Cloud-based interprocess communication in .NET

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Amazon Simple Queue Service (SQS) is a way of sending messages between applications (components in a distributed application) via web services over the internet. The intent of SQS is to provide a highly scalable and hosted message queue.
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Amazon SQS Queue
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The simple structure of Amazon s SQS interprocess communication web service
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SQS works in a complementary fashion with EC2. (See figure 6.5.) It s a highly reliable, scalable message queuing service that enables asynchronous message-based communication between distributed components of an application. Those components are typically EC2 instances. You can send any number of messages to an Amazon SQS queue at any time from any component. The messages can be retrieved from the same component or a different one right away or at a later time. No message is ever lost in the interim; each message is persistently stored in highly available, highly reliable queues. Multiple processes can read, write from, and write to an Amazon SQS queue at the same time without interfering with each other. Now that you ve delved into loose coupling, its incarnation in distributed applications, and the way in which those distributed application components may communicate across the internet, let s look at a framework called MapReduce that handles much of this infrastructure for you and yet allows scaling to a massive level, all the while giving you a simple way to maintain high reliability.
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In all engineering disciplines, reliability is the ability of a system to perform its required functions under stated conditions for a specified period of time. In software, for application reliability, this becomes the ability of a software application and all the components it depends on (operating system, hypervisor, servers, disks, network connections, power supplies, and so on) to execute without faults or halts all the way to completion. But completion is defined by the application designer. Even with perfectly written software and no detected bugs in all underlying software systems, applications that begin to use thousands of servers will run into the mean-time-to-failure in some piece of hardware, and some number of those instances will fail. Therefore, the application depending on those instances will also fail. The cloud, with its tendency to use commodity hardware and virtualization, and with the potential for enormous scale, presents many additional challenges to designing reliable applications.
Achieving high reliability at cloud scale
Redundancy
In addition to the standard set of things to consider when designing a reliable application, building in redundancy is the most important additional factor for the cloud. Many design techniques for achieving high reliability depend upon redundant software, data, and hardware. For example, NASA for decades has built systems called triple modular redundancy with spares, which means that three copies of a critical system are live and producing answers that run through a voter who produces the final answer. If one or more of the live systems fails, a spare can be powered up and brought online. Such redundancy helps ensure recovery from detected failure conditions. These techniques are expensive and complex but can be less so in the cloud because some of the hard stuff is provided by the cloud vendors. For redundant software components, this may consist of double- or triple-redundant software components (portions of your application) running in parallel with common validation checks. One idea is to have the components developed by different teams based on the same specifications. This approach costs more, but extreme reliability may require it. Because each component is designed to perform the same function, the failures of concurrent identical components are easily discovered and corrected during quality-assurance testing. You should be cautious, because it s possible for separate developer teams to make the same error in reading the specification. Such common-cause errors are infrequent but do happen. Although redundant software components provide the quality-assurance process with a clever way to validate service accuracy, certain applications may want to deploy component redundancy into the production environment. Such runtime component redundancy may be useful for situations where the runtime environment is hazardous and can t be strictly controlled (such as the space shuttle). In such hazardous conditions, multiple parallel application processes can provide validity checks on each other and let the majority rule. Although it s true that redundant software components cause extra resource consumption, the tradeoff between reliability and the cost of extra hardware may be worth it. Another redundancy-based design technique is the use of services such as clustering (linking many computers together to act as a single faster computer), load-balancing (workloads kept balanced between multiple computers), data replication (making multiple identical copies of data to be processed independently and in parallel), and protecting complex operations with transactions to ensure process integrity. There is a lot less complexity when you use a cloud provider because it has in-built infrastructure that does this for you. Redundant hardware is one of the most popular strategies for providing reliable systems. This includes redundant arrays of independent disks (RAID) for data storage, redundant network interfaces, and redundant power supplies. With this kind of hardware infrastructure, individual component failures can occur without affecting the overall reliability of the application. It s important to use standardized commodity hardware to allow easy installation and replacement.
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