Population protocols are a popular model of distributed computing, in which randomly-interacting agents with little computational power cooperate to jointly perform computational tasks. Inspired by developments in molecular computation, and in particular DNA computing, recent algorithmic work has focused on the complexity of solving simple yet fundamental tasks in the population model, such as leader election (which requires convergence to a single agent in a special "leader" state), and majority (in which agents must converge to a decision as to which of two possible initial states had higher initial count). Known results point towards an inherent trade-off between the time complexity of such algorithms, and the space complexity, i.e. size of the memory available to each agent.In this paper, we explore this trade-off and provide new upper and lower bounds for majority and leader election. First, we prove a unified lower bound, which relates the space available per node with the time complexity achievable by a protocol: for instance, our result implies that any protocol solving either of these tasks for n agents using O(log log n) states must take Ω(n/polylogn) expected time. This is the first result to characterize time complexity for protocols which employ super-constant number of states per node, and proves that fast, poly-logarithmic running times require protocols to have relatively large space costs.On the positive side, we give algorithms showing that fast, poly-logarithmic convergence time can be achieved using O(log 2 n) space per node, in the case of both tasks. Overall, our results highlight a time complexity separation between O(log log n) and Θ(log 2 n) state space size for both majority and leader election in population protocols, and introduce new techniques, which should be applicable more broadly.
Population protocols are a popular model of distributed computing, in which n agents with limited local state interact randomly, and cooperate to collectively compute global predicates. Inspired by recent developments in DNA programming, an extensive series of papers, across different communities, has examined the computability and complexity characteristics of this model. Majority, or consensus, is a central task in this model, in which agents need to collectively reach a decision as to which one of two states A or B had a higher initial count. Two metrics are important: the time that a protocol requires to stabilize to an output decision, and the state space size that each agent requires to do so. It is known that majority requires Ω(log log n) states per agent to allow for fast (poly-logarithmic time) stabilization, and that O(log 2 n) states are sufficient. Thus, there is an exponential gap between the space upper and lower bounds for this problem. This paper addresses this question.On the negative side, we provide a new lower bound of Ω(log n) states for any protocol which stabilizes in O(n 1−c ) expected time, for any constant c > 0. This result is conditional on monotonicity and output assumptions, satisfied by all known protocols. Technically, it represents a departure from previous lower bounds, in that it does not rely on the existence of dense configurations. Instead, we introduce a new generalized surgery technique to prove the existence of incorrect executions for any algorithm which would contradict the lower bound. Subsequently, our lower bound also applies to general initial configurations, including ones with a leader. On the positive side, we give a new algorithm for majority which uses O(log n) states, and stabilizes in O(log 2 n) expected time. Central to the algorithm is a new leaderless phase clock technique, which allows agents to synchronize in phases of Θ(n log n) consecutive interactions using O(log n) states per agent, exploiting a new connection between population protocols and power-of-twochoices load balancing mechanisms. We also employ our phase * Dan
Population protocols, roughly defined as systems consisting of large numbers of simple identical agents, interacting at random and updating their state following simple rules, are an important research topic at the intersection of distributed computing and biology. One of the fundamental tasks that a population protocol may solve is majority: each node starts in one of two states; the goal is for all nodes to reach a correct consensus on which of the two states was initially the majority. Despite considerable research effort, known protocols for this problem are either exact but slow (taking linear parallel time to converge), or fast but approximate (with non-zero probability of error).In this paper, we show that this trade-off between precision and speed is not inherent. We present a new protocol called Average and Conquer (AVC) that solves majority exactly in expected parallel convergence time O(log n/(s ) + log n log s), where n is the number of nodes, n is the initial node advantage of the majority state, and s = Ω(log n log log n) is the number of states the protocol employs. This shows that the majority problem can be solved exactly in time poly-logarithmic in n, provided that the memory per node is s = Ω(1/ + log n log 1/ ). On the negative side, we establish a lower bound of Ω(1/ ) on the expected parallel convergence time for the case of four memory states per node, and a lower bound of Ω(log n) parallel time for protocols using any number of memory states per node.
Population protocols are networks of finite-state agents, interacting randomly, and updating their states using simple rules. Despite their extreme simplicity, these systems have been shown to cooperatively perform complex computational tasks, such as simulating register machines to compute standard arithmetic functions. The election of a unique leader agent is a key requirement in such computational constructions. Yet, the fastest currently known population protocol for electing a leader only has linear stabilization time, and it has recently been shown that no population protocol using a constant number of states per node may overcome this linear bound.In this paper, we give the first population protocol for leader election with polylogarithmic stabilization time, using polylogarithmic memory states per node. The protocol structure is quite simple: each node has an associated value, and is either a leader (still in contention) or a minion (following some leader). A leader keeps incrementing its value and "defeats" other leaders in one-to-one interactions, and will drop from contention and become a minion if it meets a leader with higher value. Importantly, a leader also drops out if it meets a minion with higher absolute value. While these rules are quite simple, the proof that this algorithm achieves polylogarithmic stabilization time is non-trivial. In particular, the argument combines careful use of concentration inequalities with anti-concentration bounds, showing that the leaders' values become spread apart as the execution progresses, which in turn implies that straggling leaders get quickly eliminated. We complement our analysis with empirical results, showing that our protocol stabilizes extremely fast, even for large network sizes. * Work performed in part while an intern with Microsoft Research. 1 An alternative definition is when reactions occur in parallel according to a Poisson process [PVV09, DV12].
Most people believe that renaming is easy: simply choose a name at random; if more than one process selects the same name, then try again. We highlight the issues that occur when trying to implement such a scheme and shed new light on the read-write complexity of randomized renaming in an asynchronous environment. At the heart of our new perspective stands an adaptive implementation of a randomized test-and-set object, that has poly-logarithmic step complexity per operation, with high probability. Interestingly, our implementation is anonymous, as it does not require process identifiers. Based on this implementation, we present two new randomized renaming algorithms. The first ensures a tight namespace of n names using O(n log 4 n) total steps, with high probability. This improves on the best previously known algorithm by almost a quadratic factor. The second algorithm achieves a namespace of size k(1 +) using O(k log 4 k/ log 2 (1 +)) total steps, both with high probability, where k is the total contention in the execution. It is the first adaptive randomized renaming algorithm, and it improves on existing deterministic solutions by providing a smaller namespace, and by significantly lowering complexity.
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