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CASPER: Carbon-Aware Scheduling and Provisioning for Distributed Web Services

Abel Souza, Shruti Jasoria, Basundhara Chakrabarty, Alexander Bridgwater, Axel Lundberg, Filip Skogh, Ahmed Ali-Eldin, David Irwin, Prashant Shenoy

TL;DR

This paper presents CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO).

Abstract

There has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the possibility to adapt the location, time, and intensity of processing to align with the availability of renewable and low-carbon energy. An example is a web application hosted across multiple cloud regions, each with varying carbon intensity based on their local electricity mix. Distributed load-balancing enables the exploitation of low-carbon energy through load migration across regions, reducing web applications carbon footprint. In this paper, we present CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO). We formulate CASPER as an multi-objective optimization problem that considers both the variable carbon intensity and latency constraints of the network. Our evaluation reveals the significant potential of CASPER in achieving substantial reductions in carbon emissions. Compared to baseline methods, CASPER demonstrates improvements of up to 70% with no latency performance degradation.

CASPER: Carbon-Aware Scheduling and Provisioning for Distributed Web Services

TL;DR

This paper presents CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO).

Abstract

There has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the possibility to adapt the location, time, and intensity of processing to align with the availability of renewable and low-carbon energy. An example is a web application hosted across multiple cloud regions, each with varying carbon intensity based on their local electricity mix. Distributed load-balancing enables the exploitation of low-carbon energy through load migration across regions, reducing web applications carbon footprint. In this paper, we present CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO). We formulate CASPER as an multi-objective optimization problem that considers both the variable carbon intensity and latency constraints of the network. Our evaluation reveals the significant potential of CASPER in achieving substantial reductions in carbon emissions. Compared to baseline methods, CASPER demonstrates improvements of up to 70% with no latency performance degradation.
Paper Structure (11 sections, 1 equation, 5 figures, 2 tables)

This paper contains 11 sections, 1 equation, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. System Design and Implementation
  4. Architecture
  5. Carbon Aware Scheduler
  6. Implementation
  7. Evaluation
  8. Setup
  9. Results
  10. Related Work
  11. Conclusion and Future Work

Figures (5)

  • Figure 1: Grid carbon intensity in 2022 across six distinct cloud regions showing 6$\times$ spatial variations.
  • Figure 2: CASPER: CAP and CAS provision and coordinate user workloads.
  • Figure 3: Illustration of CAS and weight calculation.
  • Figure 4: Redirection rate (a, b and c) and resource provisioning per-region and policies (d): Provisioning tends to increase in greener nearby regions.
  • Figure 5: Latency and carbon tradeoffs across policies.