A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes

As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data centers will continue to grow. A common approach to reducing this energy demand is to limit the power consumption of hardware components when workloads are experiencing bottlenecks elsewhere in the system. However, designing a resource controller capable of detecting and limiting power consumption on-the-fly is a complex issue and can also adversely impact application performance. In this paper, we explore the use of Reinforcement Learning (RL) to design a power capping policy on cloud compute nodes using observations on current power consumption and instantaneous application performance (heartbeats). By leveraging the Argo Node Resource Management (NRM) software stack in conjunction with the Intel Running Average Power Limit (RAPL) hardware control mechanism, we design an agent to control the maximum supplied power to processors without compromising on application performance. Employing a Proximal Policy Optimization (PPO) agent to learn an optimal policy on a mathematical model of the compute nodes, we demonstrate and evaluate using the STREAM benchmark how a trained agent running on actual hardware can take actions by balancing power consumption and application performance.

Generative Anomaly Detection for Time Series Datasets

Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have their deployment and management costs. To address these challenges, we present a distributed and scalable deep-learning prediction serving system called Barista and make the following contributions. First, we present a fast and effective methodology for forecasting workloads by identifying various trends. Second, we formulate an optimization problem to minimize the total cost incurred while ensuring bounded prediction latency with reasonable accuracy. Third, we propose an efficient heuristic to identify suitable compute resource configurations. Fourth, we propose an intelligent agent to allocate and manage the compute resources by horizontal and vertical scaling to maintain the required prediction latency. Finally, using representative real-world workloads for urban transportation service, we demonstrate and validate the capabilities of Barista.

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased focus on exploiting edge computing resources for low-latency prediction serving and often a lack of a complete understanding of resources required to execute ML workflows efficiently, ML model deployment demands expertise for managing the lifecycle of ML workflows efficiently and with minimal cost. To address these challenges, we propose an end-to-end data analytics, a serverless platform called Stratum. Stratum can deploy, schedule and dynamically manage data ingestion tools, live streaming apps, batch analytics tools, ML-as-a-service (for inference jobs), and visualization tools across the cloud-fog-edge spectrum. This paper describes the Stratum architecture highlighting the problems it resolves.