E-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing...

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e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load

Balancing

Dzmitry Kliazovich University of Luxembourg, LuxembourgPascal Bouvry

Sisay T. Arzo University of TrentoFabrizio Granelli

Samee U. Khan North Dakota State University, U.S.A.

Dzmitry Kliazovich (dzmitry.kliazovich@uni.lu) 2

Cloud Computing

• Cloud computing market: $241 billion in 2020• Main focus is on Software-as-a-Service (SaaS)

Aug 22, 2013

Source: Larry Dignan, “Cloud computing market”, ZDNet, 2011.

Cloud Computing Applications

Aug 22, 2013

Resource Requirements of Cloud Applications

Aug 22, 2013

Computing Network Bandwidth

Communication delays

(tolerance)

Degree of interactivity Storage

Resource Requirements of Cloud Applications

Aug 22, 2013

Computing Network Bandwidth

Communication delays

(tolerance)StorageDegree of

interactivity

Aug 22, 2013

Communicationresources

• Traditional resource allocation and scheduling– Distribute incoming jobs to the pool of servers– Communication requirements and networking are not

taken into account

Aug 22, 2013

Scheduling in Data Centers

Aug 22, 2013

S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S

CoreNetwork

AggregationNetwork

AccessNetwork

10 GE 1 GE

L3 Switch L2/L3 Rack Switch Computing Server

Network congestion!!!

Scheduling in Data Centers

Aug 22, 2013

S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S

CoreNetwork

AggregationNetwork

AccessNetwork

10 GE 1 GE

L3 Switch L2/L3 Rack Switch Computing Server

Network is balanced !!!

eSTAB Scheduling

eSTAB Scheduling in Data Centers

Aug 22, 2013

e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing

• Treat communication and computing demands equally#1

• Optimize energy efficiency and load balancing of network traffic#2

• Formal model for selection of servers, racks, and network modules#3

eSTAB Scheduling in Data Centers

Aug 22, 2013

• Step 1– Select servers connected to the data center

network with the highest available bandwidth (low network load)

• Step 2– Within the selected group of servers, select a

computing server with the smallest available computing capacity (high server load)

Step #1: Selecting a Rack

eSTAB Model

Aug 22, 2013

• Find a module such that

– where is the available bandwidth of a module computed on a per-server basis

• For a module the available bandwidth can be computed as

– is the transmission capacity of a module – is a currently effective transmission rate of the traffic– is a number of servers hosted in the module.

eSTAB Model

Aug 22, 2013

• Available bandwidth for bursty transmissions

eSTAB Model

Aug 22, 2013

• Available bandwidth weighted with the size of the bottleneck queue

– is an instantaneous occupancy of the queue measured either in bytes or packets at the time

– is the maximum allowed size of the queue– and control the shape of the distribution

eSTAB Model

Aug 22, 2013

• Available bandwidth weighted with the size of the bottleneck queue

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

Queue occupacy q(t)/Qmax

Favor Empty Queues

Penalize Highly-Loaded

Queues

1−1𝑇 ∫

𝑡+𝑇 (𝑒−(𝜌 ∙(𝑞 (𝑡 )− 1)

𝑄𝑚𝑎𝑥

)𝑑𝑡

eSTAB Model

Aug 22, 2013

• Parameter controls the position of the falling edge of with the respect to the level of queue occupancy

• Parameter controls the shape of the falling slope

eSTAB Model

Aug 22, 2013

• eSTAB traffic related metric

Queue occupacy, qLink load,

Step #2: Selecting a Server

eSTAB Model

Aug 22, 2013

• Energy consumption of servers

CPU130W (43%)

Memory36W (12%)

Disks12W (4%)

Peripherial50W (17%)

Motherboard25W (8%)

Other48W (16%)

Computing Servers301 W

eSTAB Model

Aug 22, 2013

• In DVFS is used, power consumption can be reduced proportionally to

– is a voltage– is a frequency of the chip

• Voltage reduction requires a frequency downshift, which implies a cubic relationship from in the CPU power consumption.

eSTAB Model

Aug 22, 2013

• eSTAB metric for server selection

– is an instantaneous load of server at time – is an averaging interval– corresponds to the CPU load of an idle server required to keep an

operating system and virtual machines running

eSTAB Model

Aug 22, 2013

• eSTAB metric for server selection

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

Server load

Fs k(t

Penalize Selection of Idle Servers

Select Servers According to their

Energy Consumption

Performance Evaluation

Cloud Computing Simulator

Aug 22, 2013

– Measures cloud performance and energy efficiency– First to simulate cloud communications with packet-level precision– Implements network-aware scheduling– Implements complete TCP/IP protocol stack

available at

http://greencloud.gforge.uni.lu

Simulation Setup

• Setup Parameters

Aug 22, 2013

e-STAB Results

Aug 22, 2013

• Processing Load Distribution Among ServersRacks are

overloadedRacks load is

balanced

2 4 6 8 10 12 14 16 18 200

Number of rack

Greene-STAB

e-STAB Results

Aug 22, 2013

• Traffic Distribution Among Racks

Racks are overloaded

Racks load is balanced

2 4 6 8 10 12 14 16 18 200

Simulation time (s)

Greene-STAB

e-STAB Results

Aug 22, 2013

• Task Completion Delay

80 ms (Green)

20 ms (e-STAB)

e-STAB Results

Aug 22, 2013

• Energy Consumption

Improved Performance Comes at a Price of Increased Energy Consumption of Network Switches

Conclusions

• Considering communication fabric is essential to allocate resource efficiently in cloud computing

• e-STAB is a new communication-aware scheduler for cloud application

• e-STAB minimizes communication-related delays and can avoid congestion-related packet losses at a price of minor increase in energy consumption of network switches

Aug 22, 2013

Thank you!

Contact information:

Dzmitry KliazovichUniversity of Luxembourgdzmitry.kliazovich@uni.lu

E-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing...

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