Presentation on theme: "Ningning HuCarnegie Mellon University1 Optimizing Network Performance In Replicated Hosting Peter Steenkiste (CMU) with Ningning Hu (CMU), Oliver Spatscheck."— Presentation transcript:
Ningning HuCarnegie Mellon University1 Optimizing Network Performance In Replicated Hosting Peter Steenkiste (CMU) with Ningning Hu (CMU), Oliver Spatscheck (AT&T), Jia Wang (AT&T)
Ningning HuCarnegie Mellon University2 Motivation The question of how to use latency to select a replicated web server has been well studied How about using available bandwidth? ?
Ningning HuCarnegie Mellon University3 Outline Pathneck Internet end user RTT distribution and access bandwidth distribution Optimization results For RTT For bandwidth For data transmission time
Ningning HuCarnegie Mellon University4 Pathneck: Recursive Packet Train (RPT) Two measurement packets are dropped at each router ICMP packets allow source to estimate train length at each hop Changes in train length provide bounds on the available bandwidth of each link Load packets measurement packets measurement packets 1220 21 20 pkts, 60 B 100 60 pkts, 500 B TTL
Ningning HuCarnegie Mellon University6 Pathneck Properties Pathneck is an active probing tool designed for locating Internet bottlenecks It is efficient and effective Also provide route, delay, and bandwidth information For technical detail please see www.cs.cmu.edu/~hnn/pathneck www.cs.cmu.edu/~hnn/pathneck We improve Pathneck to cover the last hop This allows us to measure the RTT and the access bandwidth of many end users.
Ningning HuCarnegie Mellon University7 Methodology Measurement sources: 18 nodes from a large tier-1 ISP 14 in the US, 3 in Europe, and 1 in East-Asia Large fraction of paths cover other ISPs Play the role of possible replica sites Measurement destinations: 164,130 IP addresses from different prefixes 67,271 IPs correspond to real online hosts Firewalls etc sometime require us to use intermediate node as “virtual” destination Play the role of clients accessing the web
Ningning HuCarnegie Mellon University8 Results Internet end user RTT distribution and access bandwidth distribution Optimization results For RTT For bandwidth For data transmission time
Ningning HuCarnegie Mellon University9 RTT Distribution The RTT “views” of Internet clients from different geographical locations are significantly different US-NE Europe East-Asia
Ningning HuCarnegie Mellon University10 Bandwidth Distribution US-NE EuropeEast-Asia The bandwidth “views” are much more alike
Ningning HuCarnegie Mellon University11 End Access Bandwidth Distribution Low access bandwidth still dominates among end users 40% < 2.2Mbps 50% < 4.2Mbps 62.5% < 10Mbps Limited by downstream bandwidth of measurement source
Ningning HuCarnegie Mellon University12 Bottleneck Location Distribution 75% of bottleneck links are at the last two hop Little chance to avoid these bottlenecks using replication However, when access bandwidth is higher than 40Mbps, content replication can help to improve performance
Ningning HuCarnegie Mellon University13 Results Internet end user RTT distribution and access bandwidth distribution Optimization results For RTT For bandwidth For data transmission time
Ningning HuCarnegie Mellon University14 Optimization Algorithm We use simple greedy algorithm to optimize the performance of our replication infrastructure In each step, select the replication node that has the largest marginal utility Greedy algorithm has been shown to be able to obtain results very close to the optimal results For our study, it is only 0.1% worse than the optimal results from brute-force search
Ningning HuCarnegie Mellon University15 RTT Optimization RTT optimization results have a clear geographical pattern The first 5 replicas provide most of the benefit US-East Europe East-Asia US-West US-Central
Ningning HuCarnegie Mellon University16 Marginal Utility of RTT Optimization The first 5 nodes have significant improvement (i.e., larger than 5%) [ Marginal utility: the relative performance improvement from a specific node ]
Ningning HuCarnegie Mellon University17 Bandwidth Optimization The first 2 replicas provide most of the benefit
Ningning HuCarnegie Mellon University18 Marginal Utility for B.W. Optimization Only the first 2 (3) nodes have significant improvement
Ningning HuCarnegie Mellon University19 For Well-provisioned Access Links Replication can indeed improve bandwidth performance for end users with access bandwidth larger than 40Mbps 74% 35% 54Mbps
Ningning HuCarnegie Mellon University20 Data Transmission Time End-users’ data transmission time depends on delay, bandwidth, and data size We estimate data transmission time using a simplified TCP model: a slow start and congestion avoidance phase Assumes no packet loss Slow start: transfer time is delay sensitive Congestion avoidance: bandwidth sensitive Data size determines whether replication should optimize delay or bandwidth Use “slow-start size” as cross over point Results: 70% of paths have slow-start size larger than 10KB Larger than the average web page
Ningning HuCarnegie Mellon University21 Data Transmission Time (2) The transmission times for 10KB, 100KB, 1MB and 10MB are 0.4s, 1.1s, 6.4s, and 59.2s, respectively
Ningning HuCarnegie Mellon University22 Related Work Content replication with different optimization metrics Geographic location, network hops and latency, Retrieval costs, update cost, storage cost, QoS guarantee, … Greedy algorithm used in replica selection
Ningning HuCarnegie Mellon University23 Conclusion Quantify Internet end-node access- bandwidth distribution and bottleneck location distribution Two differences distinguish the optimization on bandwidth and on RTT Geographic location is not important for bandwidth optimization For throughput, only well-provisioned end users can benefit from content replication
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