Abstract
There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads.

Abstract
Wide availability of edge computing platforms, as expected in emerging 5G networks, enables a computing continuum between centralized cloud services and the edge of the network, close to end-user devices. This is particularly appealing for online analytics as data collected by devices is made available for decisionmaking. However, cloud-based parallel-distributed data processing platforms are not able to directly access data on the edge. This can be circumvented, at the expense of freshness, with data synchronization that periodically uploads data to the cloud for processing. In this work, we propose an adaptive database synchronization system that makes distributed data in edge nodes available dynamically to the cloud by balancing between reducing the amount of data that needs to be transmitted and the computational effort needed to do so at the edge. This adapts to the availability of CPU and network resources as well as to the application workload.

Abstract
We present PAID, a framework that allows developers to implement portable I/O policies and optimizations for different applications with minor modifications to their original code base. The chief insight behind PALO is that if we are able to intercept and differentiate requests as they flow through different layers of the I/O stack, we can enforce complex storage policies without significantly changing the layers themselves. PAIO adopts ideas from the Software-Defined Storage community, building data plane stages that mediate and optimize I/O requests across layers and a control plane that coordinates and fine-tunes stages according to different storage policies. We demonstrate the performance and applicability of PALO with two use cases. The first improves 99th percentile latency by 4 x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments.

Abstract
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, simulation of biological systems, or quantum computation, inconsistency has to be addressed in a precise and controlled way. This paper generalises Belnap-Dunn four-valued logic, introducing paraconsistent transition systems (PTS), endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values, and their modal logic.

Abstract
This paper introduces a class of automata and associated languages, suitable to model a computational paradigm of fuzzy systems, in which both vagueness and simultaneity are taken as first-class citizens. This requires a weighted semantics for transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The usual relationships between automata and languages are revisited in this setting, including a specific Kleene theorem.

Abstract