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Energy Efficient Scheduling for Serverless Systems

Michail Tsenos, Aristotelis Peri, Vana Kalogeraki

TL;DR

This work proposes an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications’ performance demands.

Abstract

Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive, event-based interface that makes the development and deployment of scalable cloud-based applications easier and cost-effective. An important aspect that has not been examined in these systems is their energy consumption during the application execution. One way to deal with this issue is to schedule the function invocations in an energy-efficient way. However, efficient scheduling of applications in a multi-tenant environment, like FaaS systems, poses significant challenges. The trade-off between the server's energy usage and the hosted functions' performance requirements needs to be taken into consideration. In this work, we propose an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications' performance demands. Our approach considers real-time performance measurements and historical data and applies a novel DVFS technique to minimize energy consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.

Energy Efficient Scheduling for Serverless Systems

TL;DR

This work proposes an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications’ performance demands.

Abstract

Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive, event-based interface that makes the development and deployment of scalable cloud-based applications easier and cost-effective. An important aspect that has not been examined in these systems is their energy consumption during the application execution. One way to deal with this issue is to schedule the function invocations in an energy-efficient way. However, efficient scheduling of applications in a multi-tenant environment, like FaaS systems, poses significant challenges. The trade-off between the server's energy usage and the hosted functions' performance requirements needs to be taken into consideration. In this work, we propose an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications' performance demands. Our approach considers real-time performance measurements and historical data and applies a novel DVFS technique to minimize energy consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.

Paper Structure

This paper contains 21 sections, 5 equations, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Architecture
  4. Processor Management and Scheduling Utility (PMSU)
  5. Energy Efficient Scheduler
  6. Energy Efficient Scheduler Methodology
  7. Scaling Component
  8. Scheduling Component:
  9. Implementation
  10. Evaluation
  11. Experimental Setup
  12. Evaluation Methodology
  13. Workloads
  14. Evaluation Results
  15. Energy consumption
  16. ...and 6 more sections

Figures (10)

  • Figure 1: Execution Time vs Energy Consumption for a benchmarking workload
  • Figure 2: System Architecture
  • Figure 3: Energy Efficient Scheduler Flowchart
  • Figure 4: Predicting an unknown function throughput (in Request Per Second - RPS) based on a known function with similar CPU utilization
  • Figure 5: Total energy consumption in Joules
  • ...and 5 more figures