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a b s t r a c tAn efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

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Financial organizations communicate with each other using ISO 8583 or its derivatives to complete the request and response cycle of card originated transactions originated from an ATM, POS, or the web. ISO 8583 is a broad standard. Its implementations slightly vary due to the flexibility available within the standard. This paper discusses the problem of adaptability of communication between payment card transaction processing entities due to this flexibility. We first provide an overview of different variations in ISO 8583 implementations and identify the interoperability issue based on our industry experience. We then propose a solution and suggest a way to standardize different implementations so that one organization can communicate with another without or with minimal changes in software. The suggestion is based on the exchange of meta data indicating how the target system is interpreting the header fields during the communication process.Finally, we discuss the benefits of the solution in which the vendors could not only avail the customization flexibility provided by ISO8583 but also ensure that their implementation of the standard is interoperable with others. This reduces the cost of interconnectivity with other partners when a network wants to expands its busines

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Bioinformatics is an active and important research discipline in which molecular data is exponentially growing in complex nature. Because of the substantial research in this field, researchers are faced with critical issues such as bandwidth, storage, and complexity in order to retrieve molecular data. It becomes very difficult to conduct research using low computational devices such as Internet of things and sensors. We are employing migration of the agent technique to decrease network traffic and to mitigate the client’s limited resource problem by utilizing server-side resources to perform large-scale computation. Our proposed solution does not necessitate additional storage or processing power on the client’s side which makes it cost effective. In the proposed solution, (i) an agent visits service provider containing biological data, say sequences requested by the client, (ii) agent fetches the required data, and on the server side it will manipulate the data, and (iii) returns along with the required results to its source platform. Thus, it solves the bandwidth, storage, and computational issues without involving the low resources of the client. For the proof of concept, Java Agent Development (JADE) framework is used as an implementation tool and the results are compared with Java Remote Method Invocation (RMI). It is important to note that our findings reveal that our strategy saves the user up to 16.25% of average time with respect to bandwidth. On the other hand, our approach takes 46.82% less time than the other with respect to data that the agent carries. In addition to the previous contributions, our approach acts as a mashup, to collect data in different format from several service providers, and converts it in any required format. Thus, it solves the problem of complexity hidden in the nature of the data to increase the researchers’ productivity.