Scalability in Computing and Robotics

Abstract

Efficient engineered systems require scalability. A scalable system has increasing performance with increasing system size. In an ideal situation, the increase in performance (e.g., speedup) corresponds to the number of units (e.g., processors, robots, users) that are added to the system (e.g., three times the number of processors in a computer would lead to three times faster computations). However, if multiple units work on the same task, then coordination among these units is required. This coordination can introduce overheads with an impact on system performance. The coordination costs can lead to sublinear improvement or even diminishing performance with increasing system size. However, there are also systems that implement efficient coordination and exploit collaboration of units to attain superlinear improvement. Modeling the scalability dynamics is key to understanding and engineering efficient systems. Known laws of scalability, such as Amdahl's law, Gustafson's law, and Gunther's Universal Scalability Law, are minimalistic phenomenological models that explain a rich variety of system behaviors through concise equations. While useful to gain general insights, the phenomenological nature of these models may limit the understanding of the underlying dynamics, as they are detached from first principles that could explain coordination overheads or synergies among units. Through a decentralized system approach, we propose a general model based on generic interactions between units that is able to describe, as specific cases, any general pattern of scalability included by previously reported laws. The proposed general model of scalability has the advantage of being built on first principles, or at least on a microscopic description of interaction between units, and therefore has the potential to contribute to a better understanding of system behavior and scalability. We show that this generic model can be applied to a diverse set of systems, such as parallel supercomputers, robot swarms, or wireless sensor networks, therefore creating a unified view on interdisciplinary design for scalability.

Designing a User-Centered Interaction Interface for Human–Swarm Teaming

“…We have not performed a parameter sensitivity analysis and our parameters were chosen experimentally. For instance, our swarm size was fixed when studies show that the swarm density has an effect on performance [37]. Another point of discussion is the scalability of our approach.…”

Guerrilla Performance Analysis for Robot Swarms: Degrees of Collaboration and Chains of Interference Events

“…Our main motivation is that swarm performance curves P (N ) seem to possess generic qualities that appear across a wide collection of different swarm scenarios [11,12,14]. Our contribution is to summarize these findings here and to turn them into a practical performance analysis guide for swarm robotics.…”

Self-complexification through integral feedback in eusocial paper wasps of various levels of sociality

“…Complex adaptive systems are composed of multiple components and can exhibit an increase in global performance with increasing system size [ 5 , 6 ]. However, instead of observing an unbounded performance increase (Gustafson's law), it is more common to observe diminishing returns in performance (Amdahl's law and Gunther's universal scalability law) with increasing system size [ 7 ]. In technical systems, parallelizing the execution of tasks is constrained by limitations and bottlenecks.…”