Almost every computing system
nowadays is distributed, ranging from multi-core laptops to
Internet-scale services; understanding the principles of distributed
computing is hence important for the design and engineering of modern
computing systems. Fundamental issues that arise in reliable and
efficient distributed systems include developing adequate methods for
modeling failures and synchrony assumptions, determining precise
performance bounds on implementations of concurrent data structures,
capturing the trade-off between consistency and efficiency, and
demarcating the frontier of feasibility in distributed computing.
For example, popular Internet services and applications such as CNN.com, YouTube, Facebook, Skype, BitTorrent attract millions of users every day, and only by the effective load-balancing and collaboration of many thousand machines, an acceptable Quality-of-Service/Quality-of-Experience can be guaranteed. While distributed systems promise a good scalability as well as a high robustness, they pose challenging research problems, such as: How to design robust and scalable distributed architectures and services? How to coordinate access to a shared resource, e.g., by electing a leader? Or how to provide incentives for cooperation in an open, collaborative distributed system?
|Author||Bienkowski, Marcin and Schmid, Stefan|
|Title of Book||Proceedings of 12th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2010)|
|Address||Berlin / Heidelberg, Germany|
|Series||Lecture Notes in Computer Science (LNCS)|
|Abstract||We attend to the classic setting where an observer needs to inform a tracker about an arbitrary time varying function f:N_0–>Z. This is an optimization problem, where both wrong values at the tracker and sending updates entail a certain cost. We consider an online variant of this problem, i.e., at time t, the observer only knows f(t') for all t'=<t. In this paper, we generalize existing cost models (with an emphasis on concave and convex penalties) and present two online algorithms. Our analysis shows that these algorithms perform well in a large class of models, and are even optimal in some settings.|