Efficient epidemic multicast in heterogeneous networks

The scalability and resilience of epidemic multicast, also called probabilistic or gossip-based multicast, rests on its symmetry: Each participant node contributes the same share of bandwidth thus spreading the load and allowing for redundancy. On the other hand, the symmetry of gossiping means that it does not avoid nodes or links with less capacity. Unfortunately, one cannot naively avoid such symmetry without also endangering scalability and resilience. In this paper we point out how to break out of this dilemma, by lazily deferring message transmission according to a configurable policy. An experimental proof-of-concept illustrates the approach.Fundação para a Ciência e a Tecnologia (FCT) - Project “P-SON: Probabilistically Structured Overlay Networks” (POS C/EIA/60941/2004)

Autonomous multi-dimensional slicing for large-scale distributed systems

Slicing is a distributed systems primitive that allows to autonomously partition a large set of nodes based on node-local attributes. Slicing is decisive for automatically provisioning system resources for different services, based on their requirements or importance. One of the main limitations of existing slicing protocols is that only single dimension attributes are considered for partitioning. In practical settings, it is often necessary to consider best compromises for an ensemble of metrics. In this paper we propose an extension of the slicing primitive that allows multi-attribute distributed systems slicing. Our protocol employs a gossip-based approach that does not require centralized knowledge and allows self-organization. It leverages the notion of domination between nodes, forming a partial order between multi-dimensional points, in a similar way to SkyLine queries for databases. We evaluate and demonstrate the interest of our approach using large-scale simulations.This work received support from the Portuguese Foundation for Science and Technology under grant SFRH/BD/71476/2010

Reliability and performance of UEGO, a clustering-based global optimizer

Den nya tullagen Union Customs Code (UCC) med målet elektroniskt tullhantering inom unionen kommer att börja tillämpas 1 maj 2016. Tullkodex för unionen innebär elektroniskt tullhantering såväl mellan näringslivet och Tullverket som mellan medlemsstaternas tulladministrationer. Syftet med lagen är att förenkla uppgiftslämning och tullhantering för företag vilket kräver omfattande uppgradering av IT-lösningar för både näringslivet och Tullverket. Kvalitet på de uppgifter som överförs till Tullverkets tull-system (TDS) antas ha större betydelse än tidigare då det genom UCC läggs en större vikt på spårbarhet.Organisationer bör därför optimera kvaliteten på de uppgifter som i samband med deklarationer lämnas till Tullverket. Detta för att undvika eventuella problem i framtiden. Optimal datakvalitet erhålls genom effektiva administrativa processer. Denna uppsats har därför genom en fallstudie studerat faktorer som förhindrar ett effektivt administrativt arbete. Resultatet visar att affärssystemets undermåliga funktionalitet, datakvalitetens opålitlighet, utebliven intern kommunikation samt avsaknad av standardiserade arbetsrutiner påverkar effektiviteten på de administrativa processerna negativt.The new customs regulation, Union Customs Code (UCC), with the goal of electronic customs handling within the European Union will apply 1 May 2016. The Union Customs Code means electronic customs handling both between business and customs Administration and between member states' customs administrations. The law aims to simplify disclosure and customs management for businesses, which requires an extensive upgrade of IT solutions for both business and customs administrations. Quality of the data transferred to Customs' Tariff System (TDS) is assumed will havegreater importance than today as the UCC places a greater emphasis on traceability. Organisations should therefore optimize the quality of the data associated with declarations submitted to Customs. It is to avoid any future problems. Optimal data quality is achieved through efficient administrative processes. This paper has therefore through a case study, studied factors that prevent efficient administrative processes. The results show that the business system's substandard functionality, data quality's unreliability, loss of internal communication and a lack of standard in operating procedures are affecting the efficiency of the administrative processes negatively

Robust epidemic aggregation under churn

In large-scale distributed systems data aggregation is a fundamental task that provides a global synopsis over a distributed set of data values. Epidemic protocols are based on a randomised communication paradigm inspired by biological systems and have been proposed to provide decentralised, scalable and fault-tolerant solutions to the data aggregation problem. However, in epidemic aggregation, nodes failure and churn have a detrimental effect on the accuracy of the local estimates of the global aggregation target. In this paper, a novel approach, the Robust Epidemic Aggregation Protocol (REAP), is proposed to provide robustness in the presence of churn by detecting three distinct phases in the aggregation process. An analysis of the impact of each phase over the estimation accuracy is provided. In particular, a novel mechanism is introduced to improve the phase that is most critical for the protocol accuracy. REAP is validated by means of simulations and is shown to achieve convergence with a good level of accuracy for a reasonable range of node churn rates