As Distributed Ledger Technologies (DLTs) mature, the inherent performance and scalability shortcomings of linearly structured blockchain designs become better understood. Protocols based on Directed Acyclic Graphs (DAGs) have been proposed to address such shortcomings. DAG-based protocols differ from traditional DLTs in the way they build and represent knowledge about transactions and relations between them. While traditional DLTs have straightforward homogeneous semantic attached to blocks and links between blocks, the semantic of vertices and edges in DAG-based protocols is nuanced and varied. In this work, we identify almost a dozen of knowledge-building dimensions in DAG-based DLTs, none of which have been studied before. Knowledge-building is important in DAG-based DLTs because of its significant impact on the size of the DAG, the pace at which new transactions are added, the finality of transactions, and so on. We analyze 40 DAG-based DLTs from this perspective, summarize our results in a taxonomy, and identify a number of research gaps.

Accumulation of blockchain data has led to a significant body of studies pertaining to block generation performance, financial assets, user interactions, etc. At the same time, surprisingly little attention has been dedicated to analyzing system characteristics of transactional workloads and to generating synthetic workloads. We argue that such analysis is imperative for multiple design and assessment goals. In this paper, we provide a comprehensive analysis of transaction features in the workload of three UTXO-based blockchain systems. The analysis covers 15 months and includes metrics such as input and output count, transaction size, temporal features, as well as their correlations and evolution over time. A few of the findings are quite surprising and have never been observed in the past. For example, we observe a shift in the distribution of output count over time. We also provide the first synthetic workload generator for UTXO transactions and release it as open source.

Systems managing Verifiable Credentials are becoming increasingly popular. Unfortunately, their support for revoking previously issued credentials allows verifiers to effectively monitor the validity of the credentials, which is sensitive information. While the issue started to gain recognition, no adequate solution has been proposed so far. In this work, we propose a novel framework for time-limited continuous verification. The holder is able to individually configure the verification period when sharing information with the verifier, and the system guarantees proven untraceability of the revocation status after the verification period expires. Differently from existing systems, the implementation adopts a more scalable blacklist approach where tokens corresponding to revoked credentials are stored in the registry. The approach employs ZK proofs that allow holders to prove non-membership in the blacklist. In addition to theoretically proving security, we evaluate the approach analytically and experimentally and show that it significantly improves bandwidth consumption on the holder while being on par with state-of-the-art solutions with respect to the other performance metrics.

AI inference workflows are typically structured as a pipeline or graph of AI programs triggered by events. As events occur, the AIs perform inference or classification tasks under time pressure to respond or take some action. Standard techniques that reduce latency in other streaming settings (such as caching and optimization-driven scheduling) are of limited value because AI data access patterns (models, databases) change depending on the triggering event: a significant departure from traditional streaming. In this work, we propose a novel affinity grouping mechanism that makes it easier for developers to express application-specific data access correlations, enabling coordinated management of data objects in server clusters hosting streaming inference tasks. Our proposals are thus complementary to other approaches such as caching and scheduling. Experiments confirm the limitations of standard techniques, while showing that the proposed mechanism is able to maintain significantly lower latency as workload and scale-out increase, and yet requires only minor code changes.

The principle of Network Neutrality (NN) has been debated around the world for nearly two decades. NN states that all traffic in the Internet must be treated equally, regardless of content, origin and/or destination. The main motivation for this principle is to protect fair competition, innovation, and ensure freedom of choice for consumers. The global debate revolves around whether NN should be enforced through regulations or not, as well as the potential impact of such regulations – or lack thereof – on the telecommunications market. In this context, multiple governments worldwide have already implemented NN regulations. In this work, we give an overview of NN regulations in 50 countries across five continents. We first give a brief introduction to the NN global debate. Then, we describe some of the main aspects related to the regulatory process of each country/region. Finally, we compare the different regulations according to common and divergent features identified.

The worldwide debate over Network Neutrality (NN) has been raging on for nearly two decades. According to NN principles, all traffic in the Internet must be treated with impartiality. In particular, unfair Traffic Differentiation (TD) is not allowed. Several strategies have been proposed for detecting TD, but locating the source of TD is still an under-explored topic. In this work, we present a holistic approach for unifying TD detection solutions into a single framework with the purpose of locating the source of TD. We propose an algorithm for combining measurements from multiple vantage points, and a strategy for selecting good vantage points. Our proposals leverage Internet peering properties to infer the behavior of individual Autonomous Systems (ASes), without requiring knowledge of the exact routes traversed by measurement probes. To evaluate our proposals, we first ran several experiments to confirm that indeed Internet routes do present the required properties. Then, several simulations were performed to assess the efficiency of our proposals. Results show that our approach is capable of locating TD under several different conditions. Another finding is that issuing measurements from a few end-hosts of core Internet ASes achieves similar results than from a much larger number of end-hosts at the edge.

The purpose of this demo is to visually show a testbed monitoring strategy used to select "stable" sets of nodes to run new protocols. The stability of a set of nodes is defined in terms of the ability of the nodes to communicate among themselves within given time bounds during reasonable intervals of time. We assume an unstable network, in which some nodes may not be able to communicate with some others, and this condition varies with time. In order to measure stability, the communication between pairs of nodes is continuously monitored by measuring the corresponding Round Trip Time (RTT). A stability graph is generated from the monitoring data in which vertices represent network nodes and an each edge means the corresponding nodes are considered to be stable during an observation period. Multiple different structures have been embedded on the stability graph to select a large enough number of nodes on which the new protocols are executed: based on degree, clique, and k-core. We compare the different strategies both in terms of the quality of the set of nodes returned and how they fare as time passes.

Network Neutrality states that all traffic in the Internet must be treated equally and thus cannot suffer unfair traffic differentiation (TD). Several solutions for detecting the presence of TD in the Internet have been proposed. However, locating where in the network TD is happening is still an open problem. In this work, we propose a strategy to locate Autonomous Systems (ASes) that are differentiating traffic. The proposed strategy takes advantage of AS-level routing properties to identify valid AS-level paths between end-hosts. It is then possible to select measurement points between which the AS-level paths traverse suspect ASes. Probes are sent from the measurement points and processed using end-to-end TD detectors based on statistical inference. The main idea is to check suspect ASes until only the AS that is actually discriminating traffic is filtered out. We first present results of experiments executed to validate the routing properties employed. Then the efficiency of the proposal for locating TD is evaluated using simulation. The results show that the proposed strategy is effective and efficient.

Network Neutrality is essential for ensuring a level playing field for the development of new applications and services on the Internet. Laws and rules alone might not be enough to protect innovation, fair competition and consumer's freedom of choice online. The research community has the responsibility to propose solutions that reveal discriminatory traffic management mechanisms on the Internet. We present the potential risks of a non-neutral Internet, identify several open challenges for designing solutions that detect traffic differentiation, and propose a model that addresses such challenges by taking advantage of distributed systems technologies.

Users of large scale network testbeds often execute experiments that require a set of nodes that behave and communicate among themselves in a reasonably stable pattern. In this work we call such a set of nodes a stable clique, and introduce a monitoring strategy that allows their detection in PlanetLab, a non-trivial task for such a large scale dynamic network. Nodes monitor each other by sampling the RTT (Round-Trip-Time) and computing its variation. Based on this data and a threshold, pairs of nodes are classified as stable or unstable. A set of graphs is generated, on which maximum sized cliques are computed. Three experiments were conducted in which hundreds of nodes were monitored for several days. Results show the unexpected behavior of some nodes, and the size of the maximum stable clique for different time windows and different thresholds.