Green Cloud Framework for Improving Carbon Efficiency of Clouds Saurabh Kumar Garg, Chee Shin Yeo, Rajkumar Buyya
Contents
Motivation
Objective
Related Work
Carbon Aware Green Cloud Architecture
Case Study: IaaS Cloud
Evaluation
Conclusion
Motivation
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
Where Does the Power Go? Power Consumption in the Datacenter
Server/Storage
50%
Computer Rm. AC 34% Conversion
7%
Network
7%
Lighting
2%
Compute resources and particularly servers are at the heart of a complex, evolving system! Source: APC
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
Green Cloud or Brown Cloud?
Ideally, for every server virtualized, save
Plus
~$700 and ~7,000 kWh / year 4 tons of CO2 emissions / year Power down underutilized physical servers, saving 40% Desktop management, saving 35% / year
But currently
Cloud datacenters
Location
Estimated power usage Effectiveness 1.21
Google
Lenoir
Apple
Apple, NC
Microsoft
Chicago, IL
1.22
Yahoo
La Vista, NE
1.16
% of Dirty Energy Generation 50.5% Coal, 38.7% Nuclear 50.5% Coal, 38.7% Nuclear 72.8% Coal, 22.3% Nuclear 73.1% Coal, 14.6% Nuclear
% of Renewable Electricity 3.8% 3.8% 1.1% 7%
Objective
To Minimize Carbon Footprint of Cloud Computing
Related Work
Energy saving within a resource site
Consolidation of Virtual Machines (VMs), VM migration, scheduling, demand projection, heat management, temperature aware allocation, and load balancing Most of these are on a single site and not considering global sites
Others
Green Open Cloud (GOC) Aggregation of workload by negotiating with users Do not consider carbon emissions
Carbon Aware Green Cloud Architecture End User
d) Allocate service
Private Cloud
a) Request a cloud service
Green Broker
Routers
c) Request energy efficiency information
Internet
b) Request any green offer
Carbon Emission Directory
e) Request service allocation
Public Cloud A
Public Cloud B
Green Offer Directory
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
Require more carbon transparency from providers
Advertising tool to increase the market share, e.g. Google 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
User: Green Broker
A typical Cloud broker
User
Lease Cloud services Schedule applications
Green Broker Cloud Request Services QoS
Application Profiling
Cloud Offers
CO2 Analysis Services Cost Calculator
CO2 Emission Calculator
Green Information System
Brokering Services such as scheduling, monitoring Green Policies
Cloud Leasing
Green Broker
Scheduler
Private Cloud
Public Cloud
1st layer: Analyze user requirements 2nd layer: Calculates cost and carbon footprint of services 3rd layer: Carbon aware scheduling
Provider: Green Middleware
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
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:
Carbon Emission
Datacenter Efficiency
Energy Efficiency of VM
Schedule greedily the most urgent deadline jobs on the most power efficient resource site.
Simulation Setup
Parallel Workload: first week of LLNL Thunder trace from Parallel Workload Archive (PWA)
1D.
Deadline generated based methodology proposed by Irwin et al. (2004)1
Configuration of Cloud resource sites2:
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.
CEGP VS Performance-based Algorithm (EST)
Conclusion
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 improving the carbon footprint by about 25%
