FairCloud: Sharing the Network
in Cloud Computing
Computer Communication Review(2012)
Arthur : Lucian Popa
Arvind Krishnamurthy
Sylvia Ratnasamy
Ion Stoica
Presenter : 段雲鵬
Outline
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Introduction
challenges sharing networks
Properties for network sharing
Mechanism
Conclusion
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Some concepts
• Bisection bandwidth
– Each node has a unit weight
– Each link has a unit weight
• Flow def
– standard five-tuple in packet headers
• B denotes bandwidth
• T denotes traffic
• W denotes the weight of a VM
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Background
• Resource in cloud computing
– Network , CPU , memory
• Network allocation
– More difficult
• Source, destination and cross traffic
– Tradeoff
• payment proportionality VS bandwidth guarantees
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Introduction
• Network allocation
– Unkown to users , bad predictability
• Fairness issues
– Flows, source-destination pairs, or sources alone ,
destination alone
• Difference with other resource
– Interdependent Users
– Interdependent Resources
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Assumption
• From a per-VM viewpoint
• Be agnostic to VM placement and routing
algorithms
• In a single datacenter
• Be largely orthogonal to work on network
topologies to improve bisection bandwidth
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Traditional Mechanism
• Per flow fairness
– Unfair when simply instantiating more flow
• Per source-destination pair
– Unfair when one VM communicates with more
VMs
• Per source
– Unfair to destinations
• Asymmetric
– Only be fair for source or destination only
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Examples
• Per source-destination pair
If there is little traffic on the A-F and B-E ,
B(A)=B(B) =B(E) =B(F) =2*B(C) =2*B(D)
=B(G) =B(H)
Per source
B(E) =B(F) =0.25*B(D) , In the opposite
direction, B(A) =B(B) =0.25*B(C)
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Properties for network sharing(1)
• Strategy proofness
– Can’t increase bandwidth by modifying behavior
at application level
• Pareto Efficiency A
– X and Y is bottlenecked , when B(X-Y) increases,
B(A-B) must decrease ,otherwise congestion will
be worse
B
10 M
1M
10 M
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Properties for network sharing(2)
• Non-zero Flow Allocation
– A strictly +B() between each pairs are expected
• Independence
L1
– When T2 increase , B1 should not be
affected
• Symmetry
– If all flows’ direction are swiched,
the allocation should be the same
L2
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Network weight and user’s payment.
• Weight Fidelity(provide incentive)
– Strict Monotonicity (Monotonicity)
• If W(VM) increases ,then all its traffic must increase
(not decrease) .
– Proportionality
• Guaranteed Bandwidth
Subset
P(2/3)
– Admission control
• They are conflicting, tradeoff
Subset
Q(1/3)
No communication between P and Q
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Per Endpoint Sharing (PES)
• Can explicitly trade between weight fidelity
and guaranteed bandwidth
– NA denote the number of VMs A is communicating
with
– WS-D=f(WS,WD) , WA-B=WB-A
– Normalized by L1 normalization
• Drawback : Static Method (out of discussion)
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Example
• WA-D=WA/NA+WD/ND
=1/2+1/2=1
• WA-C=WB-D=1/2+1/1=1.5
• Total Weight=4(4 VMs)
• So WA-D=1/4=0.25 WA-C=WB-D=1.5/4=0.325
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Comparison
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PES
• For one host , B ∝ (closer VMs) instead of
(remote VMs)
• Higher guarantees for the worst case
• WA−B = WB−A =α*WA/NA+ β*WB/ NB
– α and β can be designed to weight between
bandwidth guarantees and weight fidelity
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One Sided PES (OSPES)
• Designed for tree-based topology
• WA−B = WB−A =α*WA/NA+ β*WB/ NB
• When closer to A, α = 1 and β = 0
• When closer to B, α = 0 and β = 1
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OSPES
• fair sharing for the traffic towards or from
the tree root
– Resource allocation are depended on the root
When
– Non-strict monotonicity
W(A) = W(B) , If the
access
link is 1 Gbs, then
each VM is
guaranteed 500
Mbps
WA-VM1=1/1 WB-VMi=1/10(i=2,3……,11)
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Max-Min Fairness
• The minimum data rate that a dataflow
achieves is maximized
– The bottleneck is fully utilized
• Can be applied
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Conclusion
• Problem : sharing the network within a cloud
computing datacenter
• Tradeoff between payment proportionality
and bandwidth guarantees
• A mechanism to make tradeoff between
conflicting requirements
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Reference
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Thanks !!
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