{'fraction_non_alphanumeric': 0.025336927223719677, 'fraction_numerical': 0.0, 'mean_word_length': 5.700361010830325, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Configuration and deployment derivation strategies for distributed real-time and embedded systems

In contrast to federated systems in which hardware and software are tightly coupled, distributed real-time and embedded (DRE) are constructed by allocating software tasks to hardware. This allocation, known as a deployment plan, must ensure that several design constraints, such as QoS demands and strict resource requirements, are satisfied. Further, the financial cost and performance of these systems may differ greatly based on software allocation decisions. As a result, creating a low cost, high performance deployment that satisfies all design constraints is difficult. Distributed real-time and embedded (DRE) systems can also be constructed by using a new development paradigm that relies on configuring commercial-off-the-shelf (COTS) components rather than developing source code from scratch. COTS components are reusable hardware, operating system, and middleware components. Configuring largescale DRE systems with COTS components reduces development-cycle time and cost. DRE systems are subject to strict resource requirements as well as quality of service (QoS) demands, making DRE system configuration difficult. This proposal describes techniques for addressing the challenges of deriving DRE system configurations and deployments. First, we show how heuristic algorithms can be utilized to rapidly determine system deployments that meet QoS demands and resource requirements. Second, we examine the use of metaheuristic algorithms to optimize system-wide deployment properties. Next, we describe a Model-Driven Architecture (MDA) based methodology for constructing a DRE system configuration modeling tool. Finally, we demonstrate a methodology that allows for DRE systems to be evolved over time as new COTS components become available.