In our computing-driven era, the escalating power consumption of modern data centers, currently constituting approximately 3% of global energy use, is a burgeoning concern. With the anticipated surge in usage accompanying the widespread adoption of AI technologies, addressing this issue becomes imperative. This paper discusses a potential solution: integrating data centers into grid programs such as “demand response” (DR). This strategy not only optimizes power usage without requiring new fossil-fuel infrastructure but also facilitates more ambitious renewable deployment by adding demand flexibility to the grid. However, the unique scale, operational knobs and constraints, and future projections of data centers present distinct opportunities and urgent challenges for implementing DR. This paper delves into the myths and opportunities inherent in this perspective on improving data center sustainability. While obstacles including creating the requisite software infrastructure, establishing institutional trust, and addressing privacy concerns remain, the landscape is evolving to meet the challenges. Noteworthy achievements have emerged in the development of intelligent solutions that can be swiftly implemented in data centers to accelerate the adoption of DR. These multifaceted solutions encompass dynamic power capping, load scheduling, load forecasting, market bidding, and collaborative optimization. We offer insights into this promising step towards making sustainable computing a reality.

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered. In this work, we propose an Adaptive policy with QoS Assurance that enables data centers to participate in regulation service programs with assurance on job QoS. Our policy regulates data center power through job scheduling and server power capping. QoS assurance is achieved by applying a queueing-theoretic result to our job scheduling strategy. We evaluate our policy by experiments on a real cluster. Our results demonstrate that the proposed policy reduces electricity costs by 25-56% while providing QoS assurance. On the other hand, the baseline policies cannot meet QoS constraints in 9 of the 14 workload traces tested.

Demand response programs offer an opportunity for large power consumers to save on electricity costs by modulating their power consumption in response to demand changes in the electricity grid. Multiple types of such programs exist; for example, regulation service programs enable a consumer to bid for a sustainable amount of power draw over a time period, along with a reserve amount they are able to provide at request of the electricity service provider. Data centers offer unique capabilities to participate in these programs since they have significant capacity to modify their power consumption through workload scheduling and CPU power limiting. This paper proposes a novel power management policy and a bidding policy that enable data centers to participate in regulation service programs under real-world constraints. The power management policy schedules computing jobs and applies server power-capping under both the constraints of power programs and the constraints of job Quality-of-Service (QoS). Simulations with workload traces from a real data center show that the proposed policies enable data centers to meet both the requirement of regulation service programs and the QoS requirement of jobs. We demonstrate that, by applying our policies, data centers can save their electricity costs by 10% while abiding by all the QoS constraints in a real-world scenario.

Effective power management in a data center is critical to ensure that power delivery constraints are met while maximizing the performance of users' workloads. Power limiting is needed in order to respond to greater-than-expected power demand. HPC sites have generally tackled this by adopting one of two approaches: (1) a system-level power management approach that is aware of the facility or site-level power requirements, but is agnostic to the application demands; OR (2) a job-level power management solution that is aware of the application design patterns and requirements, but is agnostic to the site-level power constraints. Simultaneously incorporating solutions from both domains often leads to conflicts in power management mechanisms. This, in turn, affects system stability and leads to irreproducibility of performance. To avoid this irreproducibility, HPC sites have to choose between one of the two approaches, thereby leading to missed opportunities for efficiency gains.This paper demonstrates the need for the HPC community to collaborate towards seamless integration of system-aware and application-aware power management approaches. This is achieved by proposing a new dynamic policy that inherits the benefits of both approaches from tight integration of a resource manager and a performance-aware job runtime environment. An empirical comparison of this integrated management approach against state-of-the-art solutions exposes the benefits of investing in end-to-end solutions to optimize for system-wide performance or efficiency objectives. With our proposed system-application integrated policy, we observed up to 7% reduction in system time dedicated to jobs and up to 11% savings in compute energy, compared to a baseline that is agnostic to system power and application design constraints.