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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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.