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In computing , load balancing improves the distribution of workloads across multiple computing resources, such as computers, a computer cluster , network links, central processing units , or disk drives. Using multiple components with load balancing instead of a single component may increase reliability and availability through redundancy. Load balancing usually involves dedicated software or hardware, such as a multilayer switch or a Domain Name System server process.
Load balancing differs from channel bonding in that load balancing divides traffic between network interfaces on a network socket OSI model layer 4 basis, while channel bonding implies a division of traffic between physical interfaces at a lower level, either per packet OSI model Layer 3 or on a data link OSI model Layer 2 basis with a protocol like shortest path bridging. One of the most commonly used applications of load balancing is to provide a single Internet service from multiple servers , sometimes known as a server farm.
An alternate method of load balancing, which does not require a dedicated software or hardware node, is called round robin DNS. In this technique, multiple IP addresses are associated with a single domain name ; clients are given IP in round robin fashion. IP is assigned to clients for a time quantum. Another more effective technique for load-balancing using DNS is to delegate www. This technique works particularly well where individual servers are spread geographically on the Internet.
However, the zone file for www. This way, when a server is down, its DNS will not respond and the web service does not receive any traffic. Furthermore, the quickest DNS response to the resolver is nearly always the one from the network's closest server, ensuring geo-sensitive load-balancing [ citation needed ]. A short TTL on the A-record helps to ensure traffic is quickly diverted when a server goes down. Consideration must be given the possibility that this technique may cause individual clients to switch between individual servers in mid-session.
Another approach to load balancing is to deliver a list of server IPs to the client, and then to have client randomly select the IP from the list on each connection. It has been claimed that client-side random load balancing tends to provide better load distribution than round-robin DNS; this has been attributed to caching issues with round-robin DNS, that in case of large DNS caching servers, tend to skew the distribution for round-robin DNS, while client-side random selection remains unaffected regardless of DNS caching.
With this approach, the method of delivery of list of IPs to the client can vary, and may be implemented as a DNS list delivered to all the clients without any round-robin , or via hardcoding it to the list.
If a "smart client" is used, detecting that randomly selected server is down and connecting randomly again, it also provides fault tolerance. For Internet services, server-side load balancer is usually a software program that is listening on the port where external clients connect to access services.
The load balancer forwards requests to one of the "backend" servers, which usually replies to the load balancer. This allows the load balancer to reply to the client without the client ever knowing about the internal separation of functions.
It also prevents clients from contacting back-end servers directly, which may have security benefits by hiding the structure of the internal network and preventing attacks on the kernel's network stack or unrelated services running on other ports. Some load balancers provide a mechanism for doing something special in the event that all backend servers are unavailable.
This might include forwarding to a backup load balancer, or displaying a message regarding the outage. It is also important that the load balancer itself does not become a single point of failure. Usually load balancers are implemented in high-availability pairs which may also replicate session persistence data if required by the specific application.
Numerous scheduling algorithms , also called load-balancing methods, are used by load balancers to determine which back-end server to send a request to. An important issue when operating a load-balanced service is how to handle information that must be kept across the multiple requests in a user's session. If this information is stored locally on one backend server, then subsequent requests going to different backend servers would not be able to find it.
This might be cached information that can be recomputed, in which case load-balancing a request to a different backend server just introduces a performance issue. Ideally the cluster of servers behind the load balancer should be session-aware, so that if a client connects to any backend server at any time the user experience is unaffected. This is usually achieved with a shared database or an in-memory session database, for example Memcached.
One basic solution to the session data issue is to send all requests in a user session consistently to the same backend server. This is known as persistence or stickiness. A significant downside to this technique is its lack of automatic failover: The same problem is usually relevant to central database servers; even if web servers are "stateless" and not "sticky", the central database is see below. Assignment to a particular server might be based on a username, client IP address , or be random.
Because of changes of the client's perceived address resulting from DHCP , network address translation , and web proxies this method may be unreliable. Random assignments must be remembered by the load balancer, which creates a burden on storage. If the load balancer is replaced or fails, this information may be lost, and assignments may need to be deleted after a timeout period or during periods of high load to avoid exceeding the space available for the assignment table.
The random assignment method also requires that clients maintain some state, which can be a problem, for example when a web browser has disabled storage of cookies. Sophisticated load balancers use multiple persistence techniques to avoid some of the shortcomings of any one method.
Another solution is to keep the per-session data in a database. Generally this is bad for performance because it increases the load on the database: To prevent a database from becoming a single point of failure , and to improve scalability , the database is often replicated across multiple machines, and load balancing is used to spread the query load across those replicas.
All servers in a web farm store their session data on State Server and any server in the farm can retrieve the data. In the very common case where the client is a web browser, a simple but efficient approach is to store the per-session data in the browser itself. One way to achieve this is to use a browser cookie , suitably time-stamped and encrypted. Another is URL rewriting. Storing session data on the client is generally the preferred solution: However, this method of state-data handling is poorly suited to some complex business logic scenarios, where session state payload is big and recomputing it with every request on a server is not feasible.
URL rewriting has major security issues, because the end-user can easily alter the submitted URL and thus change session streams. Yet another solution to storing persistent data is to associate a name with each block of data, and use a distributed hash table to pseudo-randomly assign that name to one of the available servers, and then store that block of data in the assigned server.
Hardware and software load balancers may have a variety of special features. The fundamental feature of a load balancer is to be able to distribute incoming requests over a number of backend servers in the cluster according to a scheduling algorithm. Most of the following features are vendor specific:. Load balancing can be useful in applications with redundant communications links.
For example, a company may have multiple Internet connections ensuring network access if one of the connections fails. A failover arrangement would mean that one link is designated for normal use, while the second link is used only if the primary link fails. Using load balancing, both links can be in use all the time. A device or program monitors the availability of all links and selects the path for sending packets. The use of multiple links simultaneously increases the available bandwidth.
Many telecommunications companies have multiple routes through their networks or to external networks. They use sophisticated load balancing to shift traffic from one path to another to avoid network congestion on any particular link, and sometimes to minimize the cost of transit across external networks or improve network reliability.
Another way of using load balancing is in network monitoring activities. Load balancers can be used to split huge data flows into several sub-flows and use several network analyzers, each reading a part of the original data. This is very useful for monitoring fast networks like 10GbE or STM64, where complex processing of the data may not be possible at wire speed.
Load balancing is widely used in datacenter networks to distribute traffic across many existing paths between any two servers. In general, load balancing in datacenter networks can be classified as either static or dynamic. Static load balancing distributes traffic by computing a hash of the source and destination addresses and port numbers of traffic flows and using it to determine how flows are assigned to one of the existing paths.
Dynamic load balancing assigns traffic flows to paths by monitoring bandwidth utilization of different paths. Dynamic assignment can also be proactive or reactive. In the former case, the assignment is fixed once made, while in the latter the network logic keeps monitoring available paths and shifts flows across them as network utilization changes with arrival of new flows or completion of existing ones. A comprehensive overview of load balancing in datacenter networks has been made available.
Load balancing is often used to implement failover —the continuation of a service after the failure of one or more of its components. The components are monitored continually e. When a component comes back online, the load balancer begins to route traffic to it again.
This can be much less expensive and more flexible than failover approaches where each single live component is paired with a single backup component that takes over in the event of a failure dual modular redundancy. Some types of RAID systems can also utilize hot spare for a similar effect. From Wikipedia, the free encyclopedia. This article needs additional citations for verification.
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