Considerations of creating a successful SDP

Hello

I’ve recently joined a BPO (Business Process Outsourcing) provider company which gives me an excellent opportunity to put my knowledge of SOA and SaaS in action. So I guess that’s what is going to shape my future posts here.
Well, here is one.
SDPs (Service Delivery Platforms) are playing almost the same role for delivering Software as a Service (SaaS) as Operating Systems do in desktop applications’ development and deployment. Rather than requiring each application to create the full stack of subsystems needed for it to run, an operating system provides an infrastructure through which general purpose services are reused. The following picture depicts the natural and ongoing process of extraction and generalization of functionality from application into frameworks and from there into the core platform components which leads to the improvement of economies of scale.

Figure 1: Borrowed from Microsoft's Architecture Journal

There would be the same concept in various levels offered by SDPs. There are different factors that can be used to specify the level of success of an SDP. What I mean by the Level of Success is SDP’s effectiveness and scalability, and the ability to provide highly reusable services – for example through an SDK - that will make the implementation and maintenance of SaaS-delivered applications less intensive.
Observation of existing SDP offerings seems to indicate that two most important factors are:

• Services breadth: the completeness of the platform; in other word, the support for different stage of SaaS-delivered application life cycle (following picture)
• Services depth: the degree of sophistication of the services it provides.
  

Figure 2: Borrowed from Microsoft's Architecture Journal

Hence there are two aspects that SDP implementers (mostly traditional hosters) and ISVs (Independent Software Vendors) who develop and deploy the service should take under consideration:

• Different Application Archetypes; Business applications can be classified in different archetypes based on their characteristics and requirements. Two examples of these archetypes are OLAP and OLTP. Each of these application families has its own constraints and characteristics. For example OLTP will optimize for low latency, whereas latency for OLAP systems is not as important. The infrastructure to implement and support each is significantly different.
  The point is that SDP’s effectiveness is pretty much dependent on the archetype served. The more knowledge of the application an SDP has, the greater its ability to increase the efficiency of running and operating it, and the greater the degree of sharing.
• Patterns and Frameworks used in design and development; no matter what archetype an application is bound to, it can follow a pattern in design or development or it can use a framework to implement some of its services. An example of common, standard and widely adopted application infrastructure framework is Microsoft’s Enterprise Library.
  I would say a valuable SDP provides an SDK including documentation, samples and even some basic tools for ISVs enabling them to develop their software using known patterns and frameworks. This way the SDP has a much increased ability to automate common procedures and offer more advanced operational management capabilities. Thus, finer-grain tuning, customization and troubleshooting will be available.
  Additionally, hosters can offer a higher range of differentiated services with different monetization schemes. For instance, the hoster knows that all applications will log run-time exceptions. So basic run-time exception logging can be offered in the basic hosting package, and advanced logging, notification and escalation could become a premium offering. Notice that with this approach the ISV application doesn’t change, because all the logic resides on the SDP side.
  

Figure 3: Borrowed from Microsoft's Architecture Journal

MSA

You might've heard of MSA (Master of Science in Analytics) by now.
It’s an intensive 10-month professional graduate degree program designed by Institute for Advanced Analytics at North Carolina State University that focuses exclusively on the tools, methods, and applications of analytics and is designed to educate professionals with sophisticated technical skills necessary to navigate and analyze the masses of data that organizations are collecting.
The objectives of the program are:

• provide students wit an understanding of basic concept and methodologies in the analysis of massive data sets
• Show how these methods are applied to a variety of complex problems facing organizations, using real-world problems
• Give students a sense of the broader context, such as security, privacy and ethical issues in the use of personal and confidential data

What makes this program unique is its emphasis on real-world, business-focused analytics. Comparing this program with other business related programs you'll realize that its aim is to provide the talent capable of leveraging world-class business intelligence systems. For example typical MBA degrees include limited instruction in statistics or advanced degrees in Data Mining don’t address critical and contextual issues such as data quality and integration, privacy, security and enterprise-wide decision making.
This endorses what the course designers believed that “Competing on analytics in corporations, government agencies and educational institutions is becoming a must”.

What has mostly caught my attention (and the reason I made this post) was that this program is about how to apply mathematic to get what you are looking for. Those who, like me, have studied applied mathematics and liked it and dealt with pure-math professors know what I mean.

If you like to participate and be one of the first graduates of this program, you better hurry. For more information you can take a look at the program’s website at NCSU.