Coud computing is definitely turning out to be the proverbial carrot for enterprises to host their applications. The cloud promises many benefits to users. It obviates the need for upfront capital expenses for computing infrastructure, real estate and maintenance personnel. This technology allows for scaling up or scaling down as demand on the application fluctuates.
While the advantages are many, migrating application onto the cloud is no trivial task. The cloud is essentially composed of commodity servers. The cloud creates multiple instances of the application and runs it on the same or on different servers. The benefit of executing in parallel is that the same task can be completed faster. The cloud offers enterprises the ability to quickly scale to handle increasing demands.
But the process of deploying applications on to the cloud requires that the application be re-architected to take advantage of this parallelism. But the ability to handle parallelization is no simple task. The key attributes that need to be handled by distributed systems are consistency and availability. If there are variables that need to be shared across the parallel instances, the application must make special provisions to handle this and ensure consistency. Similarly, the application must be designed to handle failures.
Applications that are intended to be deployed on the cloud must be designed to scale-out rather than having the ability to scale-up. Scaling up refers to the process of adding more horsepower by way of faster CPUs, more RAM and faster throughput. But applications that need to be deployed on the cloud need to have the ability to scale out or scale horizontally, where more servers are added without any change in processing horsepower. The design for horizontal scalability is the key to cloud computing architectures.
One of the key principles to keep in mind while designing for the cloud is to ensure that the application is composed of loosely coupled processes, preferably based on SOA principles. While a multi-threaded architecture -- where resource sharing through mutexes works in monolithic applications -- such an architecture is of no help when there are multiple instances of the same application running on different servers. How does one maintain consistency of the shared resource across instances? This is a tough problem. Ideally the application should be thread-safe and should be based on a shared-nothing kind of architecture. One such technique is to use queues that the cloud provides as a means of sharing across instances. However, this may affect the performance of the system. Other methods include using “memcached,” which has been used successfully by Facebook, Twitter, Livejournal, and Zynga deployed on the cloud. Still another method is to use the MapReduce algorithm, where the variables across instances are handled by “map,” and the “reduce” part handles the consistency across instances.
Another key consideration is the need to support availability requirements. Since the cloud is made up of commodity hardware, there is every possibility of servers failing. The application must be designed with in-built resilience to handle such failures. This could by designing active-standby architecture or by providing for check-pointing so that application can re-start from some known previous point.
Hence, while cloud computing is the way to go in the future, there is a need to be able to carefully design the application to take full advantage of the cloud.
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