Even with the considerable advances in the development of middleware solutions, there is still a substantial gap in Internet of Things (IoT) and highperformance computing (HPC) integration. It is not possible to expose services such as processing, storage, sensing, security, context awareness, and actuating in a unified manner with the existing middleware solutions. The consequence is the utilization of several solutions with their particularities, thus requiring different skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce the gap between IoT and HPC technologies, we present the JavaCá&Lá (JCL), a middleware used to help the implementation of distributed user-applications classified as IoT-HPC. This ubiquity is possible because JCL incorporates (1) a single application programming interface to program different device categories; (2) the support for different programming models; (3) the interoperability of sensing, processing, storage, and actuating services; (4) the integration with MQTT technology; and (5) security, context awareness, and actions services introduced through JCL application programming interface. Experimental evaluations demonstrated that JCL scales when doing the IoT-HPC services. Additionally, we identify that customized JCL deployments become an alternative when Java-Android and vice-versa code conversion is necessary. The MQTT brokers usually are faster than JCL HashMap sensing storage, but they do not perform distributed, so they cannot handle a huge amount of sensing data. Finally, a short example for monitoring moving objects exemplifies JCL facilities for IoT-HPC development. KEYWORDShigh-performance computing, Internet of Things, middleware 584 Big data, 1 Internet of Things (IoT), 2 and elastic cloud services 3 are technologies that provide this new decentralized, dynamic, and communication-intensive society. According to El Baz et al, 4 the demand of integration of Internet of Things (IoT) and high-performance computing (HPC) exists, and it will increase soon motivated by different applications, like smart buildings, smart cities, or smart logistics. In the work of McKee et al, 5 it used the concept Internet of Simulation as an IoT extension, so the authors advocated that HPC, cloud, edge, 6 and fog 7 computing is not enough for smart cities demands. In summary, these new applications are requiring both alternatives (IoT and HPC, for instance) in a single middleware solution, but the integration imposes new challenges related with heterogeneity because both technologies must communicate and operate together. Other challenges related to fundamental IoT and HPC requirements, like deployment, code refactoring, performance, scheduling, and fault tolerance, are also important and hard to be solved when integration is mandatory. We understand that fog and edge computing are decentralizing cloud services even more to reduce data transfer latency, but without evidence of IoT services support, thus no integration is achieved while using these ...

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Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.