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Auditability allows to track all the read operations performed on a register. It abstracts the need of data owners to control access to their data, tracking who read which information. This work considers possible formalizations of auditing and their ramification for the possibility of providing it. The natural definition is to require a linearization of all write, read and audit operations together (atomic auditing). The paper shows that atomic auditing is a powerful tool, as it can be used to solve consensus. The number of processes that can solve consensus using atomic audit depends on the number of processes that can read or audit the register. If there is a single reader or a single auditor (the writer), then consensus can be solved among two processes. If multiple readers and auditors are possible, then consensus can be solved among the same number of processes. This means that strong synchronization primitives are needed to support atomic auditing. We give implementations of atomic audit when there are either multiple readers or multiple auditors (but not both) using primitives with consensus number 2 (swap and fetch&add). When there are multiple readers and multiple auditors, the implementation uses compare&swap. These findings motivate a weaker definition, in which audit operations are not linearized together with read and write operations (regular auditing). We prove that regular auditing can be implemented from ordinary reads and writes on atomic registers.

Despite the widespread usage of FIFO queues in distributed applications, designing efficient wait-free implementations of queues remains a challenge. The majority of wait-free queue implementations restrict either the number of dequeuers or the number of enqueuers that can operate on the queue, even when they use strong synchronization primitives, like the Compare&Swap. If we do not limit the number of processes that can perform enqueue and dequeue operations, the best-known upper bound on the worst case step complexity for a wait-free queue is given by [Khanchandani and Wattenhofer, 2018]. In particular, they present an implementation of a multiple dequeuer multiple enqueuer wait-free queue whose worst case step complexity is in O(√n), where n is the number of processes. In this work, we investigate whether it is possible to improve this bound. In particular, we present a wait-free FIFO queue implementation that supports n enqueuers and k dequeuers where the worst case step complexity of an Enqueue operation is in O(log n) and of a Dequeue operation is in O(k log n). Then, we show that if the semantics of the queue can be relaxed, by allowing concurrent Dequeue operations to retrieve the same element, then we can achieve O(log n) worst-case step complexity for both the Enqueue and Dequeue operations.

LIPIcs, Volume 217, OPODIS 2021, Complete Volume

Front Matter, Table of Contents, Preface, Conference Organization

A shared-memory counter is a well-studied and widely-used concurrent object. It supports two operations: An Inc operation that increases its value by 1 and a Read operation that returns its current value. Jayanti, Tan and Toueg [Jayanti et al., 2000] proved a linear lower bound on the worst-case step complexity of obstruction-free implementations, from read and write operations, of a large class of shared objects that includes counters. The lower bound leaves open the question of finding counter implementations with sub-linear amortized step complexity. In this paper, we address this gap. We present the first wait-free n-process counter, implemented using only read and write operations, whose amortized operation step complexity is O(log^2 n) in all executions. This is the first non-blocking read/write counter algorithm that provides sub-linear amortized step complexity in executions of arbitrary length. Since a logarithmic lower bound on the amortized step complexity of obstruction-free counter implementations exists, our upper bound is optimal up to a logarithmic factor.

Consensus is one of the central distributed abstractions. By enabling a collection of processes to agree on one of the values they propose, consensus can be used to implement any generic replicated service in a consistent and fault-tolerant way. In this paper, we study uncontended complexity of anonymous consensus algorithms, counting the number of memory locations used and the number of memory updates performed in operations that encounter no contention. We assume that contention-free operations on a consensus object perform "fast" reads and writes, and resort to more expensive synchronization primitives, such as CAS, only when contention is detected. We call such concurrent implementations interval-solo-fast and derive one of the first nontrivial tight bounds on space complexity of anonymous interval-solo-fast consensus.

We study the problem of counting the number of nodes in a slotted-time communication network, under the challenging assumption that nodes do not have identifiers and the network topology changes frequently. That is, for each time slot links among nodes can change arbitrarily provided that the network is always connected. This network model has been motivated by the ongoing development of new communication technologies that enable the deployment of a massive number of devices with highly dynamic connectivity patterns. Tolerating dynamic topologies is clearly crucial in face of mobility and unreliable communication. Current communication networks do have node identifiers though. Nevertheless, in future massive networks, it might be suitable to avoid nodes IDs to facilitate mass production. Consequently, knowing what is the cost of anonymity is of paramount importance to understand what is feasible or not for future generations of Dynamic Networks. Counting is a fundamental task in distributed computing since knowing the size of the system often facilitates the desing of solutions for more complex problems. Also, the size of the system is usually used to decide termination in distributed algorithms. Currently, the best upper bound proved on the running time to compute the exact network size is double-exponential. However, only linear complexity lower bounds are known, leaving open the question of whether efficient Counting protocols for Anonymous Dynamic Networks exist or not. In this paper we make a significant step towards answering this question by presenting a distributed Counting protocol for Anonymous Dynamic Networks which has exponential time complexity. This algorithm, which we call Incremental Counting, ensures that eventually every node knows the exact size of the system and stops executing the protocol. Previous Counting protocols have either double-exponential time complexity, or they are exponential but do not terminate, or terminate but do not provide ru