A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities

Distributed minimum spanning tree (MST) problem is one of the most central and fundamental problems in distributed graph algorithms. Garay et al. \cite{GKP98,KP98} devised an algorithm with running time $O(D + \sqrt{n} \cdot \log^* n)$, where $D$ is the hop-diameter of the input $n$-vertex $m$-edge graph, and with message complexity $O(m + n^{3/2})$. Peleg and Rubinovich \cite{PR99} showed that the running time of the algorithm of \cite{KP98} is essentially tight, and asked if one can achieve near-optimal running time **together with near-optimal message complexity**. In a recent breakthrough, Pandurangan et al. \cite{PRS16} answered this question in the affirmative, and devised a **randomized** algorithm with time $\tilde{O}(D+ \sqrt{n})$ and message complexity $\tilde{O}(m)$. They asked if such a simultaneous time- and message-optimality can be achieved by a **deterministic** algorithm. In this paper, building upon the work of \cite{PRS16}, we answer this question in the affirmative, and devise a **deterministic** algorithm that computes MST in time $O((D + \sqrt{n}) \cdot \log n)$, using $O(m \cdot \log n + n \log n \cdot \log^* n)$ messages. The polylogarithmic factors in the time and message complexities of our algorithm are significantly smaller than the respective factors in the result of \cite{PRS16}. Also, our algorithm and its analysis are very **simple** and self-contained, as opposed to rather complicated previous sublinear-time algorithms \cite{GKP98,KP98,E04b,PRS16}

A New Oceanographic Data Portal: Padjadjaran Oceanographic Data Centre (PODC)

Understanding the physio-chemical oceanic and atmospheric processes is critical in monitoring climate change. Archipelagic and Small Island countries are vulnerable to the detrimental effects of climate change, and open access oceanic databases can solve data limitations leading to further development of action plans and government policies. A website was developed (www.isea-podc.org) to distribute and augment free oceanographic data based on various in-situ sampling instruments. Oceanographers review the data collected and stored in the portal. It is led by the Marine Research Laboratory (MEAL), Padjadjaran University, in partnership with Marine Science Institute (MSI), University of the Philippines. This framework supplements information that can support marine ecosystems, fisheries, and climate science studies. Furthermore, all data are accessible to not only the academe but also decision-makers in all aspects. The data sources are student research and the new instruments (RHEA and ARHEA) developed by MEAL. In the future, the portal will be integrated with other government institutional data to provide other functional features and can yield network-wide analyses. In the next phase, collaboration from ASEAN countries should be conducted to gain more impact and provide robust datasets

Message Reduction in the LOCAL Model Is a Free Lunch

A new spanner construction algorithm is presented, working under the LOCAL model with unique edge IDs. Given an n-node communication graph, a spanner with a constant stretch and O(n^{1 + epsilon}) edges (for an arbitrarily small constant epsilon > 0) is constructed in a constant number of rounds sending O(n^{1 + epsilon}) messages whp. Consequently, we conclude that every t-round LOCAL algorithm can be transformed into an O(t)-round LOCAL algorithm that sends O(t * n^{1 + epsilon}) messages whp. This improves upon all previous message-reduction schemes for LOCAL algorithms that incur a log^{Omega (1)} n blow-up of the round complexity