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The last years have been characterized by the arising of highly distributed computing\ud\udplatforms composed of a heterogeneity of computing and communication resources including\ud\udcentralized high-performance computing architectures (e.g. clusters or large shared-memory\ud\udmachines), as well as multi-/many-core components also integrated into mobile nodes\ud\udand network facilities. The emerging of computational paradigms such as Grid and Cloud\ud\udComputing, provides potential solutions to integrate such platforms with data systems, natural\ud\udphenomena simulations, knowledge discovery and decision support systems responding to a\ud\uddynamic demand of remote computing and communication resources and services.\ud\udIn this context time-critical applications, notably emergency management systems, are\ud\udcomposed of complex sets of application components specialized for executing specific\ud\udcomputations, which are able to cooperate in such a way as to perform a global goal in a\ud\uddistributed manner. Since the last years the scientific community has been involved in facing\ud\udwith the programming issues of distributed systems, aimed at the definition of applications\ud\udfeaturing an increasing complexity in the number of distributed components, in the spatial\ud\uddistribution and cooperation between interested parties and in their degree of heterogeneity.\ud\udOver the last decade the research trend in distributed computing has been focused on\ud\uda crucial objective. The wide-ranging composition of distributed platforms in terms of\ud\uddifferent classes of computing nodes and network technologies, the strong diffusion of\ud\udapplications that require real-time elaborations and online compute-intensive processing as\ud\udin the case of emergency management systems, lead to a pronounced tendency of systems\ud\udtowards properties like self-managing, self-organization, self-controlling and strictly speaking\ud\udadaptivity.\ud\udAdaptivity implies the development, deployment, execution and management of applications\ud\udthat, in general, are dynamic in nature. Dynamicity concerns the number and the specific\ud\udidentification of cooperating components, the deployment and composition of the most\ud\udsuitable versions of software components on processing and networking resources and\ud\udservices, i.e., both the quantity and the quality of the application components to achieve\ud\udthe needed Quality of Service (QoS). In time-critical applications the QoS specification\ud\udcan dynamically vary during the execution, according to the user intentions and the\ud\udDeveloping Real-Time Emergency\ud\udManagement Applications: Methodology for\ud\uda Novel Programming Model Approach\ud\udGabriele Mencagli and Marco Vanneschi\ud\udDepartment of Computer Science, University of Pisa, L. Bruno Pontecorvo, Pisa\ud\udItaly\ud\ud2\ud\ud2 Will-be-set-by-IN-TECH\ud\udinformation produced by sensors and services, as well as according to the monitored state\ud\udand performance of networks and nodes.\ud\udThe general reference point for this kind of systems is the Grid paradigm which, by\ud\uddefinition, aims to enable the access, selection and aggregation of a variety of distributed and\ud\udheterogeneous resources and services. However, though notable advancements have been\ud\udachieved in recent years, current Grid technology is not yet able to supply the needed software\ud\udtools with the features of high adaptivity, ubiquity, proactivity, self-organization, scalability\ud\udand performance, interoperability, as well as fault tolerance and security, of the emerging\ud\udapplications.\ud\udFor this reason in this chapter we will study a methodology for designing high-performance\ud\udcomputations able to exploit the heterogeneity and dynamicity of distributed environments\ud\udby expressing adaptivity and QoS-awareness directly at the application level. An effective\ud\udapproach needs to address issues like QoS predictability of different application configurations\ud\udas well as the predictability of reconfiguration costs. Moreover adaptation strategies need to\ud\udbe developed assuring properties like the stability degree of a reconfiguration decision and the\ud\udexecution optimality (i.e. select reconfigurations accounting proper trade-offs among different\ud\udQoS objectives). In this chapter we will present the basic points of a novel approach that lays\ud\udthe foundations for future programming model environments for time-critical applications\ud\udsuch as emergency management systems.\ud\udThe organization of this chapter is the following. In Section 2 we will compare the existing\ud\udresearch works for developing adaptive systems in critical environments, highlighting their\ud\uddrawbacks and inefficiencies. In Section 3, in order to clarify the application scenarios that\ud\udwe are considering, we will present an emergency management system in which the run-time\ud\udselection of proper application configuration parameters is of great importance for meeting the\ud\uddesired QoS constraints. In Section 4we will describe the basic points of our approach in terms\ud\udof how compute-intensive operations can be programmed, how they can be dynamically\ud\udmodified and how adaptation strategies can be expressed. In Section 5 our approach will\ud\udbe contextualize to the definition of an adaptive parallel module, which is a building block\ud\udfor composing complex and distributed adaptive computations. Finally in Section 6 we will\ud\uddescribe a set of experimental results that show the viability of our approach and in Section 7\ud\udwe will give the concluding remarks of this chapter

Developing Real-Time Emergency Management Applications: Methodology for a Novel Programming Model Approach