Walter%20Binder

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Green Computing Energy Consumption Optimized
Service Hosting

• Walter Binder
• University of Lugano, Switzerland
• Niranjan Suri
• IHMC, Florida, USA

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Motivation

• Data centers are becoming ubiquitous
• Large installations of computer systems
• Providing critical services
• Data centers are big power consumers
• Continuously operating computers, regardless of
  the load
• Cooling

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Reducing Power Consumption

• Green Grid consortium advocates data center
  design and management to improve energy
  efficiency
• Right-sizing data centers at design time
• Energy-efficient cooling
• Virtualization (multiple servers on same physical
  machine)
• Processor power saving (e.g., clock rate
  depending on load)
• Powering down unused machines
• Computers with dedicated roles (e.g., computers
  performing backups)

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Our Approach

• Load on machines varies over time
• Turn off subset of unnecessary machines,
  respectively restart machines according to load
• Problems
• Load is distributed over multiple machines
• Load reduction typically also distributed across
  multiple machines
• Need to consolidate load on a subset of machines
  in order to free up machines that can be turned
  off
• Goal Minimum number of machines running
• Constraint QoS must be ensured
• Service-Level Agreements (SLAs) must not be
  violated

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Example
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Service Types

• Hosting environment may offer multiple service
  types
• Service type consists of
• Service interface
• SLA defining QoS parameters
• SLA parameters specified according to a common
  ontology
• WS-Agreement, WSLA, SLAng, etc.
• Here Single QoS parameter Response time

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Stateless versus Stateful Services

• Stateless service
• Requests are independent
• After completing all pending requests, a
  stateless service may be stopped
• Stateful service
• Requests in one session may depend on prior
  requests in the same session
• Sessions may be explicitly terminated by clients,
  or expire after some period of inactivity
• After termination of all sessions, a stateful
  service may be stopped

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Hosting Environment (1)

• Dedicated machines for three different purposes
• File servers
• Provide all data sources
• Compute servers
• Execute service requests
• Dispatchers
• Receive service requests and choose compute
  servers to handle them
• Decide on shutdown and restart of compute servers
• Dispatchers and file servers are continuously
  running
• Only idle compute servers may be shut down

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Hosting Environment (2)
Compute servers
File servers
Dispatcher
Clients
dataaccess
requests
dispatch
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Hosting Environment (3)

• Heterogeneous environment
• Machines have different computing resources
• Dynamically changing environment
• New machines may be added
• Cores may fail
• Compute servers may host any number of service
  types, and a service type may be hosted by any
  number of compute servers
• Compute servers are ranked according to energy
  efficiency

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Node Manager

• Each compute server runs a Node Manager component
• Monitors idle time and average response time for
  each service type
• Communicates measurements to dispatcher
• Handles server shutdown upon request from
  dispatcher
• Notifies dispatcher upon startup

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Shutdown of Compute Severs

• Dispatcher notifies Node Manager on compute
  server to prepare shutdown
• No further service requests are dispatched to the
  compute server
• Node Manager waits for
• Completion of all previously accepted requests
• Termination of all active sessions
• Alternative Migration of sessions

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Shutdown Options

• Complete shutdown
• No power consumption
• Ensures clean state upon restart (e.g., no memory
  leaks)
• Slow restart
• Hibernation
• No power consumption
• Memory saved on persistent storage
• Resume by reloading memory snapshot
• Standby
• Reduced power consumption
• Processor stopped, but memory remains active
• Fast restart

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Restart of Compute Servers

• Wake on LAN
• Magic packet is broadcast to LAN
• Special header 0xFF repeated 6 times
• MAC address of the machine to restart
• Dispatcher initiates compute server restart
• Node Manager notifies dispatcher of completed
  restart
• Dispatcher needs to know MAC addresses of all
  compute servers

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Service Dispatch Definitions

• n compute servers lts1,,sngt
• Sorted according to energy efficiency
• sx more energy efficient than sy ? x lt y
• In each configuration
• s1 sr are running (1 r n)
• sr sn are shut down (or in the process
  of shutting down)
• pT(i) probability that request for
  service type T is dispatched to si

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Service Dispatch upon Request

• Take a random number z (0 z 1 uniform
  distribution)
• Choose sc such that c min i (1 i n)
  (z sum(1 i pT(i)))
• Related to lottery scheduling
• Tickets instead of probabilities

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Update of Probabilities (1)

• In regular intervals, dispatcher obtains
  monitoring data from Node Managers of running
  compute servers
• If si had idle time and si had no problem meeting
  the SLAs
• Increase load on si, reduce load on sr
• pT(r) pT(r) ?p
• pT(i) pT(i) ?p
• If r gt 1 and for all service types TpT(r) 0,
  initiate shutdown of sr

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Update of Probabilities (2)

• If compute server si violates the SLA for a
  service type T (overload situation)
• First try to find a running compute server sk (1
  k r) that has idle time and met the SLAs of
  all service types
• Balance load between si and sk
• pT(i) pT(i) ?p
• pT(k) pT(k) ?p
• If there is no such compute server sk, initiate
  restart of sr1

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Future Work (1)

• Testbed and evaluation
• Main evaluation metric Energy savings for given
  workloads
• Service performance must be modeled
• Traces of service execution in data centers
  needed
• Migration of sessions
• Reduces the time for preparing shutdown
• Complex optimization criteria
• Minimize number of service types hosted on the
  same compute server
• Consider estimated shutdown preparation time when
  choosing the compute server to shut down

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Future Work (2)

• Distribution and replication
• Service dispatcher must not become bottleneck
• Fault tolerance
• Dispatcher must detect compute server failures
• Dispatcher must not become single point of
  failure
• Sudden load fluctuations
• Shutting down machines increases vulnerability
  wrt. denial-of-service attacks

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Conclusions

• Data centers are growing and consume huge amounts
  of electrical energy
• Energy can be saved by powering down unused
  machines according to the current load
• Requires consolidation of services on a subset of
  the available machines
• Probabilistic approach to energy
  consumption-aware load-balancing