e-STAB: Energy-Efficient Scheduling for Cloud
Computing Applications with Traffic Load
Balancing
Dzmitry Kliazovich
Pascal Bouvry
University of Luxembourg, Luxembourg
Sisay T. Arzo
Fabrizio Granelli
University of Trento
Samee U. Khan
North Dakota State University, U.S.A.
Cloud Computing
• Cloud computing market: $241 billion in 2020
• Main focus is on Software-as-a-Service (SaaS)
Source: Larry Dignan, “Cloud computing market”, ZDNet, 2011.
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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Cloud Computing Applications
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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Resource Requirements of Cloud
Applications
Computing
Aug 22, 2013
Network
Bandwidth
Communication
delays
(tolerance)
Degree of
interactivity
Dzmitry Kliazovich ([email protected])
Storage
4
Resource Requirements of Cloud
Applications
Computing
Aug 22, 2013
Network
Bandwidth
Communication
delays
(tolerance)
Degree of
interactivity
Dzmitry Kliazovich ([email protected])
Storage
5
Cloud Computing Applications
Communication
resources
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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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])
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Scheduling in Data Centers
Network congestion!!!
Aug 22, 2013
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Scheduling in Data Centers
Network is balanced !!!
Aug 22, 2013
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eSTAB Scheduling
eSTAB Scheduling in Data Centers
e-STAB: Energy-Efficient Scheduling for Cloud
Computing Applications with Traffic Load Balancing
Aug 22, 2013
#1
• Treat communication and computing
demands equally
#2
• Optimize energy efficiency and load
balancing of network traffic
#3
• Formal model for selection of servers,
racks, and network modules
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eSTAB Scheduling in Data Centers
• 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)
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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Step #1: Selecting a Rack
eSTAB Model
• Find a module 𝑚𝑖 𝜖𝑀 such that
𝐴𝑚(mi ) = max 𝐴𝑚 𝑚 ,
∀m∈M
– where 𝐴𝑚 is the available bandwidth of a module 𝑚𝑖 computed on a per-server basis
• For a module 𝑚𝑖 ∈ 𝑀 the available bandwidth can be computed as
𝐶𝑚 𝑖 − 𝑚𝑖
𝐴𝑚𝑖 =
𝑆𝑚𝑖
– 𝐶𝑚𝑖 is the transmission capacity of a module 𝑖
– 𝑚𝑖 is a currently effective transmission rate of the traffic
– 𝑆𝑚𝑖 is a number of servers hosted in the module.
Aug 22, 2013
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eSTAB Model
• Available bandwidth for bursty transmissions
1
𝐴𝑚𝑖 𝑡 =
𝑇
𝑡+𝑇
𝑡
=
Aug 22, 2013
𝐶𝑚 𝑖 − 𝑚𝑖 𝑡
𝑆𝑚𝑖
1
(𝐶𝑚 𝑖
𝑆𝑚𝑖
−
𝑑𝑡 =
1 𝑡+𝑇
𝑚𝑖
𝑇 𝑡
Dzmitry Kliazovich ([email protected])
𝑡 𝑑𝑡)
15
eSTAB Model
• Available bandwidth weighted with the size of
the bottleneck queue
1
𝑄 𝑡 =1−
𝑇
𝑡+𝑇
𝜌∙(𝑞 𝑡 −1) 𝜑
−( 𝑄
)
𝑚𝑎𝑥
𝑒
𝑑𝑡
𝑡
– 𝑞(𝑡) is an instantaneous occupancy of the queue
measured either in bytes or packets at the time 𝑡
– 𝑄𝑚𝑎𝑥 is the maximum allowed size of the queue
– 𝜌 and 𝜑 control the shape of the distribution
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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eSTAB Model
• Available bandwidth weighted with the size of
the bottleneck queue
1
1
1−
𝑇
0.9
0.8
Favor Empty
Queues
𝑡+𝑇
𝑒
−(
𝜌∙(𝑞 𝑡 −1) 𝜑
)
𝑄𝑚𝑎𝑥
𝑑𝑡
𝑡
0.7
Q(t)
0.6
0.5
Penalize
Highly-Loaded
Queues
0.4
0.3
0.2
0.1
0
0
Aug 22, 2013
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Queue occupacy q(t)/Q max
Dzmitry Kliazovich ([email protected])
0.8
0.9
1
17
eSTAB Model
• Parameter 𝜌 controls the position of the
falling edge of 𝑄(𝑡) with the respect to the
level of queue occupancy
• Parameter 𝜑 controls the shape of the falling
slope
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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eSTAB Model
• eSTAB traffic related metric
Fm and Fr
1
0.5
0
1
0
0.2
0.8
0.4
0.6
0.6
0.4
0.8
0.2
0
Link load, 
Aug 22, 2013
1
Queue occupacy, q
Dzmitry Kliazovich ([email protected])
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Step #2: Selecting a Server
eSTAB Model
• Energy consumption of servers
𝑃 𝑙 = 𝑃𝑓𝑖𝑥𝑒𝑑 + 𝑃𝑓 𝑓 3
Other
48W (16%)
Computing Servers
301 W
Motherboard
25W (8%)
CPU
130W (43%)
Peripherial
50W (17%)
Disks
12W (4%)
Aug 22, 2013
Memory
36W (12%)
Dzmitry Kliazovich ([email protected])
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eSTAB Model
• In DVFS is used, power consumption can be
reduced proportionally to
𝑉2 ∙ 𝑓
– 𝑉 is a voltage
– 𝑓 is a frequency of the chip
• Voltage reduction requires a frequency
downshift, which implies a cubic relationship
from 𝑓 in the CPU power consumption.
Aug 22, 2013
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eSTAB Model
• eSTAB metric for server selection
1
𝐹𝑠𝑘 𝑡 =
𝑇
𝑡+𝑇
1
(
𝑡
1
1 + 𝑙𝑘
10
𝜀
− 𝜀 𝑙𝑘 𝑡 −2
+𝑒
1
𝑃𝑖𝑑𝑙𝑒
−
1−
2
𝑃𝑝𝑒𝑎𝑘
𝑙𝑘 (𝑡) 3
− 𝜏
3
(𝑡) − 𝑒
𝑑𝑡,
– 𝑙𝑘 𝑡 is an instantaneous load of server 𝑘 at time 𝑡
– 𝑇 is an averaging interval
– 𝜀 corresponds to the CPU load of an idle server required to keep an
operating system and virtual machines running
Aug 22, 2013
Dzmitry Kliazovich ([email protected])
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eSTAB Model
• eSTAB metric for server selection
Select Servers
According to their
Energy
Consumption
1
0.9
Penalize
Selection of
Idle Servers
0.8
0.7
Fsk(t)
0.6
0.5
0.4
0.3
0.2
0.1
0
0
Aug 22, 2013
0.1
0.2
0.3
0.4
0.5
0.6
Server load
0.7
Dzmitry Kliazovich ([email protected])
0.8
0.9
1
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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
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Simulation Setup
• Setup Parameters
Aug 22, 2013
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e-STAB Results
• Processing Load Distribution Among Servers
Racks load is
balanced
Racks are
overloaded
Aug 22, 2013
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e-STAB Results
• Traffic Distribution Among Racks
Green
e-STAB
1
0.8
Rack load
Racks are
overloaded
Racks load is
balanced
0.6
0.4
0.2
0
Aug 22, 2013
2
4
6
8
10
12
Number of rack
14
16
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20
29
e-STAB Results
• Task Completion Delay
0.14
Green
e-STAB
80 ms (Green)
Task completion delay (s)
0.12
0.1
0.08
0.06
20 ms (e-STAB)
0.04
0.02
0
Aug 22, 2013
2
4
6
8
10
12
14
Simulation time (s)
16
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20
30
e-STAB Results
• Energy Consumption
Improved Performance Comes at a Price of Increased
Energy Consumption of Network Switches
Aug 22, 2013
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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
Dzmitry Kliazovich ([email protected])
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Thank you!
Contact information:
Dzmitry Kliazovich
University of Luxembourg
[email protected]