Paolo Giaccone’s research while affiliated with Consorzio Nazionale Interuniversitario per le Telecomunicazioni and other places

The process of estimating the number of individuals within a defined area, commonly referred to as people counting, is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well‐informed decision‐making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart‐device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR. Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy‐preserving mechanisms. Importantly, it seamlessly integrates with trajectory‐based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights.

In resource-constrained cloud systems, e.g., at the network edge or in private clouds, serverless computing is increasingly adopted to deploy microservices-based applications, leveraging its promised high resource efficiency. Provisioning resources to serverless services, however, poses several challenges, due to the high cold-start latency of containers and stringent Service Level Agreement (SLA) requirements of the microservices. In response, we investigate the behavior of containers in different states (i.e., running, warm, or cold) and exploit our experimental observations to formulate an optimization problem that minimizes the energy consumption of the active servers while reducing SLA violations. In light of the problem complexity, we propose a low-complexity algorithm, named AiW, which utilizes a multi-queueing approach to balance energy consumption and system performance by reusing containers effectively and invoking cold-starts only when necessary. To further minimize the energy consumption of data centers, we introduce the two-timescale COmputing resource Management at the Edge (COME) framework, comprising an orchestrator running our proposed AiW algorithm for container provisioning and Dynamic Server Provisioner (DSP) for dynamically activating/deactivating servers in response to AiW’s decisions on request scheduling. COME addresses the mismatch in timescales for resource provisioning decisions at the container and server levels. Extensive performance evaluation through simulation shows AiW’s close match to the optimum and COME’s significant reduction in power consumption by 22–64% compared state-of-the-art alternatives.

Online gaming has seen a significant surge in popularity, becoming a dominant form of entertainment worldwide. This growth has necessitated the evolution of game servers from centralized to distributed models, leading to the emergence of distributed game engines. These engines allow for the distribution of game engine modules (GEMs) across multiple servers, improving scalability and performance. However, this distribution presents a new challenge: the game engine module placement problem. This problem involves strategically placing GEMs to maximize the number of accepted placement requests while minimizing the delay experienced by players, a critical factor in enhancing the gaming experience. The problem can be formulated as an Integer Linear Programming (ILP) model, which provides an optimal solution but suffers from high computational complexity, making it impractical for real-world applications. To address this challenge, this paper introduces two novel heuristic algorithms, MAP-MIND and MAP-MIND*. The MAP-MIND algorithm demonstrates superior performance, achieving near-optimal delay and more than 92% GEM request acceptance in the worst heterogeneous scenarios. The MAP-MIND* algorithm, while slightly under-performing MAP-MIND in terms of delay, proves to be significantly faster, making it a viable alternative for real-world applications with equal GEM request acceptance. The trade-off between the two algorithms offers a flexible approach to GEM placement, balancing performance and computational efficiency.

In an edge-cloud multi-tier network, datacenters provide services to mobile users, with each service having specific latency constraints and computational requirements. Deploying such a variety of services while matching their requirements with the available computing resources is challenging. In addition, time-critical services may have to be migrated as the users move, to keep fulfilling their latency constraints. Unlike previous work relying on an orchestrator with an always-updated global view of the available resources and the users' locations, this work envisions a distributed solution to the above problems. In particular, we propose a distributed asynchronous framework for service deployment in the edge-cloud that increases the system resilience by avoiding a single point of failure, as in the case of a central orchestrator. Our solution ensures cost-efficient feasible placement of services, while using negligible bandwidth. Our results, obtained through trace-driven, large-scale simulations, show that the proposed solution provides performance very close to those obtained by state-of-the-art centralized solutions, and at the cost of a small communication overhead.

The process of estimating the number of individuals within a defined area, commonly referred to as people counting , is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well-informed decision-making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart-device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability , aligning with the stringent requirements set forth by regulations like the European GDPR [1] . Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy-preserving mechanisms. Importantly, it seamlessly integrates with trajectory-based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights.