1. Cloud Computing: The Next Revolution in Information Technology

2. Green Cloud Computing

3. Energy-Efficient Cloud Computing: Opportunities and Challenges
Dr. Rajkumar Buyya
Cloud Computing and Distributed Systems (CLOUDS) Lab Dept. of Computer Science and Software Engineering The University of Melbourne, Australia
Major Sponsors/Supporters

4. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

5. Clouds offer Subscription-Oriented IT Services: {compute, apps, data,
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

6. 3 Main Types or Personalities
Cloud Computing
3 Main Types or Personalities
Software-as-a-Service (SaaS): A wide range of application services delivered via various business models normally available as public offering
Platform-as-a-Service (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
Cloud computing is a new paradigm and people are saying it will revolutionize the IT industry. It provides all IT needs through three services: SaaS, PaaS and IaaS

7. Scientific Computing, Enterprise ISV, Social Networking, Gaming
SaaS
Animoto, Sales Force, Google Document
Scientific Computing, Enterprise ISV, Social Networking, Gaming
User Applications
Google AppEngine, MapReduce, Aneka, Microsoft Azure
PaaS
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
IaaS
Amazon EC2, GoGrid, RightScale, Jovent
Cloud Physical Resources: Storage, virtualized clusters, servers, network.
Infrastructure

8. (Heterogeneous Resources)
Public Cloud
(IaaS)
User
User Middleware
Master Node
Private Cloud
(Heterogeneous Resources)
Slave Nodes
(Cluster)
Hybrid Cloud

9. Shared (Economy of Scale)
Several Benefits……
Service Oriented
Elastic
Virtualized
Dynamic
(& Distributed)
Cloud
Computing
Autonomic
Shared (Economy of Scale)
Market Oriented
(Pay As You Go)

10. 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
Everyone is now aware of increasing contribution of IT industry to environmental pollution.

11. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

12. 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.
Everyone is now aware of increasing contribution of IT industry to environmental pollution.

13. Where Does the Power Go? Power Consumption in the Datacenter
Computer Rm. AC %
Server/Storage %
Conversion %
Network %
Lighting %
Power Consumption in the Datacenter
Compute resources and particularly servers are at the heart of a complex, evolving system!
With the improvement of technology, the power consumption of datacenters is also increasing. Most of the power actually goes in the IT applications running on the servers. Even in cooling, the energy consumption is due to server heat.
Source: APC

14. 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.

15. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

16. 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 months, but they have also doubled the power densities.
Moore’s Law for Power Consumption:

17. 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 10°C 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 85°F warehouse but produced the correct answer when running in a 65°F machine-cooled room.

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

19. 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.
Fujitsu disk drive has 4 power modes: busy, idle, standby, and sleep.
CPU also support multiple supply voltage levels.

20. 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
Power
deadline
Power
deadline
One of popular technique to reduce energy consumption is DVS technique.
For example, we have a task with the deadline 25 msec.
If it is executed on 5-V supply voltage, it may finish at time 10 msec.
If it is executed on 2-V supply voltage, it may finish at time 25 msec.
Both two cases meet the deadline, but the energy consumption can be reduced if we take the second case.
5.02
2.02
10 msec
25 msec
10 msec
25 msec
(a) Supply voltage = 5.0 V
(b) Supply voltage = 2.0 V

21. 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

22. Taxonomy of Power Management Techniques

23. Data Center Level
Taxonomy

24. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

25. 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.com’s 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

26. 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.

27. Green Cloud Computing
Revenue
Power Consumption

28. Internet Service Provider Air Conditioning, and Chiller
Cloud Usage Model
Internet Service Provider
Routers
Internet
Cloud Datacenter A
End User
Cloud Datacenter B
Cloud Datacenter C
Datacenter
LAN and Gateway router
(Network Devices)
VM and Storage
(Server)
Air Conditioning, and Chiller
(Cooling Devices)
UPS, PDU, lighting
(Electrical Devices)
Cloud Computing

29. Green Cloud Computing Architecture

30. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

31. Case Study 2: Dynamic VM Consolidation
To evaluate the framework, a case study for IaaS clouds is done.

32. Three Sub-Problems When to migrate VMs? Which VMs to migrate?
Host overload detection algorithms
Host underload detection algorithms
Which VMs to migrate?
VM selection algorithms
Where to migrate VMs?
VM placement algorithms

33. 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)

34. 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:

35. 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

36. 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)

37. 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.

38. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

39. 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
But it has introduced another concern among governments and environmentalists. Will it further increase the carbon footprint of IT industry? The following data gives an opposite picture. Cloud computing can reduce the carbon footprint. But data here has shown how brown clouds are. It seems that most of the cloud datacenters are not really worried about carbon.

40. 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
Each place has varied carbon footprint, CPU configuration, energy efficiency,
Source: Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers by Lawrence Berkeley National Laboratory’s report

41. Green Cloud Architecture
This is the proposed overall architecture. User send requests of Cloud services. Green Broker decides the resources in such a way that it result in minimum carbon emissions. This framework includes two new entities which gives incentives to Cloud providers by providing ways to attract customers.

42. Third Party: Green Offer and 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
These are the features of the two new entities.

43. User: Green Broker A typical Cloud broker Green 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

44. Provider: Green Middleware
Each service of Cloud provider needs to be equipped so that minimum energy is consumed. For example, SaaS layer should have the most efficient applications with power capping solutions. Simlarly, PaaS layer should give customers some Green offers so that energy consumption can be reduced.

45. 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
To evaluate the framework, a case study for IaaS clouds is done.

46. 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.
We designed a CEGP policy for Green Broker which take into account the carbon efficiency of the schedule.
Carbon Emission
Datacenter Efficiency
Energy Efficiency of VM

47. 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:
Due to unavailability of real Cloud workloads, HPC workload is taken from Lawrence Livermore National Laboratory (LLNL) for evaluation. The other factors are taken from various studies.
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.

48. EDF: Carbon-Efficient (CEGP) VS EST (Early Start-time) Algorithm (EST)
We compared with a performance based algorithm EDF-EST which schedules applications on the datacenters where they can start earliest. Our proposed architecture using CEGP (EDF-CEGP) can reduce up to 23% of the energy consumption (Figure a) and 25% of the carbon emission (Figure b) compared to an existing approach using EST (EDF-EST) across all datacenters. The amount of workload executed is also pretty similar.

49. 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%.

50. Outline Cloud Computing at a Glance Powering Cloud Infrastructure
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

51. 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)

52. Wiley Press, New York, USA, Feb 2011
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, pp, 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): , Elsevier Press, Amsterdam, The Netherlands, June 2011.
Wiley Press, New York, USA, Feb 2011

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54. Green Cloud Computing

55. Simulation Results: ESV

56. Simulation Results: Energy

57. Simulation Results: SLAV

58. Simulation Results: the Number of VM Migrations