Energy Efficient Cloud Control and Pricing in Geographically Distributed Data Centers

TL;DR

This paper presents a comprehensive cloud control and pricing framework for geographically distributed data centers that optimizes energy efficiency while maintaining QoS and revenue, using dynamic resource management and novel pricing schemes.

Contribution

It introduces a multi-method cloud control approach considering geotemporal inputs and QoS, along with new pricing schemes that adapt to resource availability and costs.

Findings

Significant energy cost savings achieved without QoS compromise.

Effective dynamic resource reallocation based on geotemporal inputs.

Proposed pricing schemes support energy-aware, high-performance cloud services.

Abstract

It is estimated that data centers constitute 1.5% of global electricity usage. At the same time, to serve increasing user requirements, modern cloud providers are operating multiple geographically distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures that we call geotemporal inputs. Furthermore, pricing policies at which cloud providers can offer computational resources depend on the quality of service (QoS). With such pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem. Existing cloud control methods are suitable only for a single data center or do not consider all the available cloud control actions that can reduce energy costs in geographically distributed data centers. In this thesis, we propose a pervasive cloud control approach consisting of multiple methods for dynamic resource reallocation and hardware configuration adapted to volatile geotemporal inputs. The proposed methods consider the QoS impact of cloud control actions and the data quality limits of time series forecasting methods. We offer a cloud controller design that supports future extensions when new decision support components need to be added. We also propose novel pricing schemes which account for the computational resource availability and costs that arise from our cloud control approach to enable both flexible, energy-aware and high performance cloud computing. We evaluate our methods empirically and in a number of simulations using historical traces of electricity prices, temperatures, workloads and other data. Our results show that significant energy cost savings are possible without harming the QoS or service revenue in geographically distributed cloud computing.