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Schiavoni, Valerio

Nom
Schiavoni, Valerio
Affiliation principale
Site web
Fonction
Maitre-Assistant, Coordinateur Complex Systems and Big Data Competence Centre
Email
valerio.schiavoni@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/293
_
0000-0003-1493-6603

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  • Publication
    Métadonnées seulement
    BRISA: Combining Efficiency and Reliability in Epidemic Data Dissemination
    (2012-5-21)
    Matos, Miguel
    ;
    Schiavoni, Valerio 
    ;
    Felber, Pascal 
    ;
    Oliveira, Rui
    ;
    Rivière, Etienne 
    There is an increasing demand for efficient and robust systems able to cope with today's global needs for intensive data dissemination, e.g., media content or news feeds. Unfortunately, traditional approaches tend to focus on one end of the efficiency/robustness design spectrum, by either leveraging rigid structures such as trees to achieve efficient distribution, or using loosely-coupled epidemic protocols to obtain robustness. In this paper we present BRISA, a hybrid approach combining the robustness of epidemic-based dissemination with the efficiency of tree-based structured approaches. This is achieved by having dissemination structures such as trees implicitly emerge from an underlying epidemic substrate by a judicious selection of links. These links are chosen with local knowledge only and in such a way that the completeness of data dissemination is not compromised, i.e., the resulting structure covers all nodes. Failures are treated as an integral part of the system as the dissemination structures can be promptly compensated and repaired thanks to the underlying epidemic substrate. Besides presenting the protocol design, we conduct an extensive evaluation in a real environment, analyzing the effectiveness of the structure creation mechanism and its robustness under faults and churn. Results confirm BRISA as an efficient and robust approach to data dissemination in the large scale.
  • Publication
    Métadonnées seulement
    TOPiCo: Detecting Most Frequent Items from Multiple High-Rate Event Streams
    (: ACM, 2015-6-29)
    Schiavoni, Valerio 
    ;
    Rivière, Etienne 
    ;
    Sutra, Pierre 
    ;
    Felber, Pascal 
    ;
    Matos, Miguel
    ;
    Oliveira, Rui
    Systems such as social networks, search engines or trading platforms operate geographically distant sites that continu- ously generate streams of events at high-rate. Such events can be access logs to web servers, feeds of messages from participants of a social network, or financial data, among others. The ability to timely detect trends and popularity variations is of paramount importance in such systems. In particular, determining what are the most popular events across all sites allows to capture the most relevant informa- tion in near real-time and quickly adapt the system to the load. This paper presents TOPiCo, a protocol that com- putes the most popular events across geo-distributed sites in a low cost, bandwidth-efficient and timely manner. TOPiCo starts by building the set of most popular events locally at each site. Then, it disseminates only events that have a chance to be among the most popular ones across all sites, significantly reducing the required bandwidth. We give a correctness proof of our algorithm and evaluate TOPiCo using a real-world trace of more than 240 million events spread across 32 sites. Our empirical results shows that (i) TOPiCo is timely and cost-efficient for detecting popular events in a large-scale setting, (ii) it adapts dynamically to the distribution of the events, and (iii) our protocol is particularly efficient for skewed distributions.
  • Publication
    Métadonnées seulement
    Lightweight, Efficient, Robust Epidemic Dissemination
    (2013-1-13)
    Matos, Miguel
    ;
    Schiavoni, Valerio 
    ;
    Felber, Pascal 
    ;
    Oliveira, Rui
    ;
    Rivière, Etienne 
    Gossip-based protocols provide a simple, scalable, and robust way to disseminate messages in large-scale systems. In such protocols, messages are spread in an epidemic manner. Gossiping may take place between nodes using push, pull, or a combination. Push-based systems achieve reasonable latency and high resilience to failures but may impose an unnecessarily large redundancy and overhead on the system. At the other extreme, pull-based protocols impose a lower overhead on the network at the price of increased latencies. A few hybrid approaches have been proposed—typically pushing control messages and pulling data—to avoid the redundancy of high-volume content and single-source streams. Yet, to the best of our knowledge, no other system intermingles push and pull in a multiple-senders scenario, in such a way that data messages of one help in carrying control messages of the other and in adaptively adjusting its rate of operation, further reducing overall cost and improving both on delays and robustness. In this paper, we propose an efficient generic push-pull dissemination protocol, Pulp, which combines the best of both worlds. Pulp exploits the efficiency of push approaches, while limiting redundant messages and therefore imposing a low overhead, as pull protocols do. Pulp leverages the dissemination of multiple messages from diverse sources: by exploiting the push phase of messages to transmit information about other disseminations, Pulp enables an efficient pulling of other messages, which themselves help in turn with the dissemination of pending messages. We deployed Pulp on a cluster and on PlanetLab. Our results demonstrate that Pulp achieves an appealing trade-off between coverage, message redundancy, and propagation delay.
  • Publication
    Accès libre
  • Publication
    Métadonnées seulement
    On the Support of Versioning in Distributed Key-Value Stores
    (: IEEE, 2014-10-6)
    Felber, Pascal 
    ;
    Pasin, Marcelo 
    ;
    Rivière, Etienne 
    ;
    Schiavoni, Valerio 
    ;
    Sutra, Pierre 
    ;
    Coehlo, Fábio
    ;
    Oliveira, Rui
    ;
    Matos, Miguel
    ;
    Vilaça, Ricardo
    The ability to access and query data stored in multiple versions is an important asset for many applications, such as Web graph analysis, collaborative editing platforms, data forensics, or correlation mining. The storage and retrieval of versioned data requires a specific API and support from the storage layer. The choice of the data structures used to maintain versioned data has a fundamental impact on the performance of insertions and queries. The appropriate data structure also depends on the nature of the versioned data and the nature of the access patterns. In this paper we study the design and implementation space for providing versioning support on top of a distributed key-value store (KVS). We define an API for versioned data access supporting multiple writers and show that a plain KVS does not offer the necessary synchronization power for implementing this API. We leverage the support for listeners at the KVS level and propose a general construction for implementing arbitrary types of data structures for storing and querying versioned data. We explore the design space of versioned data storage ranging from a flat data structure to a distributed sharded index. The resulting system, ALEPH, is implemented on top of an industrial-grade open-source KVS, Infinispan. Our evaluation, based on real-world Wikipedia access logs, studies the performance of each versioning mechanisms in terms of load balancing, latency and storage overhead in the context of different access scenarios.
  • Publication
    Accès libre
    SAFETHINGS: Data Security by Design in the IoT
    (: IEEE, 2017-9-4)
    Barbosa, Manuel
    ;
    Ben Mokhtar, Sonia
    ;
    Felber, Pascal 
    ;
    Maia, Francisco
    ;
    Matos, Miguel
    ;
    Oliveira, Rui
    ;
    Rivière, Etienne 
    ;
    Schiavoni, Valerio 
    ;
    Voulgaris, Spyros
    Despite years of research and the long-lasting promise of pervasiveness of an "Internet of Things", it is only recently that a truly convincing number of connected things have been deployed in the wild. New services are now being built on top of these things and allow to realize the IoT vision.However, integration of things in complex and interconnected systems is still only in the hands of their manufacturers and of Cloud providers supporting IoT integration platforms. Several issues associated with data privacy arise from this situation. Not only do users need to trust manufacturers and IoT platforms for handling their data, but integration between heterogeneous platforms is still only incipient.In this position paper, we chart a new IoT architecture, SAFETHINGS, that aims at enabling data privacy by design, and that we believe can serve as the foundation for a more comprehensive IoT integration. The SAFETHINGS architecture is based on two simple but powerful conceptual component families, the cleansers and blenders, that allow data owners to get back the control of IoT data and its processing.
  • Publication
    Accès libre
    On the Cost of Safe Storage for Public Clouds: an Experimental Evaluation
    (: IEEE, 2016-9-26)
    Burihabwa, Dorian 
    ;
    Pontes, Rogério
    ;
    Felber, Pascal 
    ;
    Maia, Francisco
    ;
    Mercier, Hugues 
    ;
    Oliveira, Rui
    ;
    Paulo, João
    ;
    Schiavoni, Valerio 
    Cloud-based storage services such as Dropbox, Google Drive and OneDrive are increasingly popular for storing enterprise data, and they have already become the de facto choice for cloud-based backup of hundreds of millions of regular users. Drawn by the wide range of services they provide, no upfront costs and 24/7 availability across all personal devices, customers are well-aware of the benefits that these solutions can bring. However, most users tend to forget-or worse ignore-some of the main drawbacks of such cloud-based services, namely in terms of privacy. Data entrusted to these providers can be leaked by hackers, disclosed upon request from a governmental agency's subpoena, or even accessed directly by the storage providers (e.g., for commercial benefits). While there exist solutions to prevent or alleviate these problems, they typically require direct intervention from the clients, like encrypting their data before storing it, and reduce the benefits provided such as easily sharing data between users. This practical experience report studies a wide range of security mechanisms that can be used atop standard cloud-based storage services. We present the details of our evaluation testbed and discuss the design choices that have driven its implementation. We evaluate several state-of-the-art techniques with varying security guarantees responding to user-assigned security and privacy criteria. Our results reveal the various trade-offs of the different techniques by means of representative workloads on top of industry-grade storage services.
  • Publication
    Métadonnées seulement
    LayStream: composing standard gossip protocols for live video streaming
    (: IEEE, 2014-9-8)
    Matos, Miguel
    ;
    Schiavoni, Valerio 
    ;
    Rivière, Etienne 
    ;
    Felber, Pascal 
    ;
    Oliveira, Rui
    Gossip-based live streaming is a popular topic, as attested by the vast literature on the subject. Despite the particular merits of each proposal, all need to implement and deal with common challenges such as membership management, topology construction and video packets dissemination. Well-principled gossip-based protocols have been proposed in the literature for each of these aspects. Our goal is to assess the feasibility of building a live streaming system, LAYSTREAM, as a composition of these existing protocols, to deploy the resulting system on real testbeds, and report on lessons learned in the process. Unlike previous evaluations conducted by simulations and considering each protocol independently, we use real deployments. We evaluate protocols both independently and as a layered composition, and unearth specific problems and challenges associated with deployment and composition. We discuss and present solutions for these, such as a novel topology construction mechanism able to cope with the specificities of a large-scale and delay-sensitive environment, but also with requirements from the upper layer. Our implementation and data are openly available to support experimental reproducibility.