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Selected Papers of the 41th International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE 2021)
Editors: Tim Willemse and Kirstin Peters
1. Prioritise the Best Variation
Binary session types guarantee communication safety and session fidelity, but alone they cannot rule out deadlocks arising from the interleaving of different sessions. In Classical Processes (CP)$-$a process calculus based on classical linear logic$-$deadlock freedom is guaranteed by combining channel creation and parallel composition under the same logical cut rule. Similarly, in Good Variation (GV)$-$a linear concurrent $\lambda$-calculus$-$deadlock freedom is guaranteed by combining channel creation and thread spawning under the same operation, called fork. In both CP and GV, deadlock freedom is achieved at the expense of expressivity, as the only processes allowed are tree-structured. Dardha and Gay define Priority CP (PCP), which allows cyclic-structured processes and restores deadlock freedom by using priorities, in line with Kobayashi and Padovani. Following PCP, we present Priority GV (PGV), a variant of GV which decouples channel creation from thread spawning. Consequently, we type cyclic-structured processes and restore deadlock freedom by using priorities. We show that our type system is sound by proving subject reduction and progress. We define an encoding from PCP to PGV and prove that the encoding preserves typing and is sound and complete with respect to the operational semantics.
2. Bidirectional Runtime Enforcement of First-Order Branching-Time Properties
Runtime enforcement is a dynamic analysis technique that instruments a monitor with a system in order to ensure its correctness as specified by some property. This paper explores bidirectional enforcement strategies for properties describing the input and output behaviour of a system. We develop an operational framework for bidirectional enforcement and use it to study the enforceability of the safety fragment of Hennessy-Milner logic with recursion (sHML). We provide an automated synthesis function that generates correct monitors from sHML formulas, and show that this logic is enforceable via a specific type of bidirectional enforcement monitors called action disabling monitors.
3. A case study on parametric verification of failure detectors
Partial synchrony is a model of computation in many distributed algorithms and modern blockchains. These algorithms are typically parameterized in the number of participants, and their correctness requires the existence of bounds on message delays and on the relative speed of processes after reaching Global Stabilization Time. These characteristics make partially synchronous algorithms parameterized in the number of processes, and parametric in time bounds, which render automated verification of partially synchronous algorithms challenging. In this paper, we present a case study on formal verification of both safety and liveness of the Chandra and Toueg failure detector that is based on partial synchrony. To this end, we first introduce and formalize the class of symmetric point-to-point algorithms that contains the failure detector. Second, we show that these symmetric point-to-point algorithms have a cutoff, and the cutoff results hold in three models of computation: synchrony, asynchrony, and partial synchrony. As a result, one can verify them by model checking small instances, but the verification problem stays parametric in time. Next, we specify the failure detector and the partial synchrony assumptions in three frameworks: TLA+, IVy, and counter automata. Importantly, we tune our modeling to use the strength of each method: (1) We are using counters to encode message buffers with counter automata, (2) we are using first-order relations to encode message buffers in IVy, […]
4. A Formal Model for Polarization under Confirmation Bias in Social Networks
We describe a model for polarization in multi-agent systems based on Esteban and Ray's standard family of polarization measures from economics. Agents evolve by updating their beliefs (opinions) based on an underlying influence graph, as in the standard DeGroot model for social learning, but under a confirmation bias; i.e., a discounting of opinions of agents with dissimilar views. We show that even under this bias polarization eventually vanishes (converges to zero) if the influence graph is strongly-connected. If the influence graph is a regular symmetric circulation, we determine the unique belief value to which all agents converge. Our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced. We also prove that polarization does not necessarily vanish in weakly-connected graphs under confirmation bias. Furthermore, we show how our model relates to the classic DeGroot model for social learning. We illustrate our model with several simulations of a running example about polarization over vaccines and of other case studies. The theoretical results and simulations will provide insight into the phenomenon of polarization.