Time series: theory and methods
Time series: theory and methods
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mercury and freon: temperature emulation and management for server systems
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Making scheduling "cool": temperature-aware workload placement in data centers
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Policies for dynamic clock scheduling
OSDI'00 Proceedings of the 4th conference on Symposium on Operating System Design & Implementation - Volume 4
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
Energy-aware server provisioning and load dispatching for connection-intensive internet services
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Experimental study of virtual machine migration in support of reservation of cluster resources
VTDC '07 Proceedings of the 2nd international workshop on Virtualization technology in distributed computing
IEEE Transactions on Parallel and Distributed Systems
Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
RACNet: a high-fidelity data center sensing network
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
ElasticTree: saving energy in data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Parsimonious linear fingerprinting for time series
Proceedings of the VLDB Endowment
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RainMon: an integrated approach to mining bursty timeseries monitoring data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Thermal Modeling of Hybrid Storage Clusters
Journal of Signal Processing Systems
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Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyberphysical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor measurements. The paper's main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observations in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data center's cyber-physical system. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintaining forecast accuracy; (iii) Unlike previous simulation-based studies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.