We study randomized algorithms for placing a sequence of n nodes on a circle with unit perimeter. Nodes divide the circle into disjoint arcs. We desire that a newly-arrived node (which is oblivious of its index in the sequence) choose its position on the circle by learning the positions of as few existing nodes as possible. At the same time, we desire that that the variation in arc-lengths be small. To this end, we propose a new algorithm that works as follows: The kth node chooses r random points on the circle, inspects the sizes of v arcs in the vicinity of each random point, and places itself at the mid-point of the largest arc encountered. We show that for any combination of r and v satisfying rv ≥ clog k, where c is a small constant, the ratio of the largest to the smallest arc-length is at most eight w.h.p., for an arbitrarily long sequence of n nodes. This strategy of node placement underlies a novel decentralized load-balancing algorithm that we propose for Distributed Hash Tables (DHTs) in peer-to-peer environments. Underlying the analysis of our algorithm is Structured Coupon Collection over n/b disjoint cliques with b nodes per clique, for any n,b ≥ 1. Nodes are initially uncovered. At each step, we choose d nodes independently and uniformly at random. If all the nodes in the corresponding cliques are covered, we do nothing. Otherwise, from among the chosen cliques with at least one uncovered node, we select one at random and cover an uncovered node within that clique. We show that as long as bd ≥ clog n, O(n) steps are suﬃcient to cover all nodes w.h.p. and each of the ﬁrst Ω(n) steps succeeds in covering a node w.h.p. These results are then utilized to analyze a stochastic process for growing binary trees that are highly balanced – the leaves of the tree belong to at most four diﬀerent levels with high probability.