## 1 Introduction  
Dynamic optimization involves methods for controlling a stochastic system to achieve desirable behavior. This approach has been applied to various problems, including traffic management [1], wastewater treatment processes [2], wind energy extraction [3], and others. Comprehensive surveys on dynamic optimization can be found in [4; 5]. In the context of computer platforms, dynamic optimization has been utilized for various purposes, such as routing optimization in wireless sensor networks [6], task placement and management in fog platforms [7], monitoring and controlling energy usage and power quality [8], and managing unpredictable workloads on large-scale platforms [9]. However, the majority of prior studies have analyzed isolated scenarios and have focused on single-objective optimization. Moreover, dynamic optimization is a highly time-consuming task, and its complexity increases with the number of states the system may have. Various techniques have been employed for dynamic optimization [10], including metaheuristics [4], which can require a significant amount of computation time to converge towards satisfactory solutions, which is a limitation in applications where real-time efficiency or fast processing speeds for large volumes of data is required. Other techniques, such as reinforcement learning and deep reinforcement learning [11; 12], often require significant computational power and resources for training, and may need large amounts of data to learn meaningful patterns.  
In this paper, we introduce a novel approach to dynamic optimization based on metric indices for adjusting parameters in a distributed platform. Specifically, our approach involves a dynamic optimization engine comprising a pivot-based index utilized to search for new parameter values. We evaluate our proposal within the context of distributed platforms used for image classification following natural disasters, such as earthquakes, volcanic eruptions, hurricanes, and wildfires [13], where a substantial, unpredictable workload initially exists but gradually diminishes over time. Therefore, it is crucial to take prompt actions to optimize platform performance to prevent saturation of the available computational resources and ensure timely processing of incoming images.  
Our case study focuses on a crowdsourcing-based platform used to process photos that cannot be automatically classified, often due to blurriness or being out of focus. The classification process involves volunteers connected through digital television (DTV) integrated with a Set-Top Box (STB). Volunteers receive a set of images and their corresponding potential categories from a centralized server. They then tag each image with a specific category and send their responses back to the server. The server aggregates all responses and checks for consensus on each image. This information is valuable for decision-makers in various institutions or public organizations, as it can be used to disseminate critical data and instructions to emergency responders and the general public. It helps in making decisions on how to allocate humanitarian aid resources effectively. The volunteer community operates within a peer-to-peer (P2P) network, where each peer is a TV connected to an STB. The STB processes the compressed digital signal, decompresses it, and transmits it to the television for viewing.