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Published byDustin Byrd Modified over 9 years ago
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HETEROGENEOUS SENSOR NETWORK FOR DATACENTER WORKLOAD AND POWER MANAGEMENT JORGE ORTIZ CS294-14 ARCHITECTURES FOR INTERNET DATACENTERS OCTOBER 10, 2007 Sensing the Datacenter
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Datacenters Consume Lots of Power Datacenter power consumption increasing Environmental Protection Agency (EPA) report shows power- consumption has doubled in last 5 years 1.5% of total U.S. Electricity Consumption in 2006 Projected to double again in next 5 years Datacenter under-provisioned for saving power Sensing and control separate from load balancer Protocols and applications are energy unaware What I propose: Couple the sensing and control with the load balancer/tasking decisions Dynamic Adjustment Graceful workload adjustment for saving power
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Sensors Facilitate Dynamic Energy Accounting Include power-related input into the protocol and management loop Make use of equipment sensors already available to gather information about power consumption Power meters to attach to server/racks On-board temperature sensors In-band network monitors Wireless sensor network technology to include out-of- band monitoring infrastructures Single on-board sensors sometimes give wacky readings Array of sensors adds redundancy and improves accuracy Wireless motes ease deployment and data collection
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Loose Ends Datacenter-scale workloads unavailable Monitoring machine room activity not at same scale as internet datacenter, but it’s a good start Power metering equipment needed A couple of one-outlet monitors already in use Access to 420A (Soda Hall Machine room) or the RadLab Machine room Direct access to specific machines in (either) machine room
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Semester Plan Provision the Soda Hall machine room (420A) or the RadLab machine room with wireless sensors (temperature, humidity, etc.) Attach power meters to a set of servers in the machine room Setup process (top), network (netstat), and disk monitors (iostat) to determine distribution of machine activity Analyze gathered data Formulate a model that relates temperature and power consumption Analyze the relationship between component utilization and ambient temperature Machine-learning techniques for formulation of predictive models for graceful workload degradation
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