Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing

Participating in demand response programs is a promising tool for reducing energy costs in data centers by modulating energy consumption. Towards this end, data centers can employ a rich set of resource management knobs, such as workload shifting and dynamic server provisioning. Nonetheless, these knobs may not be readily available in a cloud data center (CDC) that serves cloud tenants/users, because workloads in CDCs are managed by tenants themselves who are typically charged based on a usage-based or flat-rate pricing and often have no incentive to cooperate with the CDC operator for demand response and cost saving. Towards breaking such "split incentive" hurdle, a few recent studies have tried market-based mechanisms, such as dynamic pricing, inside CDCs. However, such mechanisms often rely on complex designs that are hard to implement and difficult to cope with by tenants. To address this limitation, we propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing for cloud resources unchanged for a long period. While it charges tenants based on a Usage-based Pricing (UP) as used by today's major cloud operators, it rewards tenants proportionally based on the time length that tenants set as deadlines for completing their workloads. This new mechanism is called Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the effectiveness of UPMR both analytically and empirically. We show that UPMR can reduce the CDC operator's energy cost by 12.9% while increasing its profit by 4.9%, compared to the state-of-the-art approaches used by today's CDC operators to charge their tenants

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

Energy Demand Response for High-Performance Computing Systems

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system\u27s demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems\u27 energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system\u27s emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system\u27s energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response

Carbon Responder: Coordinating Demand Response for the Datacenter Fleet

The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement demand response (DR) by adjusting their load based on grid signals. However, this presents challenges for private datacenters with diverse workloads and services. One of the key challenges is efficiently and fairly allocating power curtailment across different workloads. In response to these challenges, we propose the Carbon Responder framework. The Carbon Responder framework aims to reduce the carbon footprint of heterogeneous workloads in datacenters by modulating their power usage. Unlike previous studies, Carbon Responder considers both online and batch workloads with different service level objectives and develops accurate performance models to achieve performance-aware power allocation. The framework supports three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and Decentralized DR. We evaluate Carbon Responder polices using production workload traces from a private hyperscale datacenter. Our experimental results demonstrate that the efficient Carbon Responder policy reduces the carbon footprint by around 2x as much compared to baseline approaches adapted from existing methods. The fair Carbon Responder policies distribute the performance penalties and carbon reduction responsibility fairly among workloads

Power Management for Cloud-Scale Data Centers

Recent years have seen the rapid growth of large and geographically distributed data centers deployed by Internet service operators to support various services such as cloud computing. Consequently, high electricity bills, as well as negative environmental implications (e.g., CO2 emission and global warming) come along. In this thesis, we first propose a novel electricity bill capping algorithm that not only minimizes the electricity cost, but also enforces a cost budget on the monthly bill for cloud-scale data centers that impact the power markets. Our solution first explicitly models the impacts of the power demands induced by cloud-scale data centers on electricity prices and the power consumption of cooling and networking in the minimization of electricity bill. In the second step, if the electricity cost exceeds a desired monthly budget due to unexpectedly high workloads, our solution guarantees the quality of service for premium customers and trades off the request throughput of ordinary customers. We formulate electricity bill capping as two related constrained optimization problems and propose efficient algorithms based on mixed integer programming. We then propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained I optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP)

Improving data center efficiency through smart grid integration and intelligent analytics

The ever-increasing growth of the demand in IT computing, storage and large-scale cloud services leads to the proliferation of data centers that consist of (tens of) thousands of servers. As a result, data centers are now among the largest electricity consumers worldwide. Data center energy and resource efficiency has started to receive significant attention due to its economical, environmental, and performance impacts. In tandem, facing increasing challenges in stabilizing the power grids due to growing needs of intermittent renewable energy integration, power market operators have started to offer a number of demand response (DR) opportunities for energy consumers (such as data centers) to receive credits by modulating their power consumption dynamically following specific requirements. This dissertation claims that data centers have strong capabilities to emerge as major enablers of substantial electricity integration from renewables. The participation of data centers into emerging DR, such as regulation service reserves (RSRs), enables the growth of the data center in a sustainable, environmentally neutral, or even beneficial way, while also significantly reducing data center electricity costs. In this dissertation, we first model data center participation in DR, and then propose runtime policies to dynamically modulate data center power in response to independent system operator (ISO) requests, leveraging advanced server power and workload management techniques. We also propose energy and reserve bidding strategies to minimize the data center energy cost. Our results demonstrate that a typical data center can achieve up to 44% monetary savings in its electricity cost with RSR provision, dramatically surpassing savings achieved by traditional energy management strategies. In addition, we investigate the capabilities and benefits of various types of energy storage devices (ESDs) in DR. Finally, we demonstrate RSR provision in practice on a real server. In addition to its contributions on improving data center energy efficiency, this dissertation also proposes a novel method to address data center management efficiency. We propose an intelligent system analytics approach, "discovery by example", which leverages fingerprinting and machine learning methods to automatically discover software and system changes. Our approach eases runtime data center introspection and reduces the cost of system management.2018-11-04T00:00:00