Server Traffic Management Server Traffic Management Jeff Chase - PowerPoint PPT Presentation

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

The Server Selection Problem 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] “Contact the weather service.” today’s buzzwords content-aware traffic management content switching (L4-L7) server switching web switching

Traffic Management for Clusters 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.

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

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

Mechanics of L4 Switching 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 x 4. remembers connection {( C , p1 ),( d , p2 )} 5. for incoming packets from ( C , p1 ) for ( x , p2 ) replace virtual IP address x with d “Client C at TCP forward to d port p1 requests 6. for outgoing packets from ( d , p2 ) for ( C , p1 ) connection to TCP server at port p2 at replace d with x VIP address x.” forward to C an instance of network address translation (NAT)

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

URL Switching 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 d,e,f host name web URL prefix switch g,h,i URL suffix Substring pattern URL hashing Issues server array HTTP parsing cost URL length Advantages delayed binding separate static content from dynamic server failures reduce content duplication HTTP 1.1 improve server cache performance session locality cascade switches for more complex policies hybrid SLB and URL popular objects

The Problem of Sessions 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”.

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

LARD Performance Study 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.

LARD Performance Conclusions 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.

LARD Performance: Issues and Questions 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.

Possible Projects 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?