• 3 requests are sent to Server A • 2 requests are sent to Server B • 1 request is sent to Server C. In this manner, the weighted round robin algorithm distributes the load according to each server's capacity. What is the Difference Between Load Balancer Sticky Session vs. Round Robin Load Balancing? A load balancer that keeps sticky sessions will create a unique session object for each client. For each request from the same client, the load balancer processes the request to the same web server each time, where data is stored and updated as long as the session exists. Sticky sessions can be more efficient because unique session-related data does not need to be migrated from server to server. However, sticky sessions can become inefficient if one server accumulates multiple sessions with heavy workloads, disrupting the balance among servers. If sticky load balancers are used to load balance round robin style, a user's first request is routed to a web server using the round robin algorithm.
Our intention is simply to provide an example of the difference that algorithmic choice can make. That said, large-scale production experiments at Twitter have verified the effectiveness of least loaded and peak EWMA (as well as other load balancing algorithms), and these algorithms are used at scale to power much of Twitter's infrastructure today. CONCLUSION For systems that load balance higher-level connections such as RPC or HTTP calls, where Layer 5 information such as endpoint latencies and request depths are available, round robin load balancing can perform significantly worse than other algorithms in the presence of slow endpoints. These systems may show significantly improved performance in the face of slow endpoints by using algorithms that can take advantage of Layer 5 information. If the results above are applicable to your situation, you may want to take advantage of algorithms like least loaded and peak EWMA. Production-tested implementations of these algorithms are available today in Finagle, and in Linkerd, the open-source service mesh for cloud-native applications.
These days, CDNs like Akamai also offer global load balancing services (e. ). Amazon's EC2 hosting also supports this kind of feature for sites hosted there (see). Since users tend not to move across continents in the course of a single session, you automatically get affinity (aka "stickiness") with geographic load balancing, assuming your pairs are located in separate data centers. Keep in mind that geo-location is really hard since you also have to geo-locate your data to ensure your back-end cross-data-center network doesn't get swamped. I suspect that F5 and other vendors also offer single-datacenter solutions which achieve the same ends, if you're really concerned about the single point of failure of network infrastructure (routers, etc. ) inside your datacenter. But router and switch vendors have high-availability solutions which may be more appropriate to address that issue. Net-net, if I were you I wouldn't worry about multiple pairs of load balancers. Get one pair and, unless you have a lot of money and engineering time to burn, partner with a hoster who's good at keeping their data center network up and running.
Capacity isn't the only basis for choosing the Weighted Round Robin (WRR) algorithm. Sometimes, you'll want to use it if say you want one server to get a substantially lower number of connections than an equally capable server for the reason that the first server is running business-critical applications and you don't want it to be easily overloaded. Least Connections There can be instances when, even if two servers in a cluster have exactly the same specs (see first example/figure), one server can still get overloaded considerably faster than the other. One possible reason would be because clients connecting to Server 2 stay connected much longer than those connecting to Server 1. This can cause the total current connections in Server 2 to pile up, while those of Server 1 (with clients connecting and disconnecting over shorter times) would virtually remain the same. As a result, Server 2's resources can run out faster. This is depicted below, wherein clients 1 and 3 already disconnect, while 2, 4, 5, and 6 are still connected.
(See here for how to configure the load balancing algorithms in Linkerd. ) ACKNOWLEDGMENTS Thanks to Marius Eriksen, Alex Leong, Kevin Lingerfelt, and William Morgan for feedback on early drafts of this document. FURTHER READING The Tail at Scale. Michael Mitzenmacher. 2001. The Power of Two Choices in Randomized Load Balancing. IEEE Trans. Parallel Distrib. Syst. 12, 10 (October 2001), 1094-1104. Have you adopted Linkerd? Let us know and we'll send you some sweet swag!
Now, do you see why round robin isn't equally distributing the traffic? In the round robin algorithm, older nodes in the pool will always end-up processing more requests. Newly added nodes will end up processing less amount of traffic. The load is never evenly distributed. For maintenance, patching & installation purposes, you have to continually keep adding and removing nodes from the load balancer pool. If you instrumented auto-scaling in place, the problem gets even worse. In auto-scaling nodes are more dynamic. They get added and removed even more frequently. What algorithm to use? Fig: New node added. Traffic is evenly distributed in Least Connections There are a variety of load balancing algorithms: Weighted Round Robin, Random, Source IP, URL, least connections, least traffic, and least latency. SEE ALSO: Python tutorial: Best practices and common mistakes to avoid Given the shortcoming in round robin, you can consider trying other choices. One choice you may consider is: ' least connections ' algorithm.
Overview So your load balancer supports multiple load balancing algorithms but you don't know which one to pick? You will in a minute. In this post, we compare 5 common load balancing algorithms, highlighting their main characteristics and pointing out where they're best and not well suited for. Let's begin. Prefer to watch a video version of this post instead? Click or tap to play. Round Robin Round Robin is undoubtedly the most widely used algorithm. It's easy to implement and easy to understand. Here's how it works. Let's say you have 2 servers waiting for requests behind your load balancer. Once the first request arrives, the load balancer will forward that request to the 1st server. When the 2nd request arrives (presumably from a different client), that request will then be forwarded to the 2nd server. Because the 2nd server is the last in this cluster, the next request (i. e., the 3rd) will be forwarded back to the 1st server, the 4th request back to the 2nd server, and so on, in a cyclical fashion.
As per this algorithm, the node which has the least number of connections will get the next request. Thus, as per our earlier example, when new 100 users start to use the application, all new users' requests will be sent to node-C. Thus, the load will be equally distributed among all nodes.
Examples of load balancing methods include random assignment, round robin, source IP has and least connection. Random assignment By far the least organized of all the load balancing methods, random assignment does exactly what it says: It randomly assigns each workload to a server in a group of servers ( server pool). The theory behind random assignment sounds more complicated than it is. In probability theory, the Law of Large Numbers says that as a sample size grows, the median (middle) result in a sample set will eventually match the mean (average) result. Applied here, it means that the more times a workload is randomly assigned to a server within the pool, eventually every server in the pool will be handling approximately equal workloads even though the workloads may initially be unequal. Of course, there are a few obvious, but conquerable, issues here: It's entirely possible that some servers in random assignment will have significantly more load than others. After all, random assignment doesn't care if server A has 4, 000 connections and server F has 400.