arXiv subjects

Moshe Sulamy

Publications and source records attributed to Moshe Sulamy.

7 recordsLinked to original sources

arXiv2025

MTASet: A Tree-based Set for Efficient Range Queries in Update-heavy Workloads

In concurrent data structures, the efficiency of set operations can vary significantly depending on the workload characteristics. Numerous concurrent set implementations are optimized and fine-tuned to excel in scenarios characterized by predominant read operations. However, they often perform poorly when confronted with workloads that heavily prioritize updates. Additionally, current leading-edge concurrent sets optimized for update-heavy tasks typically lack efficiency in handling atomic range queries. This study introduces the MTASet, which leverages a concurrent (a,b)-tree implementation. Engineered to accommodate update-heavy workloads and facilitate atomic range queries, MTASet surpasses existing counterparts optimized for tasks in range query operations by up to 2x. Notably, MTASet ensures linearizability.

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arXiv2019

The Role of A-priori Information in Networks of Rational Agents

Until now, distributed algorithms for rational agents have assumed a-priori knowledge of $n$, the size of the network. This assumption is challenged here by proving how much a-priori knowledge is necessary for equilibrium in different distributed computing problems. Duplication - pretending to be more than one agent - is the main tool used by agents to deviate and increase their utility when not enough knowledge about $n$ is given. The a-priori knowledge of $n$ is formalized as a Bayesian setting where at the beginning of the algorithm agents only know a prior $\sigma$, a distribution from which they know $n$ originates. We begin by providing new algorithms for the Knowledge Sharing and Coloring problems when $n$ is a-priori known to all agents. We then prove that when agents have no a-priori knowledge of $n$, i.e., the support for $\sigma$ is infinite, equilibrium is impossible for the Knowledge Sharing problem. Finally, we consider priors with finite support and find bounds on the necessary interval $[\alpha,\beta]$ that contains the support of $\sigma$, i.e., $\alpha \leq n \leq \beta$, for which we have an equilibrium. When possible, we extend these bounds to hold for any possible protocol.

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arXiv2019

Consensus in Equilibrium: Can One Against All Decide Fairly?

Is there an equilibrium for distributed consensus when all agents except one collude to steer the decision value towards their preference? If an equilibrium exists, then an $n-1$ size coalition cannot do better by deviating from the algorithm, even if it prefers a different decision value. We show that an equilibrium exists under this condition only if the number of agents in the network is odd and the decision is binary (among two possible input values). That is, in this framework we provide a separation between binary and multi-valued consensus. Moreover, the input and output distribution must be uniform, regardless of the communication model (synchronous or asynchronous). Furthermore, we define a new problem - Resilient Input Sharing (RIS), and use it to find an {\em iff} condition for the $(n-1)$-resilient equilibrium for deterministic binary consensus, essentially showing that an equilibrium for deterministic consensus is equivalent to each agent learning all the other inputs in some strong sense. Finally, we note that $(n-2)$-resilient equilibrium for binary consensus is possible for any $n$. The case of $(n-2)$-resilient equilibrium for \emph{multi-valued} consensus is left open.

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arXiv2018

Reaching Distributed Equilibrium with Limited ID Space

We examine the relation between the size of the id space and the number of rational agents in a network under which equilibrium in distributed algorithms is possible. When the number of agents in the network is not a-priori known, a single agent may duplicate to gain an advantage, pretending to be more than one agent. However, when the id space is limited, each duplication involves a risk of being caught. By comparing the risk against the advantage, given an id space of size $L$, we provide a method of calculating the minimal threshold $t$, the required number of agents in the network, such that the algorithm is in equilibrium. That is, it is the minimal value of $t$ such that if agents a-priori know that $n \geq t$ then the algorithm is in equilibrium. We demonstrate this method by applying it to two problems, Leader Election and Knowledge Sharing, as well as providing a constant-time approximation $t \approx \frac{L}{5}$ of the minimal threshold for Leader Election.

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arXiv2018

Cheating by Duplication: Equilibrium Requires Global Knowledge

The question of what global information must distributed rational agents a-priori know about the network in order for equilibrium to be possible is researched here. Until now, distributed algorithms with rational agents have assumed that $n$, the size of the network, is a-priori known to the participants. We investigate the above question, considering different distributed computing problems and showing how much each agent must a-priori know about $n$ in order for distributed algorithms to be equilibria. The main tool considered throughout the paper is the advantage an agent may gain by duplication- pretending to be more than one agent. We start by proving that when no bound on $n$ is given equilibrium for Coloring and Knowledge Sharing is impossible. %We prove that when agents have no a-priori knowledge on $n$, or even a known bound, equilibrium for both Knowledge Sharing and Coloring is impossible. We provide new algorithms for both problems when $n$ \emph{is} a-priori known to all agents, thus showing that there are algorithms in which the only way for an agent to gain an advantage is duplication. We further show that for each distributed problem there is an a-priori known range, an upper and a lower bound on $n$, such that if the actual $n$ is guaranteed to lay in that range, equilibrium is possible. By providing equilibria for a specific range, and impossibility results for any larger range, we prove the tight range necessary for equilibrium in: Leader Election, Knowledge Sharing, Coloring, Partition and Orientation.

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arXiv2016

Idle Ants Have a Role

Using elementary distributed computing techniques we suggest an explanation for two unexplained phenomena in regards to ant colonies, (a) a substantial amount of ants in an ant colony are idle, and (b) the observed low survivability of new ant colonies in nature. Ant colonies employ task allocation, in which ants progress from one task to the other, to meet changing demands introduced by the environment. Extending the biological task allocation model given in [Pacala, Gordon and Godfray 1996] we present a distributed algorithm which mimics the mechanism ants use to solve task allocation efficiently in nature. Analyzing the time complexity of the algorithm reveals an exponential gap on the time it takes an ant colony to satisfy a certain work demand with and without idle ants. We provide an $O(\ln n)$ upper bound when a constant fraction of the colony are idle ants, and a contrasting lower bound of $Ω(n)$ when there are no idle ants, where $n$ is the total number of ants in the colony.

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arXiv2015

Optimal and Resilient Pheromone Utilization in Ant Foraging

Pheromones are a chemical substance produced and released by ants as means of communication. In this work we present the minimum amount of pheromones necessary and sufficient for a colony of ants (identical mobile agents) to deterministically find a food source (treasure), assuming that each ant has the computational capabilities of either a Finite State Machine (FSM) or a Turing Machine (TM). In addition, we provide pheromone-based foraging algorithms capable of handling fail-stop faults. In more detail, we consider the case where $k$ identical ants, initially located at the center (nest) of an infinite two-dimensional grid and communicate only through pheromones, perform a collaborative search for an adversarially hidden treasure placed at an unknown distance $D$. We begin by proving a tight lower bound of $Ω(D)$ on the amount of pheromones required by any number of FSM based ants to complete the search, and continue to reduce the lower bound to $Ω(k)$ for the stronger ants modeled as TM. We provide algorithms which match the aforementioned lower bounds, and still terminate in optimal $\mathcal{O}(D + D^2 / k)$ time, under both the synchronous and asynchronous models. Furthermore, we consider a more realistic setting, where an unknown number $f < k$ of ants may fail-stop at any time; we provide fault-tolerant FSM algorithms (synchronous and asynchronous), that terminate in $\mathcal{O}(D + D^2/(k-f) + Df)$ rounds and emit no more than the same asymptotic minimum number of $\mathcal{O}(D)$ pheromones overall.

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