Cloud Computing: The Next Revolution in Information Technology

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Cloud Computing The Next Revolution in
Information Technology
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Green Cloud Computing
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Energy-Efficient Cloud Computing Opportunities
and Challenges

• Dr. Rajkumar Buyya

Cloud Computing and Distributed Systems (CLOUDS)
LabDept. of Computer Science and Software
EngineeringThe University of Melbourne,
Australiawww.cloudbus.orgwww.buyya.comwww.manjr
asoft.com
Major Sponsors/Supporters
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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Clouds offer Subscription-Oriented IT Services
compute, apps, data,.. as a Service (..aaS)
Public Cloud
Cloud Manager
Private Cloud
Clients
Other Cloud Services
Govt. Cloud Services
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Cloud Computing
3 Main Types or Personalities Software-as-a-Servic
e (SaaS) A wide range of application services
delivered via various business models normally
available as public offering Platform-as-a-Servi
ce (PaaS) Application development platforms
provides authoring and runtime environment
Infrastructure-as-a-Service (IaaS) Also known
as elastic compute clouds, enable virtual
hardware for various uses
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Animoto, Sales Force, Google Document
Scientific Computing, Enterprise ISV, Social
Networking, Gaming
User Applications
Google AppEngine, MapReduce, Aneka, Microsoft
Azure
Cloud Programming Environment and Tools Web 2.0,
Mashups, Concurrent and Distributed Programming,
Workflow Cloud Hosting Platforms QoS Negotiation
Admission Control, Pricing, SLA Management,
Monitoring
Cloud Economy
User-level and infrastructure level Platform
Amazon EC2, GoGrid, RightScale, Jovent
Cloud Physical Resources Storage, virtualized
clusters, servers, network.
Infrastructure
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Public Cloud (IaaS)
User
User Middleware
Hybrid Cloud
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Several Benefits
Service Oriented
Elastic
Virtualized
Dynamic ( Distributed)
Cloud Computing
Autonomic
Shared(Economy of Scale)
Market Oriented (Pay As You Go)
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Dark side..

• Gartner Report 2007 IT industry contributes 2
  of world's total CO2 emissions
• U.S. EPA Report 2007 1.5 of total U.S. power
  consumption used by data centers which has more
  than doubled since 2000 and costs 4.5 billion

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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Powering Cloud Infrastructure

• Modern data centers, operating under the Cloud
  computing model, are hosting a variety of
  applications ranging from those that run for a
  few seconds (e.g. serving requests of web
  applications such as e-commerce and social
  networks portals) to those that run for longer
  periods of time (e.g. simulations or large
  dataset processing).
• However, Cloud Data Centers consume excessive
  amount of energy
• According to McKinsey report on Revolutionizing
  Data Center Energy Efficiency
• A typical data center consumes as much energy as
  25,000 households.
• The total energy bill for data centers in 2010
  was over 11 billion and energy costs in a
  typical data center doubles every five years.

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Where Does the Power Go?
Power Consumption in the Datacenter
Compute resources and particularly servers are at
the heart of a complex, evolving system!
Source APC
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Clouds Impact on the Environment

• Data centers are not only expensive to maintain,
  but also unfriendly to the environment.
• Carbon emission due to Data Centers worldwide is
  now more than both Argentina and the Netherlands
  emission.
• High energy costs and huge carbon footprints are
  incurred due to the massive amount of electricity
  needed to power and cool the numerous servers
  hosted in these data centers.

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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Background

• Traditionally, HPC (commodity clusters) Data
  center community has focused on performance
  (speed).
• At the same time, microprocessor vendors have not
  only doubled the number of transistors (and
  speed) every 18-24 months, but they have also
  doubled the power densities.
• Moores Law for Power Consumption

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Research Motivations of Power Aware/Energy
Efficient Computing

• Rapid uptake of Cloud Data Centers for hosting
  industrial applications
• Reducing the operational costs of powering and
  cooling Data Centers
• The tremendous increase in computer performance
  has come with an even grater increase in power
  usage.
• According to Eric Schmit, CEO of Google, what
  matter most to Google is not speed but power,
  because data centers can consume as much
  electricity as a city.
• Improving reliability
• As a rule of thumb, for every 10C increase in
  temperature, the failure rate of a system
  doubles.
• Computing environment affected the correctness of
  the results.
• The 18-node Linux cluster produced an answer
  outside the residual (i.e., a silent error) when
  running in dusty 85F warehouse but produced the
  correct answer when running in a 65F
  machine-cooled room.

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Reliability/Implications

• Reliability of Leading Edge Supercomputer (D.
  Reed, 2004)
• Estimated Cost of An hour of system downtime (W.
  Feng, (ACM Queue, 2003)

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Power Aware Computing

• Power Aware (PA) computing/communication
• The objective of PA computing/communications is
  to improve power management and consumption using
  the awareness of power consumption of devices.
• Power consumption is one of the most important
  considerations in mobile devices due to the
  limitation of the battery life.
• System level power management
• Recent devices (CPU, disk, communication links,
  etc.) support multiple power modes.
• Resource Management and Scheduling Systems can
  use these multiple power modes to reduce the
  power consumption.

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DVS (Dynamic Voltage Scaling)

• DVS (Dynamic Voltage Scaling) technique
• Reducing the dynamic energy consumption by
  lowering the supply voltage at the cost of
  performance degradation
• Recent processors support such ability to adjust
  the supply voltage dynamically.
• The dynamic energy consumption ? Vdd2
  Ncycle
• Vdd the supply voltage
• Ncycle the number of clock cycle
• An example

deadline
Power
Power
deadline
5.02
2.02
10 msec
25 msec
10 msec
25 msec
(a) Supply voltage 5.0 V
(b) Supply voltage 2.0 V
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DVS-based Power Aware Scheduling

• Motivation
• Develop Resource Management and Scheduling
  Algorithms that aim at minimizing the energy
  consumption at the same meet the job deadline.
• Exploit industrial move towards Utility Model /
  SLA-based Resource Allocation for Cloud Computing

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Taxonomy of Power Management Techniques
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Data Center Level
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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Cloud Providers Measures

• Cloud service providers need to adopt measures to
  ensure that their profit margin is not
  dramatically reduced due to high energy costs.
• Amazon.coms estimate the energy-related costs
  of its data centers amount to 42 of the total
  budget that include both direct power consumption
  and the cooling infrastructure amortized over a
  15-year period.
• Google, Microsoft, and Yahoo are building large
  data centers in barren desert land surrounding
  the Columbia River, USA to exploit cheap
  hydroelectric power.
• There is also increasing pressure from
  Governments worldwide to reduce carbon
  footprints, which have a significant impact on
  climate change.
• Carbon Tax (July 2012 in Australia) on industries

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Green Cloud performance ? energy efficiency

• As energy costs are increasing while availability
  dwindles, there is a need to shift focus from
  optimising data center resource management for
  pure performance alone to optimising for energy
  efficiency while maintaining high service level
  performance.
• We propose Green Cloud computing model that
  achieves not only efficient processing and
  utilisation of computing infrastructure, but also
  minimise energy consumption.

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Green Cloud Computing
Revenue
Power Consumption
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Cloud Usage Model
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Green Cloud Computing Architecture
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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Case Study 2 Dynamic VM Consolidation
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Three Sub-Problems

• When to migrate VMs?
• Host overload detection algorithms
• Host underload detection algorithms
• Which VMs to migrate?
• VM selection algorithms
• Where to migrate VMs?
• VM placement algorithms

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Proposed Power-Aware Algorithms

• Host overload detection
• Adaptive utilization threshold based algorithms
• Median Absolute Deviation algorithm (MAD)
• Interquartile Range algorithm (IQR)
• Regression based algorithms
• Local Regression algorithm (LR)
• Robust Local Regression algorithm (LRR)
• Host underload detection algorithms
• Migrating the VMs from the least utilized host
• VM selection algorithms
• Minimum Migration Time policy (MMT)
• Random Selection policy (RS)
• Maximum Correlation policy (MC)
• VM placement algorithms
• Heuristic for the bin-packing problem
  Power-Aware Best Fit Decreasing algorithm (PABFD)

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Performance Metrics

• SLA violation metrics
• Overloading Time Fraction (OTF) - the time
  fraction, during which active hosts experienced
  the 100 CPU utilization
• Performance Degradation due to VM Migrations
  (PDM)
• A combined SLA Violation metric (SLAV)
  SLAV OTF PDM
• A combined metric that captures both energy
  consumption and the level of SLA violations,
  Energy and SLA Violation (ESV)
• ESV Energy SLAV

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Simulation Setup

• CloudSim with a power package
• A Data Center consisting
• 800 heterogeneous physical servers containing HP
  ProLiant ML110 G4 and HP ProLiant ML110 G5
  servers.
• More than 1000 Heterogeneous VMs corresponding to
  Amazon EC2 instance types
• Workload traces from more than 1000 VMs from
  servers located in more than 500 places around
  the world.
• The data were obtained from the CoMon project, a
  monitoring infrastructure for PlanetLab

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Best Algorithm Combinations and Benchmark
Algorithms
Dynamic VM consolidation significantly reduces
energy consumption compared to non-power aware
allocation and static allocation policies, like
DVFS, NPA (non-power aware)
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Case Study 1 Key Observations

• Dynamic VM consolidation algorithms significantly
  outperforms static allocation policies.
• Heuristic-based dynamic VM consolidation
  algorithms substantially outperform the optimal
  online deterministic algorithm (THR-1.0) due to a
  vastly reduced level of SLA violations.
• The MMT policy produces better results compared
  to the MC and RS policies, meaning that the
  minimization of the VM migration time is more
  important than the minimization of the
  correlation between VMs allocated to a host.
• Dynamic VM consolidation algorithms based on
  local regression outperform the threshold-based
  and adaptive-threshold based algorithms due to
  better predictions of host overload, and
  therefore decreased SLA violations and the number
  of VM migrations.

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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Green Cloud or Brown Cloud?

• Ideally, for every server virtualized, save
• 700 and 7,000 kWh / year
• 4 tons of CO2 emissions / year
• Plus
• Power down underutilized physical servers, saving
  40
• Desktop management, saving 35 / year
• But currently

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Some Observations

• Datacenters has heterogeneous properties
• Geographically distributed datacenters (different
  environmental factors and electricity prices)
• Each resource site has different CPU
  configurations
• Each site has different energy efficiency
• Different Carbon-footprint

• Source Best Practices for Data Centers Lessons
  Learned from Benchmarking 22 Data Centers by
  Lawrence Berkeley National Laboratorys report

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Green Cloud Architecture
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Third Party Green Offer and Carbon Emission
Directory

• Carbon Emission Directory
• Contains data on Power Usage Effectiveness (PUE),
  cooling efficiency, carbon footprint, network
  cost
• Helps user to select cloud services with minimum
  carbon footprint
• Incentive for providers
• Advertising tool to increase the market share,
  e.g. Google
• Require more carbon transparency from providers
• Government role by enforcing policies such as
  Carbon Tax
• Green Offer Directory
• Incentive for users
• Choosing more carbon efficient hours
• Lists services with their discounted prices and
  green hours

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User Green Broker

• A typical Cloud broker
• Lease Cloud services
• Schedule applications
• Green Broker
• 1st layer Analyze user requirements
• 2nd layer Calculates cost and carbon footprint
  of services
• 3rd layer Carbon aware scheduling

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Provider Green Middleware
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Case Study IaaS Cloud

• Carbon Emission Directory Stores all carbon
  emission rates for each IaaS provider
• Green Offer Directory Receives number of VMs
  that can be initiated at a particular time for
  maximum energy efficiency
• Green Broker Computes schedule with the lowest
  carbon emission based on application requirements

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Carbon Efficient Green Policy (CEGP)

• Collect resource requests from user and resource
  site information such as VMs, carbon emission
  rate, DCiE, CPU power efficiency
• Sort jobs based on deadline
• Sort resource sites based on carbon footprint
• Schedule greedily the most urgent deadline jobs
  on the most power efficient resource site.

Carbon Emission
Datacenter Efficiency
Energy Efficiency of VM
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Simulation Setup

• Parallel Workload first week of LLNL Thunder
  trace from Parallel Workload Archive (PWA)
• Deadline generated based methodology proposed by
  Irwin et al. (2004)1
• Configuration of Cloud resource sites2

1D. Irwin, L. Grit, and J. Chase, Balancing risk
and reward in a market-based task service, in
Proc. of the 13th IEEE International Symposium on
High Performance Distributed Computing, Honolulu,
USA, 2004. 2 L. Wang and Y. Lu, Efficient Power
Management of Heterogeneous Soft Real-Time
Clusters, in Proc. of the 2008 Real-Time Systems
Symposium, Barcelona, Spain, 2008.
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EDF Carbon-Efficient (CEGP) VS EST (Early
Start-time) Algorithm (EST)
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Case Study 2 Summary

• Presented a Carbon Aware Green Cloud Framework to
  improve the carbon footprint of Cloud computing.
• Proposed framework provides incentives to both
  users and providers to utilize and deliver the
  most Green" services.
• Proposed a Carbon Efficient Green Policy (CEGP)
  for IaaS providers.
• Green Policy CEGP can save up to 23 energy while
  reducing the carbon footprint by about 25.

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Outline

• Cloud Computing at a Glance
• Cloud Benefits and Challenges
• Powering Cloud Infrastructure
• Energy Consumption, Costs, Implications
• Power-Aware Computing
• Trends, Foundations, Issues, Taxonomy
• Green Cloud Computing Framework
• Energy-Efficient Resource Management
• Within a Cloud Data Center
• Across Multiple Data Centers (InterCloud)
• Summary and Thoughts for Future

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Conclusions

• Clouds are essentially Data Centers hosting
  application services offered on a subscription
  basis. However, they consume high energy to
  maintain their operations.
• ? high operational cost environmental impact
• Proposed heuristics for energy-efficient dynamic
  VM consolidation that significantly reduce energy
  consumption, while providing a low level of SLA
  violations.
• Presented a Carbon Aware Green Cloud Framework to
  improve the carbon footprint of Cloud computing
• Open Issues
• EE Data Structures Algorithms
• EE Resource Management for other workloads (e.g.,
  workflows)

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References

• Keynote Paper
• R. Buyya, A. Beloglazov, J. Abawajy,
  Energy-Efficient Management of Data Center
  Resources for Cloud Computing A Vision,
  Architectural Elements, and Open Challenges,
  Proceedings of the 2010 International Conference
  on Parallel and Distributed Processing Techniques
  and Applications (PDPTA2010), Las Vegas, USA,
  July 12-15, 2010.
• Taxonomy EE InterClouds
• A. Beloglazov, R. Buyya, Y. Lee, A. Zomaya, A
  Taxonomy and Survey of Energy-Efficient Data
  Centers and Cloud Computing Systems, Advances in
  Computers, Volume 82, 47-111pp, M. Zelkowitz
  (editor), Elsevier, Amsterdam, The Netherlands,
  March 2011.
• S. Garg, C. Yeo, A Anandasivam, R. Buyya,
  Environment-Conscious Scheduling of HPC
  Applications on Distributed Cloud-oriented Data
  Centers, Journal of Parallel and Distributed
  Computing, 71(6)732-749, Elsevier Press,
  Amsterdam, The Netherlands, June 2011.

Wiley Press, New York, USA, Feb 2011
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Thanks for your attention!

• Are there any
• Questions?
• Comments/Suggestions

We welcome you to Study/Research with Us Do
Business with us! http/www.cloudbus.org
www.Manjrasoft.com rbuyya_at_unimelb.edu.au
raj_at_manjrasoft.com
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Green Cloud Computing
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Simulation Results ESV
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Simulation Results Energy
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Simulation Results SLAV
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Simulation Results the Number of VM Migrations