granularity.

Abstract

Automatic network management strategies have become paramount for meeting the needs of innovative real-time and data-intensive applications, such as in the Internet of Things. However, meeting the ever-growing and fluctuating demands for data and services in such applications requires more than ever an efficient and scalable network resource management approach. Such approach should enable the automated provisioning of services while incentivising energy-efficient resource usage that expands throughout the edge-to-cloud continuum. This paper is the first to realise the concept of modular Software-Defined Networks based on serverless functions in an energy-aware environment. By adopting Function as a Service, the approach enables on-demand deployment of network functions, resulting in cost reduction through fine resource provisioning granularity. An analytical model is presented to approximate the service delivery time and power consumption, as well as an open-source prototype implementation supported by an extensive experimental evaluation. The experiments demonstrate not only the practical applicability of the proposed approach but significant improvement in terms of energy efficiency.
Paper Structure (22 sections, 14 equations, 7 figures, 4 tables, 1 algorithm)

This paper contains 22 sections, 14 equations, 7 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Motivation
  4. Contributions
  5. Related Work
  6. Microservice-based Provisioning of Network Services
  7. Architecture
  8. Network Layer
  9. Event Processing Layer
  10. Function Management Layer
  11. Analytical Model
  12. Delay Model
  13. Transmission Delay of Controller
  14. Processing Delay of Gateway
  15. Processing Delay of Serverless Nodes
  16. ...and 7 more sections

Figures (7)

  • Figure 1: Microservice-based IIoT service platform.
  • Figure 2: SDN/NFV architecture in a serverless environment.
  • Figure 3: System Model for distributed SDN.
  • Figure 4: Implementing the microservice-based network architecture (a) ONOS and OpenFaaS integration sequence diagram. (b) Protocol stack of ONOS.
  • Figure 5: Performance models results (a) Mean response time with varying the number of replicas from 2 to 10. (b) Total power consumption with varying the cold start delay. (c) Total power consumption with varying the number of containers from 1 to 10.
  • ...and 2 more figures