Reaching consensus in Proof-of-Stake (PoS) based consensus protocols, requires supermajority agreement among participating validator nodes. Such protocols need significant network resources due to the concurrent voting of a large number of consensus nodes. As a solution, these nodes are divided into committees, with each committee voting individually at a dedicated time slot. In this paper, we introduce CliqueSensus, a protocol that, given a distribution of consensus nodes into committees, lets them self-organize into small, ephemeral clusters structured in clique topologies, to accelerate the voting process, while using only a small fraction of the network resources required by conventional message dissemination methods. Our evaluation demonstrates that our protocol exhibits rapid convergence and operates with minimal network overhead. We focus on the PoS consensus algorithm adopted by Ethereum 2.0. In addition to our protocol, we also analyze and simulate the clustering approach that Ethereum has adopted, showcasing that our protocol can reduce validation message dissemination time by 23% to 70%, while requiring about 190 times fewer validation message forwards.

Overlay management is the cornerstone of building robust and dependable Peer-to-Peer systems. A key component for building such overlays is the peer-sampling service, a mechanism that continuously supplies each node with a set of up-to-date peers randomly selected across all alive nodes. Arguably, the most pernicious malicious action against such mechanisms is the provision of arbitrarily created links that point at malicious nodes. This paper proposes Securecyclon, a peer-sampling protocol that deterministically eliminates the ability of malicious nodes to overrepresent themselves in Peer-to-Peer overlays. To the best of our knowledge, this is the first protocol to offer this property, as previous works were able to only bound the proportion of excessive links to malicious nodes, without completely eliminating them. Securecyclon redefines the concept of node descriptors from just being containers of information that enable communication with specific nodes, to being communication certificates that traverse the network and enable nodes to provably discover malicious nodes. We evaluate our solution with the conduction of extended simulations, and we demonstrate that it provides resilience even at the extreme condition of 40% malicious node participation.

Apache Spark is one of the most commonly used frameworks for Big Data processing. Research on the provided streaming dynamic resource allocation feature, has been shown that large data load fluctuations, for instance, in website traffic, have a negative impact on the automatic scaling. Research has also indicated that the lack of data load prediction, which aims at the identification of the expected data load increase on peak hours/days, is the root cause of the aforementioned issue. Hence, this paper proposes an enhanced solution, namely, KORDI (Knowledge-based Orchestrated Resource Distribution), aiming at optimising the allocation of Spark resources on Streaming applications in real time with the use of SARIMAX model. The experimental evaluation proves that the proposed solution provides a cost reduction of 38% without affecting stability.

Blockchains and smart contracts are an emerging, promising technology, that has received considerable attention. We use the blockchain technology, and in particular Ethereum, to implement a large-scale event-based Internet of Things (IoT) control system. We argue that the distributed nature of the “ledger,” as well as, Ethereum's capability of parallel execution of replicated “smart contracts”, provide the sought after automation, generality, flexibility, resilience, and high availability. We design a realistic blockchain-based loT architecture, using existing technologies while by taking into consideration the characteristics and limitations of IoT devices and applications. Furthermore, we leverage blockchain's immutability and Ethereum's support for custom tokens to build a robust and efficient token-based access control mechanism. Our evaluation shows that our solution is viable and offers significant security and usability advantages.

We present and evaluate models that allow clients to access IoT resources using secure and trusted device-to-device (D2D) communication, while utilizing smart contracts to obtain the benefits of blockchain technology. These benefits include decentralized trust, immutability, transparency, and high availability. The models consider different network connection capabilities of the clients and the IoT resources, namely continuous network connectivity and D2D-only connectivity. We describe two approaches for utilizing blockchains and smart contracts in the authorization process: in the first approach, only hashes of the authorization information are recorded on the blockchain. In the second approach, a smart contract handles authorization requests. We implement the approaches using the OAuth 2.0 delegated authorization framework and evaluate the implementations on the public Ethereum testnet Rinkeby, in terms of execution cost, contract creation cost, and delay. Our evaluation quantifies the tradeoffs of blockchain cost and smart contract functionality, such as blocking and non-blocking operation, and the reduction of the transaction cost that can be achieved when multiple authorization requests are concatenated in a single transaction.

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices. In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.

Energy is the scarcest resource in ad hoc wireless networks, particularly in wireless sensor networks requiring a long lifetime. Intermittently switching the radio on and off is widely adopted as the most effective way to keep energy consumption low. This, however, prevents the very goal of communication, unless nodes switch their radios on at synchronized intervals—a rather nontrivial coordination task. In this article, we address the problem of synchronizing node radios to a single universal schedule in wireless mobile ad hoc networks that can potentially consist of thousands of nodes. More specifically, we are interested in operating the network with duty cycles that can be less than 1% of the total cycle time. We identify the fundamental issues that govern cluster merging and provide a detailed comparison of various policies using extensive simulations based on a variety of mobility patterns. We propose a specific scheme that allows a 4,000-node network to stay synchronized with a duty cycle of approximately 0.7%. Our work is based on an existing, experimental MAC protocol that we use for real-world applications and is validated in a real network of around 120 mobile nodes.

In many popular applications like peer-to-peer systems, large amounts of data are distributed among multiple sources. Analysis of this data and identifying clusters is challenging due to processing, storage, and transmission costs. In this paper, we propose GDCluster, a general fully decentralized clustering method, which is capable of clustering dynamic and distributed data sets. Nodes continuously cooperate through decentralized gossip-based communication to maintain summarized views of the data set. We customize GDCluster for execution of the partition-based and density-based clustering methods on the summarized views, and also offer enhancements to the basic algorithm. Coping with dynamic data is made possible by gradually adapting the clustering model. Our experimental evaluations show that GDCluster can discover the clusters efficiently with scalable transmission cost, and also expose its supremacy in comparison to the popular method LSP2P.

Currently, MapReduce is the most popular programming model for large-scale data processing and this motivated the research community to improve its efficiency either with new extensions, algorithmic optimizations, or hardware. In this paper we address two main limitations of MapReduce: one relates to the model's limited expressiveness, which prevents the implementation of complex programs that require multiple steps or iterations. The other relates to the efficiency of its most popular implementations (e.g., Hadoop), which provide good resource utilization only for massive volumes of input, operating sub optimally for smaller or rapidly changing input. To address these limitations, we present AJIRA, a new middleware designed for efficient and generic data processing. At a conceptual level, AJIRA replaces the traditional map/reduce primitives by generic operators that can be dynamically allocated, allowing the execution of more complex batch and stream processing jobs. At a more technical level, AJIRA adopts a distributed, multi-threaded architecture that strives at minimizing overhead for non-critical functionality. These characteristics allow AJIRA to be used as a single programming model for both batch and stream processing. To this end, we evaluated its performance against Hadoop, Spark, Esper, and Storm, which are state of the art systems for both batch and stream processing. Our evaluation shows that AJIRA is competitive in a wide range of scenarios both in terms of processing time and scalability, making it an ideal choice where flexibility, extensibility, and the processing of both large and dynamic data with a single programming model are either desirable or even mandatory requirements.

We focus on a popular message dissemination protocol for wireless ad-hoc networks, Gossip3. Our contribution is twofold. First, we perform an extensive experimental evaluation of Gossip3 under fully utilized wireless channel and across diverse node densities. We identify the parameters of Gossip3 that need special configuration for the protocol to operate optimally. Second, we devise a self-configuration algorithm for Gossip3, that allows the protocol to work optimally for any network. We demonstrate through simulations that our protocol significantly outperforms the default configuration of Gossip3.

Wireless broadcasting systems, such as Digital Video Broadcasting (DVB), are subject to signal degradation, having an effect on end users' reception quality. Reception quality can be improved by increasing signal strength, but this comes at a significantly increased energy use and still without guaranteeing error-free reception. BOOSTER is a system for cooperative repair of DVB streams. It operates based on a fully decentralized epidemic algorithm that boosts reception quality by cooperatively repairing lossy packet streams among the community of DVB viewers. In this demo, we demonstrate BOOSTER in a special configurable implementation that allows on-the-fly tweaking of the error rate, network topology, and some protocol parameters, allowing the audience to get a feeling on how they affect end-user experience and load distribution.

Wireless broadcasting systems, such as Digital Video Broadcasting (DVB), are subject to signal degradation, having an effect on end users' reception quality. Reception quality can be improved by increasing signal strength, but this comes at a significantly increased energy use and still without guaranteeing error-free reception. In this paper we present BOOSTER, a fully decentralized epidemic-based system that boosts reception quality by cooperatively repairing lossy packet streams among the community of DVB viewers. To validate our system, we collected real data by deploying a set of DVB receivers geographically distributed in and around Amsterdam and Utrecht, The Netherlands. We implemented and tested our system in PeerSim, using our collected real trace information as input. We present in detail the crucial design decisions, the algorithms that underpin our system, the realistic experimental methodology, as well as extensive results that demonstrate the feasibility and efficiency of this approach. In particular we conclude that the upload bandwidth required by each node for significant recovery of a real DVB broadcast is in the order of 5KB/sec when nodes allow up to 2 second delay for repairing - a rather trivial bandwidth for today's typical ADSL connections with an acceptable introduced delay.

Energy is the scarcest resource in ad-hoc wireless networks, particularly in wireless sensor networks requiring a long lifetime. Intermittently switching the radio on and off is widely adopted as the most effective way to keep energy consumption low. This, however, prevents the very goal of communication, unless nodes switch their radios on at synchronized intervals, a rather nontrivial coordination task. In this paper we address the problem of synchronizing node radios to a single universal schedule in very large scale wireless ad-hoc networks. More specifically, we focus on how independently synchronized clusters of nodes can detect each other and merge to a common radio schedule. Our main contributions consist in identifying the fundamental subproblems that govern cluster merging, providing a detailed comparison of the respective policies and their combinations, and supporting them by extensive simulation. Energy consumption, convergence speed, and network scalability have been the driving factors in our evaluation. The proposed policies are extensively tested in networks of up to 4,096 nodes. Our work is based on the GMAC protocol, a gossip-based MAC protocol for wireless ad-hoc networks.

Abstract Much research on content‐based P2P searching for file‐sharing applications has focused on exploiting semantic relations between peers to facilitate searching. Current methods suggest reactive ways to manage semantic relations: they rely on the usage of the underlying search mechanism, and infer semantic relationships based on the queries placed and the corresponding replies received. In this paper we follow a different approach, proposing a proactive method to build a semantic overlay. Our method is based on an epidemic protocol that clusters peers with similar content. Peer clustering is done in a completely implicit way, that is, without requiring the user to specify preferences or to characterize the content of files being shared. In our approach, each node maintains a small list of semantically optimal peers. Our simulation studies show that such a list is highly effective when searching files. The construction of this list through gossiping is efficient and robust, even in the presence of changes in the network. Copyright © 2007 John Wiley & Sons, Ltd.

Gossip-based communication protocols are appealing in large-scale distributed applications such as information dissemination, aggregation, and overlay topology management. This paper factors out a fundamental mechanism at the heart of all these protocols: the peer-sampling service. In short, this service provides every node with peers to gossip with. We promote this service to the level of a first-class abstraction of a large-scale distributed system, similar to a name service being a first-class abstraction of a local-area system. We present a generic framework to implement a peer-sampling service in a decentralized manner by constructing and maintaining dynamic unstructured overlays through gossiping membership information itself. Our framework generalizes existing approaches and makes it easy to discover new ones. We use this framework to empirically explore and compare several implementations of the peer-sampling service. Through extensive simulation experiments we show that---although all protocols provide a good quality uniform random stream of peers to each node locally---traditional theoretical assumptions about the randomness of the unstructured overlays as a whole do not hold in any of the instances. We also show that different design decisions result in severe differences from the point of view of two crucial aspects: load balancing and fault tolerance. Our simulations are validated by means of a wide-area implementation.