Monthly Archives: February 2018

My thoughts on Citrix buying Cedexis and what it is?

Earlier today Citrix announced publicly that they have bought the company Cedexis. (If you didn’t catch the news you can read the official blogpost here –>

Being the tech-curious mind that I am, and started to read through the official blogpost didn’t give me any clarity in what kind of value it would actually bring to Citrix. Also, I haven’t heard about the company before (other than some on social media from time-to-time, so I started to do some research) so therefore I decided to take a closer look and how Citrix can benefit from it.

Looking into the company I noticed that they have a set of products which make up the core which is called Cedexis ADP (Application Delivery Platform) which is actually aimed at making more intelligent load balancing using a combination of (Real-user monitoring & synthetic monitoring) to make the correct decision on where to route the data.


The platform is split into smaller parts where the core is three applications.

Radar: Is a product which contains and gathers real-user telemetry from thousands of users worldwide (you can see some of the interesting statistics here so with this we have detailed mappings on outages and response times and such. This is using a simple JavaScript script embedded within a content page or application provider’s pages to collect information about the performance and availability of a data center or delivery platform. (You can also access some nifty reports here as well à

Sonar: is a live-ness check service that can be used to monitor web-based services for availability. Sonar works by making HTTP or HTTPS requests from multiple points-of-presence around the world to a URL, Sonar checks are performed from multiple test locations from around the world.

Openmix: a SaaS Global Load Balancing which uses information from for instance Radar to consider real-time data feeds of end-user telemetry, and server or application monitoring data from Sonar to do Intelligent Global Load Balancing. Using all these different tools we can also combine this with other data such as Cost/Performance and define our own rating on a service if we for instance have a service available on multiple locations/platforms. The cool thing about Openmix being a cloud service and all is that Is available via DNS and HTTP, example here à

Fusion: In addition to Radar and Sonar data, Openmix can use 3rd party data as part of its decision criteria, which can integrate an existing synthetic monitoring service you already use. Or make cost-based decisions using usage data from a CDN provider. Here is a picture of the supported integrations that Fusion has which can be used to determine the best path.


There are also some new integrations such as Datadog, which also allows us to do for instance more efficient routing application logic based upon Datadog alerts.

So, looking at the products we can see that Cedexis have multiple tools to determine the optimal path, including the use of real-time user information and synthetic testing combined with third party integrations using custom metrics also a global SaaS load balancing service. For instance if we have a service which is available in multiple locations on multiple cloud providers, how can we ensure that an end-user is directed to the optimal route? We have multiple logic such as Radar (How is the network performing or CDN where the content is served from? ) and Sonar: (what is the RTT of the application from the ongoing test?) and also information from Fusion(New relic integration for instance APM which shows that Service Y is performing slow because of DB errors) and deduct from that information the correct path. However, Cedexis is missing the product to handle the actually load balancing in between the end-users and the backend services and is depedant on someone else to actually do the local load balancing and handle SSL traffic. While NetScaler on the other hand is missing the products to do more intelligent load balancing based upon real user telemetry, instead of just doing health-checks to the backend web server or doing GSLB based upon user proximity or such.

I can see the value of integrating the Cedexis platform into the NetScaler portfolio seeing that it can make it a much more powerful smart application delivery system. So, this is just my personal idea on how the portfolio could look like from an integrated solution. We could have NetScaler MAS feeding fusion using Web Analytics for instance and also seeing the performance usage on the NetScaler’s) which will then make it easier for Openmix to make the decision if the end-users should be load balanced to region X or Y based upon the weight that was defined on the application or service.


So just some intial thoughts on the Cedexis platform. Looking forward to try the platform and testing it out in real-scenario and what plans Citrix have for the platform moving forward.

Cloud Wars – IBM vs Microsoft vs Google vs Amazon IaaS

In my previous blog post I did a short overview of the different cloud vendors, a bit about their focus areas and also a bit about strengths and weaknesses. The blogpost can be found here –> .In this post I want to focus more on IaaS and the offerings surrounding it, first I want describe a bit about each vendor and then ill go into a bit more comparison and also include the price/performance factor here as well and end it with some focus on automation functionality and additional services.

As mentioned in my previous blogpost, IBM with its Softlayer capabilities has had extremely focus on bare-metal, with the addition on traditional IaaS and also with the extended partnership with VMware, they can also provide vCloud Foundation package (which also is a prerequisite package for VMware HCX) or just plain ESXi with vCenter deployment. On the bare-metal options we can choose between hourly or monthly pre-configured servers or customize with single to quad processing solutions that range from 4 to 72 cores. We can also order bare-metal servers with dedicated GPU offerings such as K2, K80, M60, P100). One of the cool features in terms of pure IaaS is that they offer pure Block storage as an option as well, using iSCSI or just plain file storage using NFS which also is an option. In terms of scale ability they can only offer up to 56 cores and 242 GB RAM for a single virtual machine which is a lot smaller then most offerings in Azure, Google and AWS. IBM like AWS and Azure also offers pre defined instances sizes which can be used and when setting up an instance you can also define what kind of network connectivity you want to have, by default you get 100 mbps uplink and private connectivity which is free, but if you want to up it to 1 GB you need to pay a cost. The main issue is that of all much of the concepts such as availability zones and other options for HA is not an option in IBM compared to GCP, AWS and Azure.

In terms of Automation, IBM has a feature called Cloud Schematics which is natively based upon Terraform, so it is basically wrapping REST API calls using a IBM Provider in Terraform, we also have the ability to run provision scripts which can be run at boot as part of a deployment. One of the things I feel is missing on IBM when it comes to automation is the ability to provider more overall system management capability such as Azure automation or AWS systems manager.

Google compared to the others have the most simplified deployment of virtual machines. Also they are the only vendor that has the option to defined custom instance sizes (for a bit higher prices of course) and also the flexibility when it comes to GPU flexibility for instance we can add GPU instances to any type of instance and also when it comes to disk type and sizes.

With Automation, Google has an API framework called Google Cloud Deployment Manager, which uses a YAML based syntax, but can also be using providers from Terraform or Puppet to do the deployment as well. Google also has the option do run start-up scripts on each virtual machine which allows for scripting of software and services inside the virtual machines. Google provides up to 96 vCPU and 1433 GB of memory on their largest instances. They however do not have any form of bare metal options, compared to IBM but that is not their focus either but like AWS, Google has gone into a partnership with Nutanix on a Hybric Cloud model which is going to be interesting to see how it turns out. Another cool thing about Google is that they provide live migrations of instances as default to handle maintance updates on their infrastructure.

For deployment of redudant solutions you need to be able to deploy instances across multiple zones within a region (Which is a simliar setup as Amazon Web Services do and Azure does with Availability Zones)

From a management perspetive, Google has been really good at developing their Cloud Shell solutions which allows for easy access to virtual instances directly from their browser and also allows for simple access with auto inserting the SSH key as part of the setup. One of the coolest things about Google is their core infrastructure and the network backbone which is called Andromeda which now has allowed them to provide low latency high bandwidth connections on east-west traffic. Also that they SDN is also worldwide meaning that if you create a virtual network by default it will be available on all the different regions (where different subnets are placed within each region  but are all interconnected)

Microsoft has also been doing a lot of work recently and investing heavily into new options such as new GPU offerings with the P100 and P40 cards but also with the introductions of availability zones (Still in preview for most services) which now allows for a great level of redundancy which is now pretty similar to Zones on GCP and AWS. Microsoft has also introduced loads of different new instances types with the burstable compute (B-series) and also now with the introduction of GA on Accelerated networking which allows for SRV-IO based network deployment of instances in Azure.

From a management perspective Microsoft has been doing alot around regular operations, such as with Log Analytics which can now do patch management and provide multiple pieces of monitoring across different platforms and also integrating different PaaS serivces to allow for a single hub to do monitoring across most of the services. Also with simple EDI based tools such as Logic Apps and Azure Automation allows us to setup simple and down to more complex automation jobs to do automated deployments and start/stop virtual instanced based upon a trigger or schedule. Also that they provide alot more tools when it come to migration and backup tools compared to the other vendors, with Azure Migrate and Azure Site Recovery.

Also Microsoft has been doing alot of investment into their Cloud shell solution as well which allows us to run az cli (bash based) and Azure PowerShell cmdlets directly from the browser. (As as of t01.02 they now also support Ansible directly from the cloud shell interface)

One of the issues with Azure from an IaaS perspective is the lacking flexibility to mixing like GPU cards with different instances, scaleable IOPS together with disk size. Also Microsft is focusing alot on building partners in the ecosystem to support automation and have been doing alot when it comes to Terraform which now covers alot of the resources in Azure directly.

When it comes to IaaS, Amazon provides most of the services both when it comes to bare-metal(coming) and support with VMware. Also different options depending on if needed reserved instances or just need to get reserve capacity or godzilla virtual machines. They also provide different storage options and scale options on IOPS depending on the size of the storage. Now with the upcoming support with VMware will also provide a whole new level of infrastructure solutions (the service is now available but still limited to certain regions in the US)

AWS also provides multiple management tools to make things easier such as AWS systems manager (which can also target on-premises virtual machines), and they even provide their own AWS Managed Services where they manage the IaaS solutions for you . AWS also has a service called OpsWorks which provides automation solution based upon Puppet and Chef as a managed service which can be then used to deliver configuration management against your own enviroment in AWS. AWS also has CloudWatch and CloudTrail to track events, logs and activity and API usage across AWS subscriptions.

AWS also has multiple options when it comes to GPU offerings such as P2 and G2 series which comes with a dedicated GPU card, or use the flexible GPU which is a software-defined GPU offering which allows us to add a GPU card to almost any type of instance.

Now the fun part is that most providers are now delivering more and more services to help with automation and system management, such as managed container engine cluster and also different advisor roles which can detect cost or security issues. This can be to check for best-pratices according to the cloud provider.

Now the interesting part is mainly around the container solutions that most providers are now fighting about. Both Microsoft and AWS have their own Container instance solution, where you just provision a container based upon an image and don’t have to worry about the infrastructure beneath (AWS Fargate and Azure Container Instance) and both of them also provide other container solutions such as Amazon Container Engine and Azure Container Engine. The fun part is that all 4 providers supports Kubernetes as the container orchestration engine and have supported features to build upon it, this can be a container registry solution or CI/CD solutions.

Technical Comparison: So the intention here is to have a short table to compare some of the different infrastrucutre services from each vendor, it does not measure the quality of service but just defines that they have a service and service name.






High Performance Computing Services

Azure Batch


Amazon Batch


Reserve Capacity instances

Low Priority VM’s

Preemptible instance

Spot instances


Reserved Instances

Reserved Instances

Committed use

EC2 Reserved Instances


Dedicated instances


EC2 Dedicated Instances


Bare Metal hosts


Yes (Announced)


Burstable Instances





VM Metadata support





Custom Instance Sizes





Compute Service Identity






High performance disk

Premium Disk

SSD persistent disk, Local SSD


SSD Octane


N-series (NV, NC, ND)

Flexible GPU

P2 instances / Flexible GPU

Only as bare metal

Nested virtualization support Yes Yes (Beta) Yes  
Hybrid Story Azure Stack Nutanix VMware VMware

GPU cards support

M60, K80, P40, P100

K80, P100, AMD S9300

M60, Custom GPU, V100

P100, M60, K80

Desktop as a service

Third Party

Third party

Workspaces & AppStream

Third Party

Scale set

VM Scale Set

Instance Group

Auto Scaling

Auto Scale

Godzilla VM

Standard_M128 128vCPU, 3800 GB

N1-highmem 96vCPU, 1433 GB

X1.32large 128vCPU, 4 TB

56 vCPU, 242 GB Memory (Other Bare Metal)

Skylake support Yes Yes Yes  

VMware support

Yes (Announced)


Yes (Limited to the US)


Billing for VM

Per minute

Per Second

Per Second (For some)

Per Hour

Deployment & Automation service

Azure Resource Manager

Google Deployment Manager


IBM Cloud Schematics


PowerShell, AzureCLI

GCloud CLI, Cloud Tools for PowerShell

AWS CLI, AWS Tools for PowerShell

Bluemix CLI

Monitoring & Logging

Microsoft Log Analytics, Azure Monitor


CloudWatch, Cloudtrail

Monitoring and Analytics


Azure Advisor

Native Service in UI

Trusted Advisor


Automation tools

Azure Automation


Amazon CloudOps for Chef and Puppet

Cloud Automation, Workload Scheduler

Support for third party configuration and infrastructure tools

Chef, Puppet, Terraform, Ansible, SaltStack

Chef, Puppet, Terraform, Ansible, SaltStack

Chef, Puppet, Terraform, Ansible, SaltStack


Cloud Shell support




EDI Tools

Azure Logic Apps


In the next blog post I will take a closer look at some price comparison and comparing apples and apples in some benchmarks which measures speed of deployment using the different deployment tools and in VM spped on different levels.