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The basic load balancing scenario involves a single dispatcher where tasksarrive that must immediately be forwarded to one of $N$ single-server queues.We discuss recent advances on scalable load balancing schemes which providefavorable delay performance when $N$ grows large, and yet only require minimalimplementation overhead. Join-the-Shortest-Queue (JSQ) yields vanishing delaysas $N$ grows large, as in a centralized queueing arrangement, but involves aprohibitive communication burden. In contrast, power-of-$d$ or JSQ($d$) schemesthat assign an incoming task to a server with the shortest queue among $d$servers selected uniformly at random require little communication, but lead toconstant delays. In order to examine this fundamental trade-off between delayperformance and implementation overhead, we consider JSQ($d(N)$) schemes wherethe diversity parameter $d(N)$ depends on $N$ and investigate what growth rateof $d(N)$ is required to asymptotically match the optimal JSQ performance onfluid and diffusion scale.  Stochastic coupling techniques and stochastic-process limits play aninstrumental role in establishing the asymptotic optimality. We demonstrate howthis methodology carries over to infinite-server settings, finite buffers,multiple dispatchers, servers arranged on graph topologies, and token-basedload balancing including the popular Join-the-Idle-Queue (JIQ) scheme. In thisway we provide a broad overview of the many recent advances in the field. Thissurvey extends the short review presented at ICM 2018 (arXiv:1712.08555).

Scalable load balancing in networked systems: A survey of recent advances