The rapid growth of AI has significantly increased data center energy demand, placing increasing pressure on power grids. Demand Response (DR) programs utilize the flexibility of power consumers, such as data centers, to help balance the supply and demand in power grids. In collaborative data center DR participation, multiple data centers share information with an external coordinator for improved power dispatch and quality of service for their workloads. However, disclosing sensitive information such as power usage and workload performance to an untrusted third party raises significant privacy concerns. To address this, we use Multi-Party Computation (MPC) to perform secure power dispatch without revealing sensitive inputs. Standard MPC has heavy communication overhead, which challenges real-time DR requirements. To meet real-time DR requirements, we optimize our system by tailoring fixed-point bitwidths and substituting expensive divisions with polynomial approximations and Newton-Raphson iterations. Evaluated using the MP-SPDZ library, our optimizations yield up to 33 $$\times $$ fewer communication rounds and a 45 $$\times $$ speedup, delivering sub-second latency for up to 16 data centers while matching the power dispatch accuracy of the baseline.

As both direct and embodied carbon emissions of data centers is expected to increase in coming years, estimating data center carbon footprints has become essential to understand and address the sustainability challenges. Due to the lack of publicly available data regarding data center operations and infrastructure, there is a need for tools that can provide carbon emission estimations with a minimal amount of data. To address this challenge, we present our open-source tool,CarbonMeter, which allows users to quickly generate carbon footprint estimates for a given data center, using only data center area and data center power capacity information.CarbonMeterallows users to easily adjust parameters that affect carbon emissions and provides a breakdown of operational and embodied footprint. Furthermore,CarbonMeterenables swift evaluation of workload migration strategies to optimize electricity costs, carbon emissions and renewable curtailment by using real-time signals in the Nordic region. We evaluate the benefits of workload migration decisions byCarbonMeteron multiple case studies encompassing data centers located in Norway, Denmark, and Germany. Our results show 6% to 21% electricity cost reductions, while reducing the carbon emissions up to 49%.

Cloud computing has become integral to modern technology infrastructure, supporting a wide range of services from e-commerce to AI applications. Chameleon is a large-scale, configurable testbed designed to enable edge-to-cloud research through full bare-metal provisioning, virtualization, and diverse hardware resources, which is built on a leading open source cloud platform OpenStack. However, monitoring Chameleon’s heterogeneous infrastructure is challenging, particularly across Open-Stack services and hardware components. Traditional threshold-based alerting methods struggle to keep up with the scale and complexity of such environments. In this work, we present an anomaly detection framework for OpenStack services in the Chameleon Cloud. We curate and publish the first dataset of resource usage metrics collected from OpenStack control plane services. We evaluate four state-of-the-art unsupervised multivariate time series models, namely TranAD, Prodigy, USAD, and OmniAnomaly, on this dataset and share key insights from deploying them. Our findings indicate that for our use case, while all models achieve high F1 scores, training with three days of healthy data effectively balances training cost and detection accuracy.

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.

As the number of cores per server node increases, designing multi-threaded applications has become essential to efficiently utilize the available hardware parallelism. Many application domains have started to adopt multi-threaded programming; thus, efficient management of multi-threaded applications has become a significant research problem. Efficient execution of multi-threaded workloads on cloud environments, where applications are often consolidated by means of virtualization, relies on understanding the multi-threaded specific characteristics of the applications. Furthermore, energy cost and power delivery limitations require data center server nodes to work under power caps, which bring additional challenges to runtime management of consolidated multi-threaded applications. This article proposes a dynamic resource allocation technique for consolidated multi-threaded applications for power-constrained environments. Our technique takes into account application characteristics specific to multi-threaded applications, such as power and performance scaling, to make resource distribution decisions at runtime to improve the overall performance, while accurately tracking dynamic power caps. We implement and evaluate our technique on state-of-the-art servers and show that the proposed technique improves the application performance by up to 21% under power caps compared to a default resource manager.

Today's US power markets offer new opportunities for the energy consumers to reduce their energy costs by first promising an average consumption rate for the next hour and then by following a regulation signal broadcast by the independent system operators (ISOs), who need to match supply and demand in real time in presence of volatile and intermittent renewable energy generation. This paper leverages the power regulation capabilities of the servers so as to enable the data centers to participate in these emerging power markets. As the data center energy consumption continues to grow, proposed participation in the power markets has the promise to achieve significant monetary savings. The paper first solves a data center regulation service (RS) optimization problem to determine the optimal average power consumption and regulation quantity that minimize the energy cost. We then propose a dynamic server power capping technique to modulate the real-time power consumption in response to ISO requests while maintaining the desired quality-of-service (QoS). Experiments on a real-life server demonstrate that our technique can reduce the energy cost by 29% on average compared to using a fixed power cap.

Power capping on server nodes has become an essential feature in data centers for controlling energy costs and peak power consumption. More than half of the server nodes are virtualized in today's data centers; thus, providing a practical power capping technique for consolidated virtual environments is a significant research problem. This paper proposes a power capping technique, vCap, which makes resource allocation decisions to maximize the Quality-of-Service (QoS) while meeting the power constraints in virtualized servers that run multi-threaded applications. For a given set of applications, vCap first decides which applications to co-schedule based on application scalability and then optimizes the QoS in an application-aware manner for each VM by adaptively adjusting the CPU resources. Experiments on real-life multi-core servers show that vCap provides 12% higher energy efficiency in comparison to the state-of-the-art power capping techniques, while adhering to the power cap 92% of the time within a 2W error margin.

Future computing clusters will prevalently run parallel workloads to take advantage of the increasing number of cores on chips. In tandem, there is a growing need to reduce energy consumption of computing. One promising method for improving energy efficiency is co-scheduling applications on compute nodes. Efficient consolidation for parallel workloads is a challenging task as a number of factors, such as scalability, inter-thread communication patterns, or memory access frequency of the applications affect the energy/performance tradeoffs. This paper evaluates the impact of co-scheduling parallel workloads on the energy consumed per useful work done on real-life servers. Based on this analysis, we propose a novel multi-level technique that selects the best policy to co-schedule multiple workloads on a multi-core processor. Our measurements demonstrate that the proposed multi-level co-scheduling method improves the overall energy per work savings of the multi-core system up to 22% compared to state-of-the-art techniques.

As the number of cores per processor grows, there is a strong incentive to develop parallel workloads to take advantage of the hardware parallelism. In comparison to single-threaded applications, parallel workloads are more complex to characterize due to thread interactions and resource stalls. This paper presents an accurate and scalable method for determining the optimal system operating points (i.e., number of threads and DVFS settings) at runtime for parallel workloads under a set of objective functions and constraints that optimize for energy efficiency in multi-core processors. Using an extensive training data set gathered for a wide range of parallel workloads on a commercial multi-core system, we construct multinomial logistic regression (MLR) models that estimate the optimal system settings as a function of workload characteristics. We use L1-regularization to automatically determine the relevant workload metrics for energy optimization. At runtime, our technique determines the optimal number of threads and the DVFS setting with negligible overhead. Our experiments demonstrate that our method outperforms prior techniques with up to 51% improved decision accuracy. This translates to up to 10.6% average improvement in energy-performance operation, with a maximum improvement of 30.9%. Our technique also demonstrates superior scalability as the number of potential system operating points increases.

The ability to cap peak power consumption is a desirable feature in modern data centers for energy budgeting, cost management, and efficient power delivery. Dynamic voltage and frequency scaling (DVFS) is a traditional control knob in the tradeoff between server power and performance. Multi-core processors and the parallel applications that take advantage of them introduce new possibilities for control, wherein workload threads are packed onto a variable number of cores and idle cores enter low-power sleep states. This paper proposes Pack & Cap, a control technique designed to make optimal DVFS and thread packing control decisions in order to maximize performance within a power budget. In order to capture the workload dependence of the performance-power Pareto frontier, a multinomial logistic regression (MLR) classifier is built using a large volume of performance counter, temperature, and power characterization data. When queried during runtime, the classifier is capable of accurately selecting the optimal operating point. We implement and validate this method on a real quad-core system running the PARSEC parallel benchmark suite. When varying the power budget during runtime, Pack & Cap meets power constraints 82% of the time even in the absence of a power measuring device. The addition of thread packing to DVFS as a control knob increases the range of feasible power constraints by an average of 21% when compared to DVFS alone and reduces workload energy consumption by an average of 51.6% compared to existing control techniques that achieve the same power range.