Network Support for Cloud Services Lixin Gao, UMass Amherst.
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Transcript of Network Support for Cloud Services Lixin Gao, UMass Amherst.
Network Support for Cloud Services
Lixin Gao, UMass Amherst
Outline
• Data center networking– Design issues– Resource sharing
• Asynchronous computation model
Conventional Data Center Networks
• Hierarchical tree structure• High speed core switches are
expensive• Hard to scale
Data Center Network Design
• Commodity Hardware– Server– Switch
• Scalable
• Fat tree, Dcell, Bcube, VL2, ….
Dpillar Structure
• Devices– All servers have dual-
port– All switches have n-port
• Server and switch columns– k columns
• Server naming– (col, label), label
• Connecting rule– Servers in and ,
their labels differ at only
011... k
iH 1iH
]1log,0[ 2 ni
i
Design Issues
• Inexpensive• Scale to a large number of servers• Fault Tolerant Routing• Load Balancing
Network Resource Sharing within Data Center
• Virtualization of CPU (Xen), memory (DiffEng), storage (SAN)
• Network resource can become bottleneck– Sorting and shuffling of MapReduce– Sync among tasks slows down computation– Backup of VMs
• Bandwidth sharing– Granularity: point-to-point or group based– Fair share: centralized vs. distributed– Privacy: public cloud vs. private cloud
MapReduce Model• Map: generate key value pairs
• Reduce: aggregate values for a key from multiple sources
• Shuffle and sort
Iterative Computations
PageRank
Clustering
BFS
Youtube video suggestion
Pattern Recognition
Synchronous Model
• Ease of MapReduce implementation• However,– Overhead of sync operation, sorting– Slow convergence, waste of CPU,
network resources–Many iterative computations can be
performed asynchronously• PageRank, shorest path, adsorption, link
proximity estimation, belief propagation….
Shortest Paths
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Shortest Paths
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Parallel execution
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Shortest Paths
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Parallel execution
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An Asynchronous Model
• A general framework– Eliminate synchronization– Scheduling policy
• Prove correctness for a wide range of applications– PageRank, Personalized PageRank– Link Proximity Estimation
• Commute time, Katz metric, shortest path
– Bayesian Inference• Scheduling policies– Top-k query
Shortest Path
Facebook dataset
SSSP-m dataset
PageRank
Google webgraph
PageRank-m webgraph
Conclusions
• Network design within data center– Design based on commodity hardware– Network resources sharing
• Asynchronous computation framework– Reduced bandwidth requirement – Efficient computation
An Example of Outage• planet02.csc.ncsu.edu experiences packet loss on July 30, 2005
Causes of Outages• Most lost packets are caused by routing
outages
Failure Type Lost packets
fraction
unknown 14572 0.2
Routing dynamics
58111 0.8
Towards 5 Nines Reliability
• Exploiting redundancy on Internet Path–Multiple routing instances to ensure
consistency
• Exploiting multiple sites within a cloud– Site selection through route monitoring– Deliver through private WAN
Packet Loss due to Routing Failures
• Failover events: 76% packets lost• Recovery events: 26% packets lost
Failover Recovery
Round-trip Delay• Failover events have significant impact
on packet round-trip delays. In the worst case, packet round-trip delays can be more than 900msec.
Failover Recovery
Reordering during Failover Events
• The number of reordered packets is small. However, the offset of reordered packets is large.
• Larger buffer sizes for real-time applications.