Radio Frequency Identification (RFID) middleware is a new class of software which facilitates data and information communication between automatic identification physical layer and enterprise applications. It provides a distributed environment to process the data from tags read by the readers, translates the data where necessary, and routes it to a variety of backend applications using suitable technologies such as Web, Remote and Windows Services. This paper reports different challenges and the corresponding research approach in developing a RFID middleware to provide a seamless environment from the edge of the enterprise network; moving data from the point of transaction to the enterprise systems. Key features of the RFID middleware architecture are encapsulation of communication details, large-scale network management, intelligent data processing and routing, hardware and software interoperability, system integration and system extendibility.To deal with high volume data, WinRFID middleware is supported by novel algorithms and data representation schemes capable of processing large amounts of data, rectifying errors in real-time, identifying patterns, correlating events, reorganizing and scrubbing data and recovering from faults and exceptions.Interoperability involves simultaneous distributed working of receivers/readers and transponders/tags at different frequencies using different protocols, with read/write capabilities, different read rates, and other characteristics as a layer transparent to the applications.Network management involves deployment, initialization and control of receivers and transponders, which can be organized into a hierarchical structure with operational syntax and semantics attached to each or a group of receivers, transponders and concentrators or even the edge computers.
We model a parallel processing system comprising several homogeneous computers interconnected by a communication network. Jobs arriving to this system have a linear fork-join structure. Each fork of the job gives rise to a random number of tasks that can be processed independently on any of the computers. Since exact analysis of fork-join models is known to be intractable, we resort to obtaining analytical bounds to the mean job response time of the fork-join job. For jobs with a single fork-join and, probabilistic allocation of tasks of the job to the N processors, we obtain upper and lower bounds to the mean job response time. Upper bounds are obtained using the concept of associated random variables and are found to be a good approximation to the mean job response time. A simple lower bound is obtained by neglecting queueing delays. We also find two lower bounds that include queueing delays. For multiple fork-join jobs, we study an approximation based on associated random variables. Finally, two versions of the Join-the-Shortest-Queue (JSQ) allocation policy (i.e., JSQ by batch and JSQ by task) are studied and compared, via simulations and diffusion limits.
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