Ali Pahlevan
In our modern society, the average citizen has turned into a daily cloud user. Despite being virtually transparent for the user, internet services require data centers behind the scenes. Data centers burn several megawatts of power and their electricity bill is a major fraction of their costs. Hence, modern cloud data centers need to tackle efficiently the increasing demand for computing resources while at the same time addressing the energy efficiency challenge. This is a complex optimization problem that worsens as more constraints and objectives are added. In public clouds, virtualization transforms a data center into a flexible cloud infrastructure in which Virtual Machines (VMs) behave as separate entities that share physical hardware resources. Therefore, it is essential to develop resource provisioning policies that are aware of VMs characteristics and applicable in dynamic scenarios. To address these challenges, this thesis first presents heuristic and Machine Learning (ML)-based VM allocation methods for various data center scenarios. Then, a novel hyper-heuristic algorithm is proposed that exploits the benefits of both methods by dynamically finding the best one according to a user-defined metric.
However, optimizing the energy and cost of a single data center powered by the grid is not enough in today's cloud computing, where multiple data centers are used to deploy online services, and utilize renewable energy sources to reduce their carbon footprint. This thesis also presents a two-phase multi-objective VM placement along with a dynamic migration technique, for geo-distributed data centers coupled with renewable and Electrical Energy Storage (EES) sources to tackle the challenges of operational cost optimization and energy-performance trade-offs. Furthermore, in order to efficiently minimize cost, power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following providers' requests. Therefore, it is essential to develop bidding strategies for data centers to participate in emerging power markets together with power management policies that are aware of power market requirements at run-time. In this thesis I also propose a strategy to jointly optimize the data center demand-response provision problem and VM allocation that satisfies the hour-ahead power market constraints in the presence of renewable and EES energy.
Even if novel data center resource management techniques can efficiently tackle the dramatic increase in the number of servers, each computing server remains power limited due to effect of post-Dennard scaling. Therefore, techniques such as Near-Threshold Computing (NTC) need to complement novel system-level approaches to improve data centers' energy efficiency. For this purpose, I first use an accurate power modeling characterization for a new server architecture based on the Fully Depleted Silicon On Insulator (FD-SOI) process technology that enables NTC features. Then, I explore the new energy-performance trade-offs brought by NTC servers when executing virtualized applications. Finally, based on this analysis, I propose an energy proportionality-aware VM allocation method at data center level that exploits the knowledge of VMs characteristics together with the accurate power model presented for NTC servers. As a result, the proposed approach increases the energy proportionality of next-generation NTC-based data centers.
EPFL2019
