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Syscussion for zkSNARKs
by Mytilinis Giagos 10 Dec '18

10 Dec '18

Καλησπέρα, Αυτήν την Παρασκευή, στην 1 μ.μ., θα αναβιώσουμε το Syscussion για να συζητήσουμε για το zkSNARKs. Πρόκειται για ένα κρυπτογραφικό πρωτόκολλο που χρησιμοποιείται σε blockchains και μας επιτρέπει να επαληθεύουμε την ορθότητα υπολογισμών χωρίς να τους εκτελούμε και χωρίς να ξέρουμε τι ακριβώς εκτελέστηκε (black magic stuff).

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Παρασκευή 31/08
by Nikolaos Chalvantzis 28 Aug '18

28 Aug '18

Καλησπέρα, με την ευκαιρία της παρουσίασης την δουλειάς μας, "ORiON: Online ResOurce Negotiator for multiple Big Data Analytics frameworks" στο ICAC 2018 την ερχόμενη εβδομάδα, σκεφτήκαμε με το Γιάννη να εγκαινιάσουμε τη νέα σεζόν με μια πρόβα αυτή την Παρασκευή στη 13.00. Ακολουθεί το abstract: In recent years we observe the rapid growth of large-scale analytics applications in a wide range of domains – from healthcare infrastructures to traffic management. The high volume of data that need to be processed has stimulated the development of special purpose frameworks which handle the data deluge by parallelizing data processing and concurrently using multiple computing nodes. These frameworks differentiate significantly in terms of the policies they follow to decompose their workloads into multiple tasks and also on the way they exploit the available computing resources. As a result, based on the framework that applications have been implemented in, we observe significant variations in their resource utilization and execution times. Therefore, determining the appropriate framework for executing a big data application is not trivial. In this work we propose Orion, a novel resource negotiator for cloud infrastructures that support multiple big data frameworks such as Apache Spark, Apache Flink and TensorFlow. More specifically, given an application, Orion determines the most appropriate framework to assign it to. Additionally, Orion reserves the required resources so that the application is able to meet its performance requirements. Our negotiator exploits state- of-the-art prediction techniques for estimating the application’s execution time when it is assigned to a specific framework with varying configuration parameters and processing resources. Finally, our detailed experimental evaluation, using practical big data workloads on our local cluster, illustrates that our approach outperforms its competitors. N. -- Nikolaos Chalvantzis PhD Candidate Computing Systems Laboratory, School of ECE National Technical University of Athens e-mail: nchalv(a)cslab.ece.ntua.gr skype: n1k0s_ch47vant21s tel: (+30)6977299040

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18/05/18
by Nikolaos Chalvantzis 15 May '18

15 May '18

Καλησπέρα, Ετοιμάζω για την Παρασκευή το παρακάτω paper που θα παρουσιαστεί στο φετινό SIGMOD σε περίπου ένα μήνα. > *P-Store: An Elastic Database System with Predictive Provisioning,** > *Rebecca Taft, Nosayba El-Sayed, Marco Serafini, Yu Lu, Ashraf > Aboulnaga, Michael Stonebraker, Ricardo Mayerhofer, Francisco Andrade > > Abstract > OLTP database systems are a critical part of the operation of many > enterprises. Such systems are often configured statically with > sufficient capacity for peak load. For many OLTP applications, > however, the maximum load is an order of magnitude larger than the > minimum, and load varies in a repeating daily pattern. It is thus > prudent to allocate computing resources dynamically to match demand. > One can allocate resources reactively after a load increase is > detected, but this places additional burden on the already-overloaded > system to reconfigure. A predictive allocation, in advance of load > increases, is clearly preferable. We present P-Store, the first > elastic OLTP DBMS to use prediction, and apply it to the workload of > B2W Digital (B2W), a large online retailer. Our study shows that > P-Store outperforms a reactive system on B2W’s workload by causing 72% > fewer latency violations, and achieves performance comparable to > static allocation for peak demand while using 50% fewer servers. -- Nikolaos Chalvantzis PhD Candidate Computing Systems Laboratory, School of ECE National Technical University of Athens e-mail: nchalv(a)cslab.ece.ntua.gr skype: n1k0s_ch47vant21s tel: (+30)6977299040

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SYScussion on this Friday
by Giagkos Mytilinis 07 May '18

07 May '18

Γεια σε όλους, Στα πλαίσια του SYScussion, αυτήν την Παρασκευή στις 13:30 θα φιλοξενήσουμε την ομάδα από το MADgIK group που πήρε την 3η θέση στο φετινό SIGMOD Programming Contest. Το SIGMOD Programming Contest είναι ένας διαγωνισμός που γίνεται κάθε χρόνο και περιλαμβάνει προβλήματα από τον χώρο των Βάσεων Δεδομένων. Η αρχική ιδέα του διαγωνισμού ήταν το πρόβλημα κάθε χρονιάς να στοχεύει να βελτιστοποιήσει ένα συγκεκριμένο DB-component και συνθέτοντας τα μετά από χρόνια να προκύψει ένα ολοκληρωμένο πραγματικό σύστημα. Το φετινό πρόβλημα είχε να κάνει με Shared Joins Optimization. Τα παιδιά θα μας παρουσιάσουν τη λύση τους και θα κάνουμε κουβέντα πάνω στο πρόβλημα. Cheers, Γιάγκος -- Ioannis Mytilinis PhD Student Computing Systems Laboratory, School of ECE National Technical University of Athens www.cslab.ece.ntua.gr/~gmytil email: gmytil(a)cslab.ece.ntua.gr tel:+30 210 7721601

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Αυριανο syscussion
by Nikos Provatas 19 Apr '18

19 Apr '18

Καλησπέρα σας, Αυριο θα παρουσιάσω το paper με τίτλο NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data των: Alessandro Lulli , Matteo Dell’Amico , Pietro Michiardi , Laura Ricci το οποίο δημοσιεύτηκε στο vldb του 2016. Το abstract του paper: We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN’s performance and scalability. ⁣Nikos Provatas PhD Candidate Computing Systems Laboratory (CSLab) National Technical University of Athens

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Syscussion on this Friday
by Giagkos Mytilinis 21 Mar '18

21 Mar '18

Hi all, Αυτήν την Παρασκευή (23/3), θα έχουμε την χαρά να κάνουμε host στο Syscussion τον Νίκο Κατσιπουλάκη (cc'ed). Ο Νίκος κάνει το PhD του στο University of Pittsburgh, όπου δουλεύει πάνω σε stream processing μαζί με τον Αλέξανδρο Λαμπρινίδη. Το talk θα γίνει στη 13:00 κατά τα γνωστά.. Ακολουθούν title και abstract: Title: To Distribute, To Drop, or To Divide, that is the question in Adaptive (and Fast) Stream Processing Abstract: Increasing need for (efficient) online dataflow processing has triggered the rapid evolution of Parallel Stream Processing Engines (pSPEs). In a pSPE, data are processed by the time they become available and queries execute for a long period of time. Low operational cost and production of results in a timely fashion can be a challenge for a pSPE, considering the volatile nature of input streams: when the arrival rate spikes, pSPEs need to adjust their internal components, so that late delivery of results is avoided. This calls for adaptable engines that are able to react to fluctuations in processing demands.In this talk, I will present work done on pSPEs' adaptability and efficiency, which I completed as a graduate student at PITT. -- Ioannis Mytilinis PhD Student Computing Systems Laboratory, School of ECE National Technical University of Athens www.cslab.ece.ntua.gr/~gmytil email: gmytil(a)cslab.ece.ntua.gr tel:+30 210 7721601

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Παρουσίαση 16/03
by Nikos Provatas 12 Mar '18

12 Mar '18

Καλησπέρα σας, Την Παρασκευή 16/03 θα παρουσιάσω το paper με τίτλο *NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data *των: Alessandro Lulli , Matteo Dell’Amico , Pietro Michiardi , Laura Ricci το οποίο δημοσιεύτηκε στο vldb του 2016. Το abstract του paper: We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN’s performance and scalability. **

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upcoming syscussion: struc2vec: Learning Node Representations from Structural Identity
by Tasos Bakogiannis 27 Feb '18

27 Feb '18

Καλησπέρα, την Παρασκευή 2/3/18 θα παρουσιαστεί το παρακάτω paper: Title: struc2vec: Learning Node Representations from Structural Identity Authors: Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo Abstract: Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at differ- ent scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a con- sequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity. Link: http://www.kdd.org/kdd2017/papers/view/struc2vec-learning-node-representati… Τάσος.

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syscussion schedule.
by Tasos Bakogiannis 21 Feb '18

21 Feb '18

Καλημέρα, ήθελα να ρωτήσω, επειδή δεν μου είναι πολύ σαφές, ποια είναι η περιοδικότητα του syscussion. Είναι κάθε 1 ή 2 εβδομάδες; Ρωτάω γιατί είτε αυτή είτε την επόμενη φορά θα πρέπει να παρουσιάσω κι εγώ. Τάσος.

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Next syscussion: Titian: Data Provenance Support in Spark
by Evie Kassela 15 Feb '18

15 Feb '18

Kαλησπέρα, Το paper που θα παρουσιαστεί στο αυριανό syscussion είναι: *Title:* Titian: Data Provenance Support in Spark *Authors:* Matteo Interlandi, Kshitij Shah, Sai Deep Tetali, Muhammad Ali Gulzar, Seunghyun Yoo, Miryung Kim, Todd Millstein, and Tyson Condie *Abstract:* Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders-of-magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time. Παρουσιάστηκε στο VLDB 2016. Eύη -- Evdokia Kassela PhD Candidate Computing Systems Laboratory, School of ECE National Technical University of Athens

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