What is the difference between load balancer and cluster

Random load balancing is recommended only for homogeneous cluster deployments, where each server instance runs on a similarly configured machine. A random allocation of requests does not allow for differences in processing power among the machines upon which server instances run. If a machine hosting servers in a cluster has significantly less processing power than other machines in the cluster, random load balancing will give the less powerful machine as many requests as it gives more powerful machines.

Random load balancing distributes requests evenly across server instances in the cluster, increasingly so as the cumulative number of requests increases. Over a small number of requests the load may not be balanced exactly evenly. Disadvantages of random load balancing include the slight processing overhead incurred by generating a random number for each request, and the possibility that the load may not be evenly balanced over a small number of requests.

WebLogic Server provides three load balancing algorithms for RMI objects that provide server affinity. Server affinity turns off load balancing for external client connections: instead, the client considers its existing connections to WebLogic server instances when choosing the server instance on which to access an object. If an object is configured for server affinity, the client-side stub attempts to choose a server instance to which it is already connected, and continues to use the same server instance for method calls.

All stubs on that client attempt to use that server instance. If the server instance becomes unavailable, the stubs fail over, if possible, to a server instance to which the client is already connected. The purpose of server affinity is to minimize the number IP sockets opened between external Java clients and server instances in a cluster.

With server affinity algorithms, the less costly server-to-server connections are still load-balanced according to the configured load balancing algorithm—load balancing is disabled only for external client connections. Server affinity is used in combination with one of the standard load balancing methods: round-robin, weight-based, or random:. A client can request an initial context from a particular server instance in the cluster, or from the cluster by specifying the cluster address in the URL.

The connection process varies, depending on how the context is obtained:. Otherwise, all remote calls will be authenticated. WebLogic Server has three load balancing algorithms that provide server affinity:. The server affinity algorithms consider existing connections between an external Java client and server instances in balancing the client load among WebLogic server instances. Server affinity:. The following examples illustrate the effect of server affinity under a variety of circumstances.

In each example, the objects deployed are configured for round-robin-affinity. In this example, the client obtains context from the cluster. Lookups on the context and object calls stick to a single connection. Requests for new initial context are load balanced on a round-robin basis. This example illustrates the effect that server affinity has on object failover. When a Managed Server goes down, the client fails over to another Managed Server to which it has a connection.

Example 3—Server affinity and server-to-server connections. This example illustrates the fact that server affinity does not affect the connections between server instances. Parameter-based routing allows you to control load balancing behavior at a lower level. Any clustered object can be assigned a CallRouter. This is a class that is called before each invocation with the parameters of the call. The CallRouter is free to examine the parameters and return the name server to which the call should be routed.

In most cases, it is more efficient to use a replica that is collocated with the stub itself, rather than using a replica that resides on a remote server. The following figure illustrates this. In this example, a client connects to a servlet hosted by the first WebLogic Server instance in the cluster. In response to client activity, the servlet obtains a replica-aware stub for Object A. It is more efficient to use the local copy, because doing so avoids the network overhead of establishing peer connections to other servers in the cluster.

This optimization is often overlooked when planning WebLogic Server clusters. The collocation optimization is also frequently confusing for administrators or developers who expect or require load balancing on each method call. If your Web application is deployed to a single cluster, the collocation optimization overrides any load balancing logic inherent in the replica-aware stub. If you require load balancing on each method call to a clustered object, see Recommended Multi-Tier Architecture for information about how to plan your WebLogic Server cluster accordingly.

As an extension to the basic collocation strategy, WebLogic Server attempts to use collocated clustered objects that are enlisted as part of the same transaction. When a client creates a UserTransaction object, WebLogic Server attempts to use object replicas that are collocated with the transaction. This optimization is depicted in the figure below. In this example, a client attaches to he first WebLogic Server instance in the cluster and obtains a UserTransaction object.

After beginning a new transaction, the client looks up Objects A and B to do the work of the transaction. In this situation WebLogic Server always attempts to use replicas of A and B that reside on the same server as the UserTransaction object, regardless of the load balancing strategies in the stubs for A and B. This transactional collocation strategy is even more important than the basic optimization described in Optimization for Collocated Objects.

Share on facebook. Share on twitter. Share on google. Share on linkedin. Clustering , Load Balancing. Corey Geer. Share this post. Share on pinterest. Rationale though is making Load Balancing address scalability while Failover Clustering address high availability. Load Balancing is all about improvising performance scale while Failover Clustering is improvising uptimes mitigating system failures. Windows recommendation is not to mix both, i. Setting up load balancing is simple — you need couple of machines connected to a common network and an additional IP where clients would connect to.

This Virtual IP where the requests are made by clients, is in turn is used for Load Balancing nodes that part of this cluster load balancing cluster. Setting up failover clustering on the other hand is little complex.

You need 2 networks a public and private hear beat , a shared drive called Quorum , and an additional Public IP in addition to minimum — 2 public and 2 private IPs that 2 systems will have.

Remember, creating a failover cluster at Windows level is a primary requirement to build a failover SQL Server cluster. Reason to create a Windows level cluster is install required cluster services and create cluster groups logical collection of nodes. You can select a cluster group obviously at least 2 nodes should be part of this group and configure SQL Server Cluster or anything else on top it. I have also come across quite a few implementations using Starwind or similar tools to create these shared iSCSI targets in form of virtual disks.

Enterprise Architect View all posts by nirajrules. Good article , but i have a question what are different intents.