Posts Tagged ‘node’

Load Balancing Ansible Tower Using NSX

February 1, 2020

Disclamer: this configuration is not validated by either VMware or Red Hat. Make sure it is applicable to your use case and thoroughly test before implementing in production.


If you landed on this page I trust you already know what Ansible is. It’s a great configuration management tool centred around using YAML to describe the desired state configuration of your various infrastructure components. This desired state is captured in what Ansible calls playbooks, which once written, can then be used in a repeatable way to deploy brand new components or enforce configuration on already deployed ones.

Ansible can be installed and used from CLI, which is usually a good starting point. If you have multiple people using Ansible in your organization, you can also deploy AWX. It’s a free GUI add-on to Ansible, which makes managing concurrent user access to Ansible easier, by adding projects, schedules and credentials management. On top of that there is Ansible Tower. Ansible Tower is a paid version of AWX and gives you additional enterprise features and services like clustering, product support, validated upgrade paths, etc. In this article we will be focusing on Ansible Tower version of the product.

Also worth mentioning that this configuration will be based on Ansible Tower cluster feature, which lets you run all nodes as active/active. Prior to version 3.1 it was called redundancy and worked only in active/passive mode. Redundancy feature is deprecated and is outside the scope of this blog post.


Deploying multiple Ansible Tower nodes in a cluster already gives you redundancy. If one of the nodes fails you can connect to another node, by just changing your browser URL. The benefit of having a load balancer is that you have one URL you can hit and if a node goes down, such situation is handled by load balancer automatically.

In this example we will be deploying a VMware NSX load-balancer in the following topology:


Deploying an NSX load-balancer for HTTPS port 443 is simple, you can find numerous examples of how to create application profiles, monitors, pools and VIPs in official VMware documentation or out on the Internet. But with Ansible there’s one catch. If you try to use the default HTTPS monitor that NSX load balancer comes with, you will find HTTP 400 code in Ansible nginx logs: - - [20/Jan/2020:04:50:19 +0000] "GET / HTTP/1.0" 400 3786 "-" "-" "-" - - [20/Jan/2020:04:50:24 +0000] "GET / HTTP/1.0" 400 3786 "-" "-" "-" - - [20/Jan/2020:04:50:29 +0000] "GET / HTTP/1.0" 400 3786 "-" "-" "-"

And an error in NSX load balancer health check:

As it turns out, when you make a HTTP request to Ansible Tower, specifying HTTP “Host” header is a requirement. Host header simply contains the hostname of the server you’re making a request to. Browsers add this header automatically, that’s why you’re not going to see any errors, when accessing Ansible Tower Using Firefox or Chrome. But NSX doesn’t add this header to the monitor checks by default, which makes Ansible Tower upset.

Here is the trick you need to do to make Tower happy:

Now nginx logs show success code 200: - - [21/Jan/2020:22:54:42 +0000] "GET / HTTP/1.0" 200 11337 "-" "-" "-" - - [21/Jan/2020:22:54:47 +0000] "GET / HTTP/1.0" 200 11337 "-" "-" "-" - - [21/Jan/2020:22:54:52 +0000] "GET / HTTP/1.0" 200 11337 "-" "-" "-"

Load balancer health check is successful:

And pool members are up and reachable:

Note: technically the host header should contain the hostname of the Tower node we’re making a health check on. But since NSX monitor is configured per pool and not per pool member, we have to use a fake hostname “” as a workaround. When I was testing it, Tower didn’t complain.


Even though I said that the rest of the load-balancer configuration is standard, I still think having screenshots for reference is helpful if you need to validate configuration. So find the full list of settings below.

Screenshot 1: Application Profile

Screenshot 2: Service Monitor

Screenshot 3: Pool

Screenshot 4: Virtual Server

EMC Isilon Overview

February 20, 2014

isilon_logo_188x110OneFS Overview

EMC Isilon OneFS is a storage OS which was built from the ground up as a clustered system.

NetApp’s Clustered ONTAP for example has evolved from being an OS for HA-pair of storage controllers to a clustered system as a result of integration with Spinnaker intellectual property. It’s not necessarily bad, because cDOT shows better performance on SPECsfs2008 than Isilon, but these systems still have two core architectural differences:

1. Isilon doesn’t have RAIDs and complexities associated with them. You don’t choose RAID protection level. You don’t need to think about RAID groups and even load distribution between them. You don’t even have spare drives per se.

2. All data on Isilon system is kept on one volume, which is a one big distributed file system. cDOT use concept of infinite volumes, but bear in mind that each NetApp filer has it’s own file system beneath. If you have 24 NetApp nodes in a cluster, then you have 24 underlying file systems, even though they are viewed as a whole from the client standpoint.

This makes Isilon very easy to configure and operate. But its simplicity comes at a price of flexibility. Isilon web interface has few options to configure and not very feature rich.

Isilon Nodes and Networking

In a nutshell Isilon is a collection of a certain number of nodes connected via 20Gb/s DDR InfiniBand back-end network and either 1GB/s or 10GB/s front-end network for client connections. There are three types of Isilon nodes S-Series (SAS + SSD drives) for transactional random access I/O, X-Series (SATA + SSD drives) for high throughput applications and NL-series (SATA drives) for archival or not frequently used data.

If you choose to have two IB switches at the back-end, then you’ll have three subnets configured for internal network: int-a, int-b and failover. You can think of a failover network as a virtual network in front of int-a and int-b. So when the packet comes to failover network IP address, the actual IB interface that receives the packet is chosen dynamically. That helps to load-balance the traffic between two IB switches and makes this set up an active/active network.


On the front-end you can have as many subnets as you like. Subnets are split between pools of IP addresses. And you can add particular node interfaces to the pool. Each pool can have its own SmartConnect zone configured. SmartConnect is a way to load-balance connections between the nodes. Basically SmartConnect is a DNS server which runs on the Isilon side. You can have one SmartConnect service on a subnet level. And one SmartConnect zone (which is simply a domain) on each of the subnet pools. To set up SmartConnect you’ll need to assign an IP address to the SmartConnect service and set a SmartConnect zone name on a pool level. Then you create an “A” record on DNS for the SmartConnect service IP address and delegate SmartConnect DNS zone to this IP. That way each time you refer to the SmartConnect zone to get access to a file share you’ll be redirected to dynamically picked up node from the pool.


Each type of node is automatically assigned to what is called a “Node Pool”. Nodes are grouped to the same pool if they are of the same series, have the same amount of memory and disks of the same type and size. Node Pool level is one of the spots where you can configure protection level. We’ll talk about that later. Node Pools are grouped within Tiers. So you can group NL node pool with 1TB drives and NL node pool with 3TB drives into an archive tier if you wish. And then you have File Pool Policies which you can use to manage placement of files within the cluster. For example, you can redirect files with specific extension or file size or last access time to be saved on a specific node pool or tier. File pool policies also allow you to configure data protection and override the default node pool protection setting.

SmartPools is a concept that Isilon use to name Tier/Node Pool/File Pool Policy approach. File placement is not applied automatically, otherwise it would cause high I/O overhead. It’s implemented as a job on the cluster instead which runs at 22:00 every day by default.

Data Layout and Protection

Instead of using RAIDs, Isilon uses FEC (Forward Error Correction) and more specifically a Reed-Solomon algorithm to protect data on a cluster. It’s similar to RAID5 in how it generates a protection block (or blocks) for each stripe. But it happens on a software level, instead of hardware as in storage arrays. So when a file comes in to a node, Isilon splits the file in stripe units of 128KB each, generates one FEC protection unit and distributes all of them between the nodes using back-end network. This is what is called “+1” protection level, where Isilon can sustain one disk or one node failure. Then you have “+2”, “+3” and “+4”. In “+4” you have four FECs per stripe and can sustain four disk or node failures. Note however that there is a rule that the number of data stripe units in a stripe has to be greater than number of FEC units. So the minimum requirement for “+4” protection level is 9 nodes in a cluster.


The second option is to use mirroring. You can have from 2x to 8x mirrors of your data. And the third option is “+2:1” and “+3:1” protection levels. These protection levels let you balance between the data protection and amount of the FEC overhead. For example “+2:1” setting compared to “+2” can sustain two drive failures or one node failure, instead of two node failure protection that “+2” offers. And it makes sense, since simultaneous two node failure is unlikely to happen. There is also a difference in how the data is laid out. In “+2” for each stripe Isilon uses one disk on each node and in “+2:1” it uses two disks on each node. And first FEC in this case goes to first subset of disks and second goes to second.

One benefit of not having RAID is that you can set protection level with folder or even file granularity. Which is impossible with conventional RAIDs. And what’s quite handy, you can change protection levels without recreation of storage volumes, as you might have to do while transitioning between some of the RAID levels. When you change protection level for any of the targets, Isilon creates a low priority job which redistributes data within the cluster.

Storwize V7000 with vSphere 5 storage configuration

December 1, 2012

storwizeInformation on how to configure Storwize for optimal performance is very scarce. I’ll try to build some understanding of it from bits an pieces gathered throughout the Internet and redbooks.

Barry Whyte gave many insights on Storwize internals in his blog. Particularly his “Configuring IBM Storwize V7000 and SVC for Optimal Performance” series of posts. I’ll quote him here. The main Storwize redbook “Implementing the IBM Storwize V7000 V6.3” is mostly an administration guide and gives no useful information on the topic. I find “SAN Volume Controller Best Practices and Performance Guidelines” way more helpful (Storwize firmware is built on SVC code).

Total Number of MDisks

That’s what Barry says:

… At the heart of each V7000 controller canister is an Intel Jasper Forrest (Sandy Bridge) based quad core CPU. … When we added the tried and trusted (SSA) DS8000 RAID functionality in 2010 (6.1.0) we therefore assigned RAID processing on a per mdisk basis to a single core. That means you need at least 4 arrays per V7000 to get maximal CPU core performance. …

Number of MDisks per Storage Pool

SVC Redbook:

The capability to stripe across disk arrays is the single most important performance advantage of the SVC; however, striping across more arrays is not necessarily better. The objective here is to only add as many arrays to a single Storage Pool as required to meet the performance objectives.

If the Storage Pool is already meeting its performance objectives, we recommend that, in most cases, you add the new MDisks to new Storage Pools rather than add the new MDisks to existing Storage Pools.

Table 5-1 shows the recommended number of arrays per Storage Pool that is appropriate for general cases.

Controller type       Arrays per Storage Pool
DS4000/DS5000         4 - 24
DS6000/DS8000         4 - 12
IBM Storwise V7000    4 - 12

The development recommendations for Storwize V7000 are summarized below:

  • One MDisk group per storage subsystem
  • One MDisk group per RAID array type (RAID 5 versus RAID 10)
  • One MDisk and MDisk group per disk type (10K versus 15K RPM, or 146 GB versus 300 GB)

There are situations where multiple MDisk groups are desirable:

  • Workload isolation
  • Short-stroking a production MDisk group
  • Managing different workloads in different groups

We recommend that you have at least two MDisk groups, one for key applications, another for everything else.

Number of LUNs per Storage Pool

SVC Redbook:

We generally recommend that you configure LUNs to use the entire array, which is especially true for midrange storage subsystems where multiple LUNs configured to an array have shown to result in a significant performance degradation. The performance degradation is attributed mainly to smaller cache sizes and the inefficient use of available cache, defeating the subsystem’s ability to perform “full stride writes” for Redundant Array of Independent Disks 5 (RAID 5) arrays. Additionally, I/O queues for multiple LUNs directed at the same array can have a tendency to overdrive the array.

Table 5-2 provides our recommended guidelines for array provisioning on IBM storage subsystems.

Controller type                     LUNs per array
IBM System Storage DS4000/DS5000    1
IBM System Storage DS6000/DS8000    1 - 2
IBM Storwize V7000                  1

General considerations

vsphere5-logoLets take a look at vSphere use case scenario on top of Storwize with 16 x 600GB SAS drives in control enclosure and 10 x 2TB NL-SAS in extension enclosure (our personal case).

First of all we need to decide how many arrays we need. Do we have different workloads? No. All storage will be assigned to virtual machines which have in general the same random read/write access pattern. Do we need to isolate workloads? Probably yes, it’s generally a good idea to separate highly critical production VMs from everything else. Do we have different drive types? Yes. Obviously we don’t want to mix drive types in one RAID. Are we going to make different RAID types? Again, yes. RAID 10 is appropriate on SAS and RAID 5 on NL-SAS. So two MDisks – one RAID 10 on SAS and one RAID 5 on NL-SAS would be enough. Storwize nodes have 4 cores each. It may seem that you would benefit from 4 MDisks, but in fact you won’t. Here what Barry says:

In the case where you only have 1 or 2 HDD arrays, then the core stuff doesn’t really come into play. Its only when you get to larger systems, where you are driving more I/O than a single RAID core can handle that you need to spread them.

This is also true if you are running all SSD arrays, so 24x SSD would be best split into 4 arrays to get maximum IOPs, whereas 24x HDD are not going to saturate a single core, so (if you could create a 23+P! [ you can’t 15+P is largest we support ] then it would perform as well as 2x 11+P etc

To storage pools. In our example we have two MDisks, so you simply make two storage pools. In future if you hit performance limit, you create additional MDisks and then you have two options. If each MDisk separately is able to sustain your performance requirements, you make additional storage pools and redistribute workload between them. If you have huge load on storage and even redistribution of VMs between two arrays doesn’t help, then you better combine two MDisks of each type in its own storage pool for striping between MDisks.

Same story for number of LUNs. IBM recommends one to one LUN to MDisk relationship. But read carefully. Recommendation comes from the fact that different workloads can clash and degrade array performance. But if we have generally the same I/O patterns coming to the array it’s safe to make several LUNs on it, until latency is in the acceptable range. Moreover, when it comes to vSphere and VMFS, it’s beneficial to have at least two volumes in terms of manageability. With several LUNs you will at least have an ability to move VMs between LUNs for reconfiguration purposes. Also keep in mind that ESXi 5 hypervisor limit each host to storage queue of depth 32 per LUN. It means that if you have one big LUN and many VMs running on the host, you can quickly reach queue limit. On the other hand do not create too many LUNs or you will oversubscribe storage processors (SPs).

Sample configuration

IBM recommends constructing both RAID 10 and RAID 5 arrays from 8 drives + 1 spare drive. But since we have 16 SAS and 10 NL-SAS I would launch CLI and create two arrays: one 14 drives + 2 spares RAID 10 and one 8 drives + 2 spares RAID 5 (or 9 drives + 1 spare, but it’s not a good idea to create RAID with uneven number of drives). Each RAID in its own pool. Several LUNs in each pool. I would go for 2TB LUNs.

General view of IBM DB2 architecture

December 16, 2011

No one would argue that DB2 is one of the leading (R)DBMS products in the market. As any other powerful software product it has complex architecture. Here I’d like to introduce some basic terminology of it in series of posts because sometimes this terminology can become somewhat vague.

Lets kick off by explaining some general view of DB2. Each IBM DB2 installation has following basic levels of architecture:

  • Instance can be understood as a completely independent environment with it’s own security configuration, resource allocation and contains databases and partitions isolated from all other instances. Each instance has its own system processes which manage data. Instance may contain several databases.
  • Database is a most familiar term. It’s a logical unit which holds your data. It has complicated structure which we will explain later on. Database can reside in one or more partitions.
  • Partitions (or nodes) is a way of creating DB2 database cluster for the sake of higher performance. You can split your database onto several servers where each of them will have their own chunk of data. Since more servers means more CPU cores, memory and disk I/O it’s a natural way of scaling DB2. Particular storage resource is called Container.
  • Database is then mapped onto several Tablespaces. Tablespace allows you to manage how database tables are held on your storage resources. For example you can hold your data in files as well as on raw hard drives (which is faster). In addition it’s possible to put frequently used data on SAS hard drives and rarely used on SATA by means of creating two different tablespaces. On top of that you can set up a different page size for each of your tablespaces.

Here is the basic idea of what DB2 represents from the view of systems administrator. To better understand a folding of different levels please refer to this well-known among dbtwoers picture: link.