ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs. Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters’ flexibility in regulating their power consumption. Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime. In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy. We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice. Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints. Our results demonstrate that ECOGreen can provide 76% of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES. This translates into ECOGreen saving up to 71% electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.

Correlation-Aware Virtual Machine Allocation for Energy-Efficient Datacenters

David Atienza Alonso, Jungsoo Kim, Marcel Ledergerber, Martino Ruggiero

Server consolidation plays a key role to mitigate the continuous power increase of datacenters. The recent advent of scale-out applications (e.g., web search, MapReduce, etc.) necessitate the revisit of existing server consolidation solutions due to distinctively different characteristics compared to traditional high-performance computing (HPC), i.e., user interactive, latency critical, and operations on large data sets split across a number of servers. This paper presents a power saving solution for datacenters that especially targets the distinctive characteristics of the scale-out applications. More specifically, we take into account correlation information of core utilization among virtual machines (VMs) in server consolidation to lower actual peak server utilization. Then, we utilize this reduction to achieve further power savings by aggressively-yet-safely lowering the server operating voltage and frequency level. We have validated the effectiveness of the proposed solution using 1) multiple clusters of real-life scale-out application workloads based web search and 2) utilization traces obtained from real datacenter setups. According to our experiments, the proposed solution provides up to 13.7% power savings with up to 15.6% improvement of Quality-of-Service (QoS) compared to existing correlation-aware VM allocation schemes for datacenters.

ACM and IEEE Press2013

Multi-Objective System-Level Management of Modern Green Data Centers

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

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Ali Pahlevan, Xiaoyu Qu, Marina Zapater Sancho

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper we propose a heuristic- and a machine learning (ML)-based VM allocation method and compare them in terms of energy, quality of service (QoS), network traffic, migrations, and scalability for various DC scenarios. Then, we present a novel hyper-heuristic algorithm that exploits the benefits of both methods by dynamically finding the best algorithm, according to a user-defined metric. For optimality assessment, we formulate an integer linear programming (ILP)-based VM allocation method to minimize energy consumption and data communication, which obtains optimal results, but is impractical at runtime. Our results demonstrate that the ML approach provides up to 24% server-to-server network traffic improvement and reduces execution time by up to 480x compared to conventional approaches, for large-scale scenarios. On the contrary, the heuristic outperforms the ML method in terms of energy and network traffic for reduced scenarios. We also show that the heuristic and ML approaches have up to 6% energy consumption overhead compared to ILP-based optimal solution. Our hyper-heuristic integrates the strengths of both the heuristic and the ML methods by selecting the best one during runtime.

2017

Multi-Objective System-Level Management of Modern Green Data Centers

Ali Pahlevan

EPFL2019