LossEstimate: Distributed failure estimation in wireless networks

► We introduce a distributed feedback mechanism for quantifying the global amount of communication failures in a network. ► The proposed algorithm is self-adaptive and fully distributed. ► Our algorithm performs very well in both static and mobile networks. The ongoing evolution of software-intensive distributed systems to ultra-large-scale (ULS) systems require innovative methods for building, running, and managing these systems. Component self-adaptation and self-configuration properties are thus becoming mandatory requirements in order to cope with application complexity. An increasing number of systems, such as video content distribution, make use of distributed feed-back mechanisms to build-up intelligent, robust and self-managing services. Technology wise, with the wide-spread usage of wireless communication interfaces on today's mobile devices, communication failures are an ever increasing nuisance in the design of distributed self-adaptive services and applications. Communication protocols designed for wired networks are not suited for this new class of networks (including mobile ad-hoc networks, wireless sensor networks, vehicular ad-hoc networks, etc.) due to the several orders of magnitude higher amount of communication failures. Although virtually every single existing communication protocol tries to deal with the various effects introduced by communication failures, almost all existing state of the art relies on previous knowledge about the amount of errors occurring at run time (information usually collected from previous deployments). A survey of current literature easily shows that, in contrast, applications that make use of distributed feedback mechanisms via online estimation of communication errors has received relatively small attention. In this paper we introduce a new distributed feedback mechanism, named LossEstimate, for runtime quantification of the global amount of communication failures present in a large-scale network. The new algorithm helps building self-adaptive services and has the advantage of being fully distributed – each node computes an estimate of the amount of errors using a gossip-alike approach. The algorithm is adaptive in the sense that it can follow changes in the mean value of the amount of communication failures over time. We focus our analysis on the impact of various network topologies, discussing the case of fully connected networks (relevant for the case of peer-to-peer networks), static multihop topologies