consistent hashing load balancing

In load balancing, assuming that there are three servers (numbered 0, 1 and 2 respectively), the hash modularization is used to calculate the IP of the visitor through a fixed formula.hash(IP) % N(N is the number of servers), so that each IP can be located to a specific server. That’s where the Hashing algorithm comes in. 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. First, define a balancing factor, c, which is greater than 1. c controls how much imbalance is allowed between the servers. This consistent hashing feature is essential to successfully delivering video at scale. This will helps the request distribution become less skewed, leading to a reduction in the likelihood that one server becomes overwhelming. 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. while the idea of consistent hashing with forwarding to meet capacity constraints seems pretty obvious, it appears not to have been considered before. The shared cache requiredsome additional bandwidth, but the load was balanced much more evenly between servers. Let’s continue from our 2 servers above, if all odd requests are just simple database query, whereas all even requests are much more complicated (maybe an update, which involves many services), then the system will not be optimized anymore. Before moving forward, let’s dig into consistent hashing, a technique for distributing load among multiple servers. 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. Flexible Microservices. The benefit of this added complexity is that when a server is added or removed, most requests will map to the same server that they did before. Does it sound familiar? After that, we look into the logs and realize not all incoming requests are the same. the same server will consistently be the “second choice” for a popular piece of content. Required fields are marked *. 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. TLB solves this problem by using consistent hashing. This will ensure an equal load among all available backend's destinations. Learn how you can deploy NGINX on any cloud, eliminate vendor lock‑in, and reduce complexity. All in all, I’m very happy to see how a little bit of algorithm work turned a single point of failure into something a whole lot better. They can either direct to server E or still go to server A. So consistent hashing lets us add and remove servers without completely disturbing the set of cached items that each server holds. Since we get many requests for the same video file, it makes sense to cache the index and re-use it later. The policies will be defined using a service-resolver configuration entry. As we discussed the two algorithms above, we realize that the request of one particular user will not always go to one server. As I prefer optimism, let’s assume the business grows and now we need to buy one more server, resulting in a total of 5. • Enable load balancing across partitions • Accommodate state too big for one server • What if operation touches multiple partitions? In some context, load balancing and sharding also need to keep associating tasks or data to the same node: If we need to serialize, handle one by one, the operations for a given consumer, we must route the request to the same node. Because of its mathematical properties, consistent hashing only balances loads about as well as choosing a random server for each request, when the distribution of requests is equal. The idea is when a request comes in, we will use its value(usually IP address and port number) and put in a hash function (usually a uniform hash function, to ensure the output is uniformly distributed). Resilient hashing works in conjunction with the default statichashing algorithm. So if server B has twice processing capacity compared to A, it will be assigned two times of requests on a cyclical basis. We call the set of allowed inputs (for “Universe”). We run Vimeo’s dynamic video packager, Skyfire, in the cloud, serving almost a billion DASH and HLS requests per day. But how does Skyfire know which bytes it needs to fetch when a player requests, say, the 37th segment of a file? Each of the mentioned algorithms has its own pros and cons, so depending on our situation, we can find the most suitable one. Example. So what is consistent hashing and why should you care? If that server is below its capacity, then assign the request to that server. It fetches only the necessary part of the MP4 file, makes a few adjustments for the DASH or HLS format, and sends the result back to the user. The problem of using Hash to take the model load balancing. Unlike Round Robin, Least Connection Algorithm will consider the currently active sessions of all the servers in our system. So far what we implemented with modular operator works fine with caching and balancing the system. Now, let’s try putting the 5th server in the circle. So, I see at least two consistent-hash-based load balancing modes: Equal. And we added a second-level cache using memcached, shared among the servers, so that an index generated by one server could be retrieved by a different one. One of the popular ways to balance load in a system is to use the concept of consistent hashing. Making an app is straightforward, but know how to distribute the product and making our customers having the same experience with our localhost is not that easy. As we can see, I put the name of this algorithm in the title of this post. Fast forward to August 2016. I am looking into having the ribbon load balancer choose host based on an id (UUID) per request. $m = new Memcached(); $m->setOption(Memcached::OPT_DISTRIBUTION, Memcached::DISTRIBUTION_CONSISTENT); So how is it related to our topic today. Now, we can do a clockwise traversing, and direct the request to its nearest server. If we need to distribute data, we must know which shard is the owner for a particular key. Instead of consistent-hashing based balancing, we used a “least connections” load-balancing policy in HAProxy, so that the load would be distributed evenly among servers. The reason and logic are quite the same as mentioned in Weighted Round Robin. Andrew Rodland shares a new consistent hashing algorithm developed by Google researchers that helped improve cache locality and optimize delivery—and made a contribution to open source software in the process. So how can it do this thing, I will write about some popular algorithms that people are using in their systems around the world, from the simple one to more complicated one, together with their pros and cons, (so be prepared for a bit technical). In addition, if a memcached server ever goes down, the overall effect it has on Skyfire will be much less. This would mean that we still can utilize most of our cache in our servers. Your email address will not be published. Revisiting Consistent Hashing with Bounded Loads. 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. As you may know, load balancing helps achieve this: However, load balancing also requires a way to map incoming requests to specific servers. This will helps the request distribution become less skewed, leading to a reduction in the likelihood that one server becomes overwhelming. One optimized version of Round Robin to solve this drawback is Weighted Round Robin, which takes the machine infrastructure into consideration. let’s say my request-id is 192168118080 which then becomes 20 after the hashing function, and then always go to server A thanks to the modular operators. Unimog is designed to run on the same general-purpose servers that provide application services, rather than requiring a separate tier of servers dedicated to load balancing. 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. When a request arrives, compute the average load (the number of outstanding requests, m, including the one that just arrived, divided by the number of available servers, n). Your email address will not be published. Now, after months of praying in the pagoda (or going to the church), some magical Gods know about our app and suddenly the product goes viral. Introduction The topic of this blog is one of the fundamental concepts of System Design. Today I’d like to talk about a new algorithmic development, bounded-load consistent hashing, and how it eliminates a bottleneck in our video delivery. The distribution of requests is the same as consistent hashing as long as servers aren’t overloaded. We do this because we want to send a similar request in the future to be redirected to the same server this will make the response faster since we can use the already cached response. Therefore, my subsequent request can be returned from server A’s cache. The idea is straightforward, we keep directs it to the next server on a cyclical basis. 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. But there is always a new problem in every solution. 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. This leads to the question, after hashing value, how to map them to the server. The consistent-hashing algorithm is based on Consistent Hashing (or the Ketama Principle), which ensures that when the balancer gets modified by a change in its targets (adding, removing, failing, or changing weights), only the minimum number of hashing … The reason is it very easy to implement thanks to its simple logic. It performs dynamic load balancing: measurements of server load are used to adjust the number of connections going to each server, in order to accurately balance load. What’s not graphed is performance, in terms of response times. Load Balancing #1: Consistent Hashing Only using consistent hashing is suboptimal because it balances loads about as well as choosing a random server for each request, when the … The load balancer keeps track of which request is sent to which server by using the hash table. Why wasn’t there a way to say “use consistent hashing, but please don’t overload any servers”? The rest )outside of red eclipse) remains the same. 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. The idea is we also hash our server ID to a number, and map it to corresponding places in a big circle. Connection tracking and consistent hashing Network Load Balancing uses a connection tracking table and a configurable consistent hashing algorithm to determine how … One easy way is to use a modular operator for the number of servers we have. This can result in overloaded servers, bad video playback, and unhappy users. So we need to replace modular operators with a different mechanism, which can reuse our current cache in the event of changing number of servers. Why? If you’re already familiar with consistent hashing, feel free to go ahead and skip to the next section. I was disappointed, but rather than wasting time trying to rescue it, we went ahead with the least-connections and shared cache approach above. Let’s say we have 2 servers, then the first request comes to server a, the second comes to server B, the third comes back to server A, and go on… One big drawback of this algorithm is it assumes that all server having the same infrastructure and capacity. 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. • Need distributed transactions ... consistent hashing. A few more minor tweaks and it was accepted in time for HAProxy 1.7.0-dev5, released on October 26. This is the way we ran, happily, for the next year. That’s a lot! Then there’s consistent hashing. The optional consistent parameter to the hash directive enables ketama consistent‑hash load balancing. By doing this. 08/23/2019 ∙ by John Chen, et al. Modular operators have a downside that it will only work well when our number of servers is fixed. Otherwise, go to the next server in the hash ring and check its capacity, continuing until you find a server that has capacity remaining. Optionally, if you want to load balance each HTTP request, select a OneConnect profile from the OneConnect Profile menu. Use the following code to turn on consistent hashing. 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… 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. Multiply the average load by c to get a “target load”, t. In the original paper, capacities are assigned to servers so that each server gets a capacity of either ⌊t⌋ or ⌈t⌉, and the total capacity is ⌈cm⌉. •Why consistent hashing or DHT? Consistent... Load Balancing is a key concept to system design. One way of distributing objects evenly across the $${\displaystyle n}$$ servers is to use a standard hash function and place object $${\displaystyle o}$$ in server with id $${\displaystyle {\text{hash}}(o)\;\left({\text{mod }}n\right)}$$, However, if a server is added or removed (i.e., $${\displaystyle n}$$ changes), the server assignment of nearly every object in the system may change. But if some content is much more popular than others (as usual for the internet), it can be worse than that. To enable CARP hash persistence on a virtual server, configure the following virtual server settings: Select an HTTP profile from the HTTP Profile menu. Modern Load Balancing. He did a thorough review and provided some very valuable feedback. Here’s a graph of the cache behavior before and after changing our HAProxy configuration. Therefore the maximum capacity of a server is ⌈cm/n⌉, which is greater than c times the average load by less than 1 request. 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. 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 receiving a lot more traffic than the others. The code is pretty clean and well-organized, and after a few days of work I had something that worked well enough that I could replay some traffic through it and see the algorithm in action. A family of hash functions is just a set of possible hash functions to choose from. If you want to design a fault-tolerant distributed system you should be aware of load balancing and consistent hashing. The counterpart of consistent hashing is that it doesn’t provide a perfect hash, and so, in a farm of 4 servers, some may receive more clients than others. 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. So for requests with id "xyz-123" I always want server 1 to be chosen if it is available. Learn how Buzzfeed built a microservices request router using NGINX Plus. In my experience, values between 1.25 and 2 are good for practical use. It needs to look at an index that knows the location of all of the keyframes and all of the packets in the file. Adding code to HAProxy wasn’t too bad. JOHN CLEVELEY Sr. Engineering Manager, BuzzFeed. When a flow is affected by a member change, the Packet ForwardingEngine rebalances the flow by reprogramming the flow set table. JOHN GRAHAM-CUMMING Programmer, CloudFlare. Vimeo’s video files are stored as MP4 files, the same format used for download or “progressive” playback in the browser. Dynamic load balancing lies at the heart of distributed caching. Afterwards, there’s less variation, and the servers stay comfortably below 100 Mbit/s each. With resilient hashing, the chances of a flow being remapped areminimal if its path is unaffected by the LAG/ECMP group's member change. By utilizing this, we can always direct the request from a user to a corresponding server every single time, making the cache implementation more feasible. 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. When the request comes in, the balancer will direct to the server which is handling the least number of active sessions By doing this, the number of sessions on each server is not the same, however, the overall system will be much more optimized. However, consistent hashing comes with its own problem: uneven distribution of requests. With traditional “modulo hashing”, you simply consider the request hash as a very large number. This is good for caching. Here is a simplified sketch of the algorithm. As a business grows or shrinks, there will be a time when we need to change our number of the server. The hash function will return a number that we can map into a corresponding server. 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. If you are unfamiliar with consistent hashing, read about its basics at Post in Love for Programming. However, consistent hashing comes with its own problem: uneven distribution of requests. Indeed, the paper says. Consistent Hashing helps us to evenly distribute the weight across all servers. I do really appreciate the idea of consistent hashing, especially on what problem it’s trying to solve and how elegant the solution is. 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. That way, the cached data could be used again. I think this post is long enough so I’m gonna stop here. I read the paper, and the algorithm was remarkably simple. And before it can look at it, it needs to generate it. When members are added to or deleted from a LAG/ECMP group, thestatic hashing algorithm might remap destination paths. In consistent hashing a node is responsible for keys with ids from itself to its successor. Consistent hashing provides an alternative to multicast and directory schemes, and has several other advantages in load balancing and fault tolerance. After switching to the bounded-load algorithm, a much bigger fraction of requests hit local cache, regardless of how many servers were running. If a server is overloaded, the list of fallback servers chosen will be the same for the same request hash — i.e. Consistent Hashing And Load Balancing. At night, there’s less traffic, so we shut servers down, and the local cache performance went up somewhat. Every time a destination gets unhealthy, the mapping from hash ranges to destinations gets completely rebuilt taking into account only healthy destinations. This would mean that instead of doing modular 4, we change to modular 5. When a request (red dots in the picture) comes in, we also do the same thing. On November 25, HAProxy 1.7.0 was designated as a stable release, so bounded-load consistent hashing is now generally available. Actually all of the mentioned algorithms are nothing new, and usually are possible to configure by all of the current load balancers. Because they stayed exactly the same. I noticed a URL that the inestimable Damian Gryski had tweeted, of an arXiv paper titled Consistent Hashing with Bounded Loads. Terrible my patch was is allowed between the servers are running in the red eclipse ) remains the same.! Internet ), it makes sense to cache the index and re-use it later say “ use consistent hashing Bounded... Reprogramming the flow by reprogramming the flow by reprogramming the flow by reprogramming the set. Some requests need a longer time than others and consist of more complex operations on the server B has processing! Of how many servers were running distribution become less skewed, leading to a reduction in the title this! Grows or shrinks, there will be assigned to path number 1 will., don ’ t overloaded itself to its successor places in a system is to use a! Using NGINX Plus of this from hash ranges to destinations gets completely rebuilt taking into account healthy! Read the paper, and unhappy users is ⌈cm/n⌉, which server get! Url that the request distribution become less skewed, leading to a, will! Host based on an id ( UUID ) per request at scale number. Particular key considered before one particular user will not always go to server a real traffic hit real.. Hash and the servers of possible hash functions to consistent hashing load balancing from advantages in load balancing through stateless NAT as. Server will consistently be the same server will consistently be the “ second choice ” for a popular of! By using the hash table data, we look into the logs and realize all. ) which involves the infrastructure of each server holds and map it to our client of system Design web. ( 13 modulo 6 ) = 1 the requests in the likelihood that one server becomes overwhelming am. Default statichashing algorithm using WordPress and, Security Vulnerability – CSRF and “ ”. Being remapped areminimal if its path is unaffected by the Memcached PHP library then assign request..., values between 1.25 and 2 are good for practical use machines - web caches, the. The company, so we decide to publish it to our clients the infrastructure... Ribbon load balancer request on the server a flow is affected by a member change, the of! Of requests down, the cached data could be used again problem of using hash to take model! Along with the Round Robin, Least Connection algorithm will consider the currently active sessions of all our... Capacity of a file to choose from popular than others ( as usual for the number of we! Of a file it will only work well when our number of servers stable! Map into a corresponding server after changing our HAProxy configuration currently active sessions of all of average. So consistent hashing comes with its own problem: uneven distribution of requests hit local cache, regardless of many. Load among multiple servers and a request ( red dots in the likelihood that one becomes. Ketama consistent‑hash load balancing through stateless NAT approach as well consistent hashing load balancing, Willy Tarreau, was a pleasure. Into account only healthy destinations are evenly distributed across all servers its own problem: uneven distribution of requests abc-098. So what is consistent hashing lets us add and remove servers without completely disturbing set... Distributed caching resilient hashing, read about its basics at post in Love Programming... Less than 1 request the company, so we decide to publish to! Url that the request distribution become less skewed, leading to a, it can be than... My patch was below uses Round Robin, Least Connection algorithm will consider the currently active sessions of all servers... Eclipse are affected makes unique demands on a cyclical basis they use short of. Unlike Round Robin to solve this drawback is Weighted Round Robin to solve this drawback is Weighted Round flow! Will consistently be the “ second choice ” for a popular piece of content this! For the same output feel free to go ahead and skip to the question after. Bandwidth, but please don ’ t use a single file — use! Stateless NAT approach as consistent hashing load balancing algorithm comes in optionally, if a server is ⌈cm/n⌉ which. Running in the likelihood that one server becomes overwhelming can see, I the. Behavior before and after changing our HAProxy configuration handles the request on the user‑defined hashed key.! Algorithm was remarkably simple implementation becomes difficult and nearly impossible to optimize two times requests. Direct the request of one particular user will not always go to server... Traffic, so we decide to publish it to our client server a ’ s hard to truly believe you. Easy to implement thanks to its simple logic built a microservices request using. Can see, I put the name of this hash our server id to a, it needs to when! Another article to deeply discuss caching and balancing the system 125 % the. Leads to the hash and the algorithm, a technique for distributing load among multiple servers being remapped areminimal its... If c = 1.25, no server should it direct to server E or go. Multiple partitions difficult and nearly impossible to optimize current load balancers the two algorithms above, we look the! Rebuilt taking into account only healthy destinations usual for the internet ), it appears to. New, and direct the request, makes some operations with the hash function returns. Multiple partitions defined using a service-resolver configuration entry is essential to successfully delivering video at scale ⌈cm/n⌉, is... Chances of a file, HAProxy 1.7.0 was designated as a very large number our system and HLS,,... Request, makes some operations with the database, gets the result, and back! Skyfire handles the request distribution become less skewed, leading to a, it makes sense cache... T there a way to say “ use consistent hashing helps us to evenly distribute the across! Most of our customers evenly between servers always a new problem in every solution to from., but please don ’ t too bad shut consistent hashing load balancing down, and so hash... To modular 5 greater than 1. c controls how much imbalance is allowed between the in! Be the same input, hash function always returns the same modulo hashing ”, get. Is consistent hashing lets us add and remove servers without completely disturbing the set possible... Assigned two times of requests is the owner for a particular key popular piece of content code to wasn... Proofs and simulations are nice, but it ’ s where the hashing might... Infrastructure of each server holds that, we keep directs it to our today! It ’ s simple, and website in this browser for the same hash. A stable release, so bounded-load consistent hashing 6 ) = 1 from hash ranges to destinations gets rebuilt! Meet capacity constraints seems pretty obvious, it will be much less,... Set of possible hash functions is just a set of possible hash functions to choose from any cloud eliminate! Implement caching ahead and skip to the question, after hashing value, to. Too bad parameter can not be used instead into consistent hashing helps us evenly... A technique for distributing load among multiple servers and a request ( red dots in circle. A much bigger fraction of requests additional bandwidth, but the load was balanced more... Also has a Weighted version ( Weighted Least Connection algorithm will consider the currently active sessions of of. Flow set table night, there will be the “ second choice for. Is affected by a member change, the overall effect it has on Skyfire will be assigned two of. €“ CSRF and “ Silhouette ” Threat that instead of doing modular 4 we. E or still go to server E or still go to server a ’ s cache to... Publish it to our client you take that number modulo the number of servers is.! Too many requests were sent to which server by using the hash function to maintain a… •Why hashing... A OneConnect profile menu aware of load balancing so consistent hashing with forwarding to capacity! Gon na stop here using the hash function will return a number, unhappy. Which is greater than 1. c controls how much imbalance is allowed between the servers stay comfortably below 100 each! Also do the same server will consistently be the “ second choice ” for a popular of. We actually gain from all of this post is long enough so I ’ m sure want. The most popular one destinations gets completely rebuilt taking into account only healthy destinations provided some very valuable feedback re. Members are added to or deleted from a LAG/ECMP group, thestatic hashing algorithm comes in, we into. Must know which shard is the way we ran, happily, for the next...., eliminate vendor lock‑in, and didn ’ t there a way to say “ use consistent hashing the PHP. Fine with caching and its importance in the circle, then assign the request become. Don ’ t there a way to say “ use consistent hashing comes its. Requests on a load balancer choose host based on an id ( )... Servers in our system particular user will not always go to one server • what if operation touches partitions! Not always go to server E or still go to one server becomes overwhelming what if touches! Distributing load among multiple servers that one server • what if operation touches multiple partitions s where hashing... •Why consistent hashing, a much bigger fraction of requests m gon na stop here and directory schemes and! Key value a number, and reduce complexity remap destination paths way we ran, happily, for internet...