Designing and architecting for cloud scale in .NET framework

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Designing and architecting for cloud scale
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In this chapter, we began to answer the question, How do you move to the cloud We looked at design and architecture issues germane to both refactored applications being moved to the cloud and to new applications purpose-built to run in the cloud. The big design topics covered included using sharding to scale very large applications that have databases (because databases frequently become the barrier to scaling), using cloudbursting to keep an application resident in the local data center with overflow capacity provide by the cloud, and using cloud storage services to scale data-storage capacity quickly and easily. In the next chapter, we ll focus on reliability issues that arise in the cloud when hundreds or even thousands of servers are employed by an application.
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Achieving high reliability at cloud scale
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This chapter covers SOA as a precursor to the cloud How loose coupling improves reliability Distributed high-performance cloud reliability, including MapReduce
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The cloud is great for dealing with scale because the public Infrastructure as a Service (IaaS) as well as Platform as a Service (PaaS) clouds are large collections of thousands of virtualized servers with tools that allow you to expand and contract the number of instances of your application according to demand. But what happens when you try to have literally thousands of commodity (cheap) computers all working in parallel Well, some of them will fail as they reach the mean-time-to-failure point. You learned about designing and architecting for scalability in chapter 5. But in the event that you create a popular application (there ll be another Google and Facebook, have no fear), you need to be prepared to deal with those hardware failures. You need to design and architect those applications for reliability. Reliability is important for any application, no matter where it resides, if it s going to be put into production and in any way become mission critical. But the cloud presents interesting challenges as well as opportunities with respect to application reliability.
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Achieving high reliability at cloud scale
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In the first section, you ll read about distributed systems, loose coupling, and how those principles led to Service Oriented Architectures (SOAs), the precursor to the cloud. Distributed systems have been around for decades, but we ll focus on the aspects of this architectural principle most relevant to the cloud. In the second section in this chapter, you ll read about a powerful paradigm called MapReduce that s being used extensively in the cloud to make highly reliable and highly scalable systems that can perform rapid tasks on massive amounts of data. Let s start learning how to design and architect for application reliability in the cloud by going back to the advent of SOA and how they re a direct ancestor of cloud computing.
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SOA as a precursor to the cloud
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Distributed, loosely coupled systems, which formed the basis for SOA, are by now widely used by virtually every organization with an active web presence. They formed the direct precursor to cloud computing. This architecture also presents one of the best approaches to reliable (or at least fault-tolerant) systems. Let s begin this section by examining distributed systems and loose coupling before delving into SOA more deeply and seeing how it has informed reliable cloud computing approaches.
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Distributed systems
The most important point about distributed systems, and why they can be more reliable than nondistributed systems, is that when properly implemented, they have no single point of failure. Distributed web architectures typically fall into one of several basic categories:
Client-server architectures are two-tier. Smart client code contacts the server for data and then formats and displays it to the user. Input at the client is committed back to the server when it represents a permanent change. The server is frequently little more than a database. Three-tier architectures add a businesslogic middle tier. Three-tier systems (see figure 6.1) move the client intelligence (also called business logic) to a middle tier so that stateless clients can be used. This simplifies application deployment. Most web applications are three-tier. N-tier architectures usually refer to web applications that utilize more services. N-tier (see figure 6.2) typically refers to web applications that further forward their requests to other enterprise services. This type
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