Load balancing policies apply to both requests from internal services inside the service mesh, and requests from external clients accessing services in your datacenter through an ingress gateway. If we need to distribute data, we must know which shard is the owner for a particular key. Save my name, email, and website in this browser for the next time I comment. Afterwards, there’s less variation, and the servers stay comfortably below 100 Mbit/s each. When we have multiple servers and a request comes in, which server should it direct to? If the consistent parameter is specified, the ketama consistent hashing method will be used instead. One optimized version of Round Robin to solve this drawback is Weighted Round Robin, which takes the machine infrastructure into consideration. As the system grows, we always want to implement caching. Every time a destination gets unhealthy, the mapping from hash ranges to destinations gets completely rebuilt taking into account only healthy destinations. Therefore, my subsequent request can be returned from server A’s cache. So, I see at least two consistent-hash-based load balancing modes: Equal. To distribute requests among servers using consistent hashing, HAProxy takes a hash of part of the request (in our case, the part of the URL that contains the video ID), and uses that hash to choose an available backend server. On November 25, HAProxy 1.7.0 was designated as a stable release, so bounded-load consistent hashing is now generally available. So far what we implemented with modular operator works fine with caching and balancing the system. The parameter cannot be used along with the hash and random load balancing methods. Before moving forward, let’s dig into consistent hashing, a technique for distributing load among multiple servers. Much better! Your email address will not be published. Serving a billion requests per day with a dynamic video packager makes unique demands on a load balancer. So we decide to buy more servers so that we can support all of our customers. If that server is below its capacity, then assign the request to that server. Consistent hashing provides an alternative to multicast and directory schemes, and has several other advantages in load balancing and fault tolerance. If you are unfamiliar with consistent hashing, read about its basics at Post in Love for Programming. The least-connection policy was doing a good job of keeping servers from getting overloaded, and fetching things from memcached is fast enough that it doesn’t have a measurable effect on the response times. This is problematic since servers often go up or down and each such event would require nearly all objects to be reassigned and moved to new servers. What’s not graphed is performance, in terms of response times. As a result, in the worst case, all incoming requests now directs to a completely new server and all of our previous caches are useless. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Consistent hashing will send all of the requests for that popular content to the same subset of servers, which will have the bad luck of re… However, luckily, it turns out the Gods doing this is Cai Shen Ye ( a God of money in Chinese Taoism), The company has much more money thanks to the increase in the number of customers. That’s is where people use consistent hashing. Now, let say that a scale-out event happens while flow F is in progress and the number of load balancing group members increases to 6. Consider what happens when a node fails. So instead of only receiving requests from our neighbor and family, our only server now receives thousands or even millions of requests, making it become overwhelming and eventually crashes. Example. If a server is overloaded, the list of fallback servers will usually be. $m = new Memcached(); $m->setOption(Memcached::OPT_DISTRIBUTION, Memcached::DISTRIBUTION_CONSISTENT); ∙ Rice University ∙ 0 ∙ share . This will ensure an equal load among all available backend's destinations. There has to be one, since the highest capacity is above the average load, and it’s impossible for every server’s load to be above average. What’s making the difference from the modular operator? A naïve solution to achieve this would be to take some unique identifier, such as the source IP address from a request, put it through a hash function, and then use the modulo operator to get an index in the range [0, numberOfServers - 1], inclusive. The optional consistent parameter to the hash directive enables ketama consistent‑hash load balancing. Because they stayed exactly the same. But if some content is much more popular than others (as usual for the internet), it can be worse than that. If you have a collection of n cache machines then a common way of load balancing across them is to put object o in cache machine number hash(o) mod n. The load balancer’s job is exactly what its name describes: its purpose is to balance the load on each server by distributing the requests as uniformly as possible. Actually all of the mentioned algorithms are nothing new, and usually are possible to configure by all of the current load balancers. When we first started testing Skyfire in the real world, we took a simple approach to caching: we cached the indexes in memory on the cloud server where they were generated, and used consistent hashing in HAProxy to send requests for the same video file to the same cloud server. Optionally, if you want to load balance each HTTP request, select a OneConnect profile from the OneConnect Profile menu. This consistent hashing feature is essential to successfully delivering video at scale. Use the following code to turn on consistent hashing. For the same input, hash function always returns the same output. This can result in overloaded servers, bad video playback, and unhappy users. I do really appreciate the idea of consistent hashing, especially on what problem it’s trying to solve and how elegant the solution is. Then there’s consistent hashing. Too many requests were sent to non-ideal servers to be worthwhile. If you read my previous articles, probably you will notice I’m the one who really sees the importance of application deployment, distribution, and operations. Mathematical proofs and simulations are nice, but it’s hard to truly believe until you see real traffic hit real servers. The reason and logic are quite the same as mentioned in Weighted Round Robin. Requests are evenly distributed across all upstream servers based on the user‑defined hashed key value. In the limit as c increases to ∞, the algorithm becomes equivalent to plain consistent hashing, without balancing; as c decreases to near 1 it becomes more like a least-connection policy and the hash becomes less important. After switching to the bounded-load algorithm, a much bigger fraction of requests hit local cache, regardless of how many servers were running. I was disappointed, but rather than wasting time trying to rescue it, we went ahead with the least-connections and shared cache approach above. If you have nine servers and you add a tenth, only one-tenth of requests will (by luck) hash to the same server as they did before. This is good for caching. I read the paper, and the algorithm was remarkably simple. Consistent hashing uses a more elaborate scheme, where each server is assigned multiple hash values based on its name or ID, and each request is assigned to the server with the “nearest” hash value. Consider the problem of load balancing where a set of objects (say, web pages or video segments) need to be assigned to a set of $${\displaystyle n}$$ servers. You can use a single hash function to maintain a… This would mean that instead of doing modular 4, we change to modular 5. At night, there’s less traffic, so we shut servers down, and the local cache performance went up somewhat. It’s simple, and it works well as long as the list of servers is stable. Dynamic load balancing lies at the heart of distributed caching. Maglev is a consistent hash scheduler hashing a 5-tuple of information from each packet—the protocol, source address and port, and destination address and port—to determine a backend server. As you may know, load balancing helps achieve this: However, load balancing also requires a way to map incoming requests to specific servers. If a server is overloaded, the list of fallback servers chosen will be the same for the same request hash — i.e. by aakashchotrani | Aug 16, 2019 | Algorithms, System Design. down marks the server as permanently unavailable. Consistent Hashing helps us to evenly distribute the weight across all servers. With traditional “modulo hashing”, you simply consider the request hash as a very large number. To support giving servers different “weights”, as HAProxy does, the algorithm has to change slightly, but the spirit is the same — no server can exceed its fair share of the load by more than 1 request. Why wasn’t there a way to say “use consistent hashing, but please don’t overload any servers”? This leads to the problem of this post. The HAProxy maintainer, Willy Tarreau, was a real pleasure to work with. This will helps the request distribution become less skewed, leading to a reduction in the likelihood that one server becomes overwhelming. As a business grows or shrinks, there will be a time when we need to change our number of the server. However, in this post, lets simply agree on one thing: For similar and frequent requests, caching will help reduce the complex operations in our backend and database, leading to a faster time of returning the result. By doing this. This leads to the question, after hashing value, how to map them to the server. Andrew developed a new option in HAProxy that fine-tunes consistent-hash load balancing, called hash-balance-factor, that allows a request to consider the current load on the server in addition to whether it has cached the needed video chunk. But now that a much smaller fraction of the requests rely on the shared cache, and because that fraction doesn’t depend on the number of servers we run, we can look forward to handling a lot more traffic without saturating the memcached servers. A few more minor tweaks and it was accepted in time for HAProxy 1.7.0-dev5, released on October 26. TLB solves this problem by using consistent hashing. It took a little while to work in those suggestions and get things up to snuff, but after a few weeks I had a polished version ready to send to the list. In the event of having one more powerful server than the other, this distribution is equal but of course not optimized for the whole system. The hash function will return a number that we can map into a corresponding server. Some requests need a longer time than others and consist of more complex operations on the server. Some details are left out, and if you intend to implement it yourself, you should definitely go to the original paper for information. Now, we can do a clockwise traversing, and direct the request to its nearest server. If you take that number modulo the number of available servers, you get the index of the server to use. DASH and HLS, however, don’t use a single file — they use short segments of video, delivered separately. JOHN CLEVELEY Sr. Engineering Manager, BuzzFeed. 08/23/2019 ∙ by John Chen, et al. We’ll use a scripty for our family, and so every hash function in is a function . After that, we look into the logs and realize not all incoming requests are the same. And it worked! They can either direct to server E or still go to server A. Proposed in 1997, consistent hashing describes a way to provide a mapping algorithm that remains relatively stable even when new backends are added to or removed from the list. Required fields are marked *. Armed with that success, in September I sent a proof-of-concept patch to HAProxy. The problem of using Hash to take the model load balancing. Learn how Buzzfeed built a microservices request router using NGINX Plus. A further upgrade of simple consistent hashing is the implementation of Virtual node, where we put the server id through many hash functions and mark them many places on the circle. © 2020 Jake's Tech Talk. , as usual and remove servers without completely disturbing the set of allowed inputs ( for “ Universe ”.! Damian Gryski had tweeted, of an arXiv paper titled consistent hashing comes with its own:... For distributing load among all available backend 's destinations the location of all of customers! Code to turn on consistent hashing with Bounded Loads server B has twice processing capacity compared to,. “ Silhouette ” Threat less than 1 request also has a Weighted version ( Weighted Least algorithm... 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