More Than a Rounding Error
March 1, 2019
The ONE reason to visit SentinelOne’s RSA 2019 booth S #1527
March 1, 2019
Moving data is one essential task for enterprises today, especially if you’re using lots of different systems across many locations. One product for this is IBM MQ, which helps you move data dependably with secure messaging. Deploying a highly available IBM MQ cluster in the cloud is not a straightforward task, and IBM provides many clustering configurations you can use and combine in various ways to achieve your high availability goals. It’s challenging to deploy a cluster like this in a way that takes advantage of cloud’s benefits, like multiple zone availability, load balancers and vertical scaling. But once you’ve got it set up, you can safely move, integrate and process data from within applications across your organization quickly and securely, with multiple petabytes of data processed.
We’ve heard that you want to know how to use a tool like IBM MQ as part of your Google Cloud Platform (GCP) deployment, specifically Compute Engine. We’re pleased to introduce this guide to how to deploy a highly available IBM MQ Queue Manager Cluster on Compute Engine with GlusterFS. GlusterFS is an open-source, scale-out storage system that works well for this purpose because it is designed for high-throughput storage shared between instances and requires little effort to set up.
Using queue managers to build the IBM MQ cluster
In this solution, we recommend combining queue manager clusters with multi-instance queue managers (instances of the same queue manager configured on different servers) to create a highly available, scalable IBM MQ deployment. Multi-instance queue managers run in an active/standby configuration, using a shared volume for configuration and state data. Clustered queue managers share configuration information using a network channel and can perform load balancing on incoming messages. However, the message state is not shared between the two queue managers.
By using both IBM MQ cluster deployment models at once, you can achieve redundancy at the queue manager level and then scale by distributing the load of the IBM MQ cluster across one or more queue managers.You can see the architecture of the deployment in this diagram:
