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“Hashing Out” the Future of Enterprise and Data-Center Networks Jennifer Rexford Princeton University http://www.cs.princeton.edu/~jrex Joint with Changhoon Kim, Minlan Yu, Matthew Caesar, and Alex Fabrikant Challenges in Edge Networks • Large number of hosts –Tens or even hundreds of thousands of hosts • Dynamic hosts –Host mobility –Virtual machine migration • Cost conscious –Equipment costs –Network-management costs Need a scalable and efficient self-configuring network 2 An All-Ethernet Solution? • Self-configuration –Hosts: MAC addresses –Switches: self-learning • Simple host mobility –Location-independent flat addresses • But, poor scalability and performance –Flooding frames to unknown destinations –Large forwarding tables (one entry per address) –Broadcast for discovery (e.g., ARP, DHCP) –Inefficient delivery of frames over spanning tree 3 An All-IP Solution? • Scalability –Hierarchical prefixes (smaller tables) –No flooding • Performance –Forwarding traffic over shortest paths • But, several disadvantages –Complex joint configuration of routing and DHCP –Clumsy mobility (location-dependent addresses) –Expensive longest-prefix match in data plane 4 Compromise: Hybrid Architecture Ethernet-based IP subnets interconnected by routers Ethernet Bridging - R Flat addressing Self-learning R Flooding Forwarding along a tree IP Routing R - Hierarchical addressing R - Subnet configuration - Host configuration - Forwarding along shortest paths R Sacrifices Ethernet’s simplicity and IP’s efficiency for scalability 5 Can We Do Better? • Shortest-path routing on flat addresses –Shortest paths: scalability and performance –MAC addresses: self-configuration and mobility H S S H H H S S S S S H S H S H S S S S H H • Scalability without hierarchical addressing –Limit dissemination and storage of host info –Sending packets on slightly longer paths 6 Outline • SEATTLE –Distributed directory service • BUFFALO –Compact forwarding tables • SeaBuff –Combining Seattle and Buffalo • Deployment scenarios –Enterprises vs. data centers 7 SEATTLE Scalable Ethernet Architecture for Large Enterprises 8 SEATTLE Design Decisions Objective Approach Solution 1. Avoiding flooding Never broadcast unicast traffic 2. Restraining broadcasting Bootstrap hosts via unicast 3. Reducing routing state Populate host info only when and where it is needed Traffic-driven resolution with caching 4. Shortest-path forwarding Allow switches to learn topology L2 link-state routing maintaining only switch-level topology * Meanwhile, avoid modifying end hosts Network-layer one-hop DHT 9 Network-Layer One-hop DHT • Maintains <key, value> pairs with function F – Consistent hash mapping a key to a switch – F is defined over the set of live switches • One-hop DHT 2128-1 0 1 – Link-state routing ensures switches know each other • Benefits – Fast and efficient reaction to changes – Reliability and capacity naturally grow with size of the network 10 Location Resolution <key, val> = <MAC addr, location> x C Owner Host discovery A Hash F(MACx) = B y Forward directly from D to A User Publish <MACx, A> Tunnel to A Resolver Switches End hosts Control message Data traffic Tunnel to B B Store <MACx, A> D Traffic to x Hash F(MACx ) = B Notify <MACx, A> E 11 Address Resolution <key, val> = <IP addr, MAC addr> x <IPx ,MACx> Broadcast ARP request for IPx C A Hash F(IPx) = B Unicast look-up to B D y Hash F(IPx ) = B Unicast reply <IPx, MACx, A> B Store <IPx, MACx, A> E Traffic following ARP takes a shortest path without separate location resolution 12 Handling Network and Host Dynamics • Network events –Switch failure/recovery Change in <key, value> for DHT neighbor Fortunately, switch failures are not common –Link failure/recovery Link-state routing finds new shortest paths • Host events –Host location, MAC address, or IP address –Must update stale host-information entries 13 Handling Host Information Changes Dealing with host mobility x Old location F Host talking with x y A < x, A > < x, D > New location C < x, A > < x, D > B D < x, D > Resolver < x, A > < x, D > E MAC- or IP-address change can be handled similarly 14 Packet-Level Simulations • Large-scale packet-level simulation –Event-driven simulation of control plane –Synthetic traffic based on LBNL traces –Campus, data center, and ISP topologies • Main results –Much less routing state than Ethernet –Only slightly more stretch than IP routing –Low overhead for handling host mobility 15 Amount of Routing State Ethernet SEATTLE SEATTLE reduces the amount of routing w/ caching state by more than an order of magnitude SEATTLE w/o caching 16 Cache Size vs. Stretch Stretch = actual path length / shortest path length (in latency) ROFL SEATTLE offers near-optimal stretch with very small amount of routing state SEATTLE 17 Sensitivity to Mobility Ethernet SEATTLE rapidly updates routing state with very low overhead SEATTLE w/ caching SEATTLE w/o caching 18 Prototype Implementation XORP Click Interface Network Map OSPF Daemon Link-state advertisements User/Kernel Click Routing Table Ring Manager Host Info Manager Host-info registration and notification msgs SeattleSwitch Data Frames Data Frames Throughput: 800 Mbps for 512B packets, or 1400 Mbps for 896B packets 19 Conclusions on SEATTLE • SEATTLE –Self-configuring –Scalable –Efficient • Enabling design decisions –One-hop DHT with link-state routing –Reactive location resolution and caching –Shortest-path forwarding 20 BUFFALO Bloom Filter Forwarding Architecture for Large Organizations 21 Data Plane Scaling Challenge • Large layer-two networks –Many end-host MAC addresses –Many switches • Forwarding-table size becomes a problem –Requires a large, fast memory –Expensive and power hungry –Over-provisioning to avoid running out • Buffalo’s goal –Given a small, fast memory –… make the most of it! Bloom Filters • Bloom filters in fast memory – A compact data structure for a set of elements – Calculate s hash functions to store element x – Easy to check set membership – Reduce memory at expense of false positives x Vm-1 V0 0 0 0 1 0 0 0 1 0 1 h1(x) h2(x) h3(x) 0 1 0 0 0 hs(x) 23 Bloom Filter Forwarding • One Bloom filter (BF) per next hop – Store all addresses forwarded to that next hop Bloom Filters query packet Nexthop 1 Nexthop 2 hit …… Nexthop T 24 BUFFALO Challenges How to optimize memory usage? • Minimize the false-positive rate How to handle false positives quickly? • No memory/payload overhead How to handle routing dynamics? • Make it easy and fast to adapt Bloom filters 25 1. Optimize Memory Usage • Goal: Minimize overall false-positive rate – Probability that one BF has a false positive • Input: – Fast memory size M – Number of destinations per next hop – The maximum number of hash functions • Output: the size of each Bloom filter – Larger BF for next hop with more destinations 26 1. Optimize Memory Usage (cont.) • Constraints – Memory constraint Sum of all BF sizes <= fast memory size M – Bound on number of hash functions To bound CPU calculation time Bloom filters share the same hash functions • Convex optimization problem – An optimal solution exists – Solved by IPOPT optimization tool – Runs in about 50 msec 27 1. Minimize False Positives • Forwarding table with 200K entries, 10 next hops Overall False Positives [fraction] • 8 hash functions 1.00E+00 1.00E-01 1.00E-02 1.00E-03 1.00E-04 1.00E-05 1.00E-06 0 200 400 600 Memory size (KB) 800 1000 28 1. Comparing with Hash Table Fast Memory Size (MB) • Save 65% memory with 0.1% false positive 14000 12000 10000 8000 6000 4000 2000 0 hash table fp=0.01% fp=0.1% fp=1% 0 500 1000 1500 #Forwarding Table Entries (K) 65% 2000 29 2. Handle False Positives • Design goals – Should not modify the packet – Never go to slow memory – Ensure timely packet delivery • BUFFALO solution – Exclude incoming interface Avoid loops in one false positive case – Random selection among the rest Guarantee reachability with multiple FPs 30 2. One False Positive • Most common case: one false positive –When there are multiple matching next hops –Avoid sending to incoming interface • We prove that there is at most a 2-hop loop with a stretch <= l(AB)+l(BA) A dst B 31 2. Multiple False Positives • Handle multiple false positives – Random selection from matching next hops – Random walk on shortest path tree plus a few false positive links – To eventually find out a way to the destination False positive link Shortest path tree for destination dst 32 2. Multiple False Positives (cont.) • Provable stretch bound –With k false positives –… expected stretch is at most O(k2*3k/3) –Proved by random walk theories • Stretch bound is actually not that bad –False positives are independent Different switches use different hash functions –k false positives for one packet are rare Probability of k false positives drops exponentially in k • Tighter bounds in special cases: e.g., tree 33 2. Stretch in Campus Network 1.E+00 fp=0.5% When fp=0.001% fp=0.1% When fp=0.5%, 99.9% of the packets fp=0.001% have no stretch 0.0002% packets 0.0003% packets have a stretch 6 times have aofstretch of path length shortest shortest path length 1.E-01 CCDF 1.E-02 1.E-03 1.E-04 1.E-05 1.E-06 0 1 2 3 4 5 6 Stretch normalized by shortest path length 7 34 3. Update on Routing Change • Use CBF in slow memory – Assist BF to handle forwarding-table updates – Easy to add/delete a forwarding-table entry CBF in slow memory 2 0 1 0 0 0 Delete a route 3 0 2 0 0 0 0 0 2 0 1 0 1 0 0 0 0 0 1 0 BF in fast memory 1 0 1 0 0 0 3. Occasionally Resize BF • Under significant routing changes –# of addresses in BFs changes significantly –Re-optimize BF sizes • Use CBF to assist resizing BF –Large CBF and small BF –Easy to expand BF size by contracting CBF CBF 0 0 1 1 0 0 0 3 0 Easy to contract CBF to size 4 BF 0 Hard to expand to size 4 0 0 1 0 BUFFALO Switch Architecture Prototype implemented in kernel-level Click 37 Prototype Evaluation • Environment – 3.0 GHz 64-bit Intel Xeon – 2 MB L2 data cache (fast-memory size M) – 200K forwarding-table entries and 10 next hops • Peak forwarding rate – 365 Kpps, 1.9 μs per packet – 10% faster than hash-based EtherSwitch • Performance with forwarding-table updates – 10.7 μs to update a route – 0.47 s to reconstruct BFs based on CBFs 38 Conclusion on BUFFALO • BUFFALO –Small, bounded memory requirement –Small stretch –Fast reaction to routing updates • Enabling design decisions –Bloom filter per next hop –Optimizing of Bloom filter sizes –Preventing forwarding loops –Dynamic updates using counting Bloom filters 39 SeaBuff Seattle + Buffalo 40 Seattle + Buffalo • Seattle –Shortest-path routing and scalable control plane –Fewer host MAC addresses stored at switches • Buffalo –Less memory for given # of routable addresses –Graceful handling of increase in # of addresses • Combined data plane egress switch destination cache Bloom filter Seattle Buffalo outgoing link Two Small Sources of Stretch x y C Traffic to x A D B E Relay for x • Seattle: diverting some packets through relay B • Buffalo: extra stretch from D to B, or B to A Choosing the Right Solution • Spatial distribution of traffic –Sparse: caching in Seattle is effective –Dense: caching is not as effective • Temporal distribution of traffic –Stable: shortest path routing is effective –Volatile: forwarding through relay spreads traffic • Topology –Arbitrary: false positives have more impact –Tree-like: false positives have less impact Conclusions • Growing important of edge networks –Enterprise networks –Data center networks • Shortest-path routing and flat addressing –Self-configuring and efficient –Scalability in exchange for stretch • Ongoing work –SeaBuff = Seattle + Buffalo –Theoretical analysis of stretch in Buffalo –Efficient access control in OpenFlow