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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 ([email protected]) 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.
Dzmitry Kliazovich ([email protected]) 4
Resource Requirements of Cloud Applications
Aug 22, 2013
Computing Network Bandwidth
Communication delays
(tolerance)
Degree of interactivity Storage
Dzmitry Kliazovich ([email protected]) 5
Resource Requirements of Cloud Applications
Aug 22, 2013
Computing Network Bandwidth
Communication delays
(tolerance)StorageDegree of
interactivity
Dzmitry Kliazovich ([email protected]) 6
Cloud Computing Applications
Aug 22, 2013
Communicationresources
Dzmitry Kliazovich ([email protected]) 7
Cloud Computing Applications
• 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
Dzmitry Kliazovich ([email protected]) 8
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
S
Links
10 GE 1 GE
Nodes
L3 Switch L2/L3 Rack Switch Computing Server
Network congestion!!!
Dzmitry Kliazovich ([email protected]) 9
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
S
Links
10 GE 1 GE
Nodes
L3 Switch L2/L3 Rack Switch Computing Server
Network is balanced !!!
eSTAB Scheduling
Dzmitry Kliazovich ([email protected]) 11
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
Dzmitry Kliazovich ([email protected]) 12
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
Dzmitry Kliazovich ([email protected]) 14
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.
Dzmitry Kliazovich ([email protected]) 15
eSTAB Model
Aug 22, 2013
• Available bandwidth for bursty transmissions
Dzmitry Kliazovich ([email protected]) 16
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
Dzmitry Kliazovich ([email protected]) 17
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
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Queue occupacy q(t)/Qmax
Q(t
)
Favor Empty Queues
Penalize Highly-Loaded
Queues
1−1𝑇 ∫
𝑡
𝑡+𝑇 (𝑒−(𝜌 ∙(𝑞 (𝑡 )− 1)
𝑄𝑚𝑎𝑥
)𝜑
)𝑑𝑡
Dzmitry Kliazovich ([email protected]) 18
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
Dzmitry Kliazovich ([email protected]) 19
eSTAB Model
Aug 22, 2013
• eSTAB traffic related metric
00.2
0.4
0.6
0.8
10
0.2
0.4
0.6
0.8
1
0
0.5
1
Queue occupacy, qLink load,
Fm a
nd F
r
Step #2: Selecting a Server
Dzmitry Kliazovich ([email protected]) 21
eSTAB Model
Aug 22, 2013
• Energy consumption of servers
CPU130W (43%)
Memory36W (12%)
Disks12W (4%)
Peripherial50W (17%)
Motherboard25W (8%)
Other48W (16%)
Computing Servers301 W
Dzmitry Kliazovich ([email protected]) 22
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.
Dzmitry Kliazovich ([email protected]) 23
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
Dzmitry Kliazovich ([email protected]) 24
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
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Server load
Fs k(t
)
Penalize Selection of Idle Servers
Select Servers According to their
Energy Consumption
Performance Evaluation
Dzmitry Kliazovich ([email protected]) 26
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
Dzmitry Kliazovich ([email protected]) 28
e-STAB Results
Aug 22, 2013
• Processing Load Distribution Among ServersRacks are
overloadedRacks load is
balanced
Dzmitry Kliazovich ([email protected]) 29
2 4 6 8 10 12 14 16 18 200
0.2
0.4
0.6
0.8
1
Number of rack
Rac
k lo
ad
Greene-STAB
e-STAB Results
Aug 22, 2013
• Traffic Distribution Among Racks
Racks are overloaded
Racks load is balanced
Dzmitry Kliazovich ([email protected]) 30
2 4 6 8 10 12 14 16 18 200
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Simulation time (s)
Tas
k co
mpl
etio
n de
lay
(s)
Greene-STAB
e-STAB Results
Aug 22, 2013
• Task Completion Delay
80 ms (Green)
20 ms (e-STAB)
Dzmitry Kliazovich ([email protected]) 31
e-STAB Results
Aug 22, 2013
• Energy Consumption
Improved Performance Comes at a Price of Increased Energy Consumption of Network Switches
Dzmitry Kliazovich ([email protected]) 32
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
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