Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks

Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, we show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Schism: a Workload-Driven Approach to Database Replication and Partitioning

We present Schism, a novel workload-aware approach for database partitioning and replication designed to improve scalability of shared-nothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitioner attempts to minimize the number of distributed transactions, while producing balanced partitions. Schism consists of two phases: i) a workload-driven, graph-based replication/partitioning phase and ii) an explanation and validation phase. The first phase creates a graph with a node per tuple (or group of tuples) and edges between nodes accessed by the same transaction, and then uses a graph partitioner to split the graph into k balanced partitions that minimize the number of cross-partition transactions. The second phase exploits machine learning techniques to find a predicate-based explanation of the partitioning strategy (i.e., a set of range predicates that represent the same replication/partitioning scheme produced by the partitioner). The strengths of Schism are: i) independence from the schema layout, ii) effectiveness on n-to-n relations, typical in social network databases, iii) a unified and fine-grained approach to replication and partitioning. We implemented and tested a prototype of Schism on a wide spectrum of test cases, ranging from classical OLTP workloads (e.g., TPC-C and TPC-E), to more complex scenarios derived from social network websites (e.g., Epinions.com), whose schema contains multiple n-to-n relationships, which are known to be hard to partition. Schism consistently outperforms simple partitioning schemes, and in some cases proves superior to the best known manual partitioning, reducing the cost of distributed transactions up to 30%.Quanta Computer (Firm) (T-Party Project

A survey and classification of storage deduplication systems

The automatic elimination of duplicate data in a storage system commonly known as deduplication is increasingly accepted as an effective technique to reduce storage costs. Thus, it has been applied to different storage types, including archives and backups, primary storage, within solid state disks, and even to random access memory. Although the general approach to deduplication is shared by all storage types, each poses specific challenges and leads to different trade-offs and solutions. This diversity is often misunderstood, thus underestimating the relevance of new research and development. The first contribution of this paper is a classification of deduplication systems according to six criteria that correspond to key design decisions: granularity, locality, timing, indexing, technique, and scope. This classification identifies and describes the different approaches used for each of them. As a second contribution, we describe which combinations of these design decisions have been proposed and found more useful for challenges in each storage type. Finally, outstanding research challenges and unexplored design points are identified and discussed.This work is funded by the European Regional Development Fund (EDRF) through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the Fundacao para a Ciencia e a Tecnologia (FCT; Portuguese Foundation for Science and Technology) within project RED FCOMP-01-0124-FEDER-010156 and the FCT by PhD scholarship SFRH-BD-71372-2010

Enabling Internet-Scale Publish/Subscribe In Overlay Networks

As the amount of data in todays Internet is growing larger, users are exposed to too much information, which becomes increasingly more difficult to comprehend. Publish/subscribe systems leverage this problem by providing loosely-coupled communications between producers and consumers of data in a network. Data consumers, i.e., subscribers, are provided with a subscription mechanism, to express their interests in a subset of data, in order to be notified only when some data that matches their subscription is generated by the producers, i.e., publishers. Most publish/subscribe systems today, are based on the client/server architectural model. However, to provide the publish/subscribe service in large scale, companies either have to invest huge amount of money for over-provisioning the resources, or are prone to frequent service failures. Peer-to-peer overlay networks are attractive alternative solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a published message often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its subscribers. We refer to this undesirable traffic, as relay overhead. Without careful considerations, the relay overhead might sharply increase resource consumption for the relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. To mitigate this problem, some solutions use unbounded number of connections per node, while some other limit the expressiveness of the subscription scheme. In this thesis work, we introduce two systems called Vitis and Vinifera, for topic-based and content-based publish/subscribe models, respectively. Both these systems are gossip-based and significantly decrease the relay overhead. We utilize novel techniques to cluster together nodes that exhibit similar subscriptions. In the topic-based model, distinct clusters for each topic are constructed, while clusters in the content-based model are fuzzy and do not have explicit boundaries. We augment these clustered overlays by links that facilitate routing in the network. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structures resemble navigable small-world networks, which spans along clusters of nodes that have similar subscriptions. The properties of such overlays make them an ideal platform for efficient data dissemination in large-scale systems. The systems requires only a bounded node degree and as we show, through simulations, they scale well with the number of nodes and subscriptions and remain efficient under highly complex subscription patterns, high publication rates, and even in the presence of failures in the network. We also compare both systems against some state-of-the-art publish/subscribe systems. Our measurements show that both Vitis and Vinifera significantly outperform their counterparts on various subscription and churn scenarios, under both synthetic workloads and real-world traces