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Server Traffic Management

Server Traffic Management. Jeff Chase Duke University, Department of Computer Science CPS 212: Distributed Information Systems. The Server Selection Problem. server array A. server farm B. Which server?. not-so-great solutions static client binding manual selection HTTP forwarding.

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Server Traffic Management

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  1. Server Traffic Management Jeff Chase Duke University, Department of Computer Science CPS 212: Distributed Information Systems

  2. The Server Selection Problem server array A server farm B Which server? not-so-great solutions static client binding manual selection HTTP forwarding Which network site? better old solutions DNS round robin [Brisco, RFC 1794] WebOS “smart clients” etc. [Vahdat97] today’s buzzwords content-aware traffic management content switching (L4-L7) server switching web switching “Contact the weather service.”

  3. Traffic Management for Clusters • Today we focus on the role of the network infrastructure in routing requests to servers in a cluster. • Ignore the wide-area problem for now (DNS and other tricks later). • Relatively simple switches can support ACLs to filter traffic to specific TCP or UDP ports from given addresses or subnets. • Current-generation server switches incorporate much richer L4 and content-aware switching features. • How much of the front end support can we build into the network elements while preserving “wire speed” performance? • What request routing policies should server arrays use? • Key point: the Web is “the only thing that matters” commercially. • TCP with HTTP+SSL is established as lingua franca, so more TCP/HTTP/SSL functionality migrates into hardware or firmware.

  4. Traffic Management for Clusters Goals server load balancing failure detection access control filtering priorities/QoS external VIP management request locality transparent caching L4: TCP L7: HTTP SSL etc. virtual IP addresses (VIPs) smart switch Clients What to switch/filter on? L3 source IP and/or VIP L4 (TCP) ports etc. L7 URLs and/or cookies L7 SSL session IDs server array

  5. L4 Server Load Balancing (SLB) Issues switch redundancy mechanics of L4 switching handling return traffic server failure detection (health checks) load balancer Policies random weighted round robin (WRR) lightest load least connections server array Key point: the heavy lifting of server selection happens only on connect request (SYN). Performance metric: connections per second. Limitations connection-grained no request locality no session locality failover?

  6. Mechanics of L4 Switching a b c d Smart switch: 1. recognizes connect request (TCP SYN) 2. selects specific server (d) for service at p2 3. replaces x with d in connect request packet 4. remembers connection {(C,p1),(d,p2)} 5. for incoming packets from (C,p1) for (x,p2) replace virtual IP address x with d forward to d 6. for outgoing packets from (d,p2) for (C,p1) replace d with x forward to C an instance of network address translation (NAT) x “Client C at TCP port p1 requests connection to TCP server at port p2 at VIP address x.”

  7. Handling Return Traffic fast dumb switch incoming traffic routes to smart switch smart switch changes MAC address smart switch leaves dest VIP intact all servers accept traffic for VIPs server responds to client IP dumb switch routes outgoing traffic simply a matter of configuration Clients slow smart switch server array examples IBM eNetwork Dispatcher (host-based) Foundry, Alteon, Arrowpoint, etc. alternatives TCP handoff (e.g., LARD)

  8. URL Switching Idea: switch parses the HTTP request, retrieves the request URL, and uses the URL to guide server selection. a,b,c Example: Foundry host name URL prefix URL suffix Substring pattern URL hashing d,e,f web switch g,h,i Issues HTTP parsing cost URL length delayed binding server failures HTTP 1.1 session locality hybrid SLB and URL popular objects server array Advantages separate static content from dynamic reduce content duplication improve server cache performance cascade switches for more complex policies

  9. The Problem of Sessions • In some cases it is useful for a given client’s requests to “stick” to a given server for the duration of a session. • This is known as session affinity or session persistence. • session state may be bound to a specific server • SSL negotiation overhead • One approach: remember {source, VIP, port} and map to the same server. • The mega-proxy problem: what if the client’s requests filter through a proxy farm? Can we recognize the source? • Alternative: recognize sessions by cookie or SSL session ID. • cookie hashing • cookie switching also allows differentiated QoS • Think “frequent flyer miles”.

  10. LARD Idea: route requests based on request URL, to maximize locality at back-end servers. LARD predates commercial URL switches, and was concurrent with URL-hashing proxy cache arrays (CARP). a,b,c d,e,f LARD front-end (a,b,c: 1) (d,e,f: 2) (g,h,i: 3) g,h,i Policies 1. LB (locality-based) is URL hashing. 2. LARD is locality-aware SLB: route to target’s site if there is one and it is not “overloaded”, else pick a new site for the target. 3. LARD/R augments LARD with replication for popular objects. server array LARD front-end maintains an LRU cache of request targets and their locations, and table of active connections for each server.

  11. LARD Performance Study • LARD paper compares SLB/WRR and LB with LARD approaches: • simulation study • small Rice and IBM web server logs • jiggle simulation parameters to achieve desired result • Nodes have small memories with greedy-dual replacement. • WRR combined with global cache-sharing among servers (GMS). • WRR/GMS is global cache LRU with duplicates and cache-sharing cost. • LB/GC is global cache LRU with duplicate suppression and no cache-sharing cost.

  12. LARD Performance Conclusions • 1. WRR has the lowest cache hit ratios and the lowest throughput. • There is much to be gained by improving cache effectiveness. • 2. LB* achieve slightly better cache hit ratios than LARD*. • WRR/GMS lags behind...it’s all about duplicates. • 3. The caching benefit of LB* is minimal, and LB is almost as good as LB/GC. • Locality-* request distribution induces good cache behavior at the back ends: global cache replacement adds little. • 4. Better load balancing in the LARD* strategies dominates the caching benefits of LB*. • LARD/R and LARD achieve the best throughput and scalability; LARD/R yields slightly better throughput.

  13. LARD Performance: Issues and Questions • 1. LB (URL switching) has great cache behavior but lousy throughput. • Why? Underutilized time results show poor load balancing. • 2. WRR/GMS has good cache behavior and great load balancing, but not-so-great throughput. • Why? How sensitive is it to CPU speed and network speed? • 3. What is the impact of front-end caching? • 4. What is the effectivness of bucketed URL hashing policies? • E.g., Foundry: hash URL to a bucket, pick server for bucket based on load. • 5. Why don’t L7 switch products support LARD? Should they? • [USENIX 2000]: use L4 front end; back ends do LARD handoff.

  14. Possible Projects • 1. Study the impact of proxy caching on the behavior of the request distribution policies. • “flatter” popularity distributions • 2. Study the behavior of alternative locality-based policies that incorporate better load balancing in the front-end. • How close can we get to the behavior of LARD without putting a URL lookup table in the front-end? • E.g., look at URL switching policies in commercial L7 switches. • 3. Implement request switching policies in the FreeBSD kernel, and measure their performance over GigE. • Mods to FreeBSD for recording connection state and forwarding packets are already in place. • 4. How to integrate smart switches with protocols for group membership or failure detection?

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