Green ComputingConclusions

Tarek AbdelzaherDept. of Computer Science

University of Illinois at Urbana Champaign

What We Tried to Achieve

A combination of depth and breadth: In-depth look at data center energy

consumption Broad look at related energy problems

A combination of instructor-driven and student-driven approach Depth coverage driven by instructor Breadth coverage driven by students

What You Should Expect Now

Should be well-familiar with the state of the art in energy management of large computing systems Should be able to write a good paper on the topic Should be able to tell a novel problem from a

solved problem Should be aware of general trends in energy

research in other areas (transportation, smart buildings, green Internet, smart grid)

Examples of Publishable Future Work

Project 1: Energy-efficient Map/Reduce Novelty claim:

First paper to consider DVFS as a means to meet temperature constraints in the context of map-reduce energy optimization.

Target community: systems, energy, autonomic computing Other approaches to energy savings relied on consolidation

and turning machines off (or running machines at full speed then turning them off).

Raising temperature such that full speed execution violates temperature constraints (fixed by DVFS) saves on cooling

Examples of Publishable Future Work

Project 2: Green HDFS Novelty claim:

First paper to consider adaptive partitioning of server farms into hot and cold servers (those hosting hot and cold data sets) to save energy.

Target community: systems, energy, autonomic computing Other approaches that partition map-reduce application data

into hot and cold used static data-to-server assignment (hence static partitioning into hot and cold servers).

Dynamic partitioning servers can be more effective in dealing with load fluctuations.

Examples of Publishable Future Work

Project 3: Energy-proportional Key-value Store Novelty claim:

First paper to implement an energy-proportional peer-to-peer storage/DHT system

Target community: P2P, energy, middleware Previous P2P solutions for distributed storage did not

address energy-proportionality Energy-proportionality reduces the total consumption

of P2P systems when not operating at peak load

Examples of Publishable Future Work

Project 4: Green Memory Novelty claim:

First paper to consider the pros and cons of unreliable (but more energy efficient) memory

Target community: systems, energy, architecture With the right “killer app” (e.g., caching), if proven to be

more resource-efficient, green memory can revolutionize the industry.

The premise is that energy spent to provide reliability on top of green memory is outweighed by its energy savings

Examples of Publishable Future Work

Project 5: Combining Multi-DVS with DPM Novelty claim:

First paper to combine dynamic power management (DPM) with MultiDVS capabilities in real-time embedded systems

Target community: real-time systems There is no prior work on platforms with MultiDVS capabilities,

with the exception of one paper. That single paper did not consider the possibility to turn machines off

In workloads with high imbalance in resource demand, opportunities for turning machines off are limited by the bottleneck resource, leaving other resources (e.g., memory) underutilized and subject to additional savings with DVS.

Examples of Publishable Future Work

Project 6: DVS for Synchronized WSNs Novelty claim:

First paper to consider the adverse effects of DVS on clock synchronization in sensor networks

Target community: sensor networks No prior work uncovered (much less explored) the

effect that DVS has on clock de-synchronization To keep clocks synchronized while bounding clock

sync overhead, one must appropriately bound DVS transitions.

Examples of Publishable Future Work

Project 7: Low-power Design for Manycore Synchronization Primitives

Novelty claim: Paper reduces energy overhead of synchronization

primitives in high-performance computing Target community: high-performance computing (?),

architecture (?) If one can show that synchronization energy in manycore

machines is a significant fraction of the total, and that the cost of the new solution does not outweigh the energy saving benefits, the contribution makes sense.

Research Directions:Measurement

Estimating energy consumption using software measurements, instead of monitoring hardware Much prior research, but current models

remain inaccurate Accurate models of single-machine

energy consumption

Research Directions:Energy Proportionality

Building energy-proportional systems Routers Stateful file systems Easy to do for stateless, highly replicated

systems, but not so easy for stateful systems with unique, heterogeneous resources

Research Directions:Energy Optimization and Control

Mature area for single servers Also mature area on the computing

side (reducing energy of computation) Not much done on joint energy

optimization of computing and cooling (with thermal constraints), with the exception of a few papers covered in this class

Research Directions:Instability Challenges

Instability in computing systems is not a well-researched area

Opportunities lie in identifying interesting new instances of instability (self-reinforcing “vicious cycles”) in practical systems, and building clever automated solutions to avoid the problem

Research Directions:Green Programming APIs

An emerging direction is to embody energy-saving mechanisms into language constructs and abstractions used by distributed systems Green map-reduce Green HDFS Green cache Green distributed file systems

Research Directions:Green Internet

Very few papers investigate high-performance router architecture for green computing Current core Internet routers themselves

look like “server farms” Current routers are not energy-proportional

Research Directions:Green Transportation

Understanding the “energy slack” in transportation systems Savings by better navigation Savings by better driving habits Savings by altering the time of departure

(within acceptable limits) Driver notification systems that exploit the

above understanding for reducing energy

Research Directions:Green Buildings

Understanding the “energy slack” in residential systems Smart subsystems: smart thermostats, smart

lighting, … Smart appliances (supply-following load):

refrigerators, washing machines, driers Smart energy storage (hybrid vehicles,

cooling, etc)

Research Directions:Smart Grid

Two main challenges: Move from a fixed distribution architecture to

a market will millions of consumers who may also serve as suppliers (e.g., households with solar panels)

Accommodate renewable energy sources (and hence unpredictability in supply)

Major Industrial Research and Development Initiatives

Job opportunities Internship opportunities Who to contact?

Just look at the industry authors on the reading list for a quick picture

(feel free to contact me for other pointers or for letters of recommendation)

Remaining Reminders

Final: Out today Due (by email) by Sunday night, Dec 12th 30 simple multiple-choice questions that

test high-level knowledge of reading list

Projects

Reports are due by 11:59pm on Friday, Dec 17th. Report should not exceed 10 two-column pages,

in 10pt font. Report must present motivation, relevant

background, novelty claims, related work (explaining the value added by your project), design, and evaluation.

“Short and to the point” is preferred to “long and content-free”.

Next Steps and Deadlines

I strongly recommend publishing a paper from your project

Upcoming deadlines: Energy and data centers: Jan 8th ICAC; March

18th IGCC 2011 Sensor networks: Feb 4th DCoSS 2011; April

8th Sensys 2011 Real-time: Jan 23rd ECRTS 2011

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