Centralized solutions offered by cloud providers are increasingly prevalent in the Internet, despite being single points of failure and threatening the privacy of users by giving access of large amounts of data to a few powerful entities. Decentralized peer-to-peer systems have garnered attention in opposition. However, their limitations, such as high latency and message complexity in searching content constitute a barrier to widespread adoption. This is the case of InterPlanetary File System (IPFS), where the algorithm used to search content comprises two steps: an optimistic and a fallback step. The optimistic step relies on the Bitswap protocol and operates on an unstructured network that tries to find content in a single hop. This solution has low latency but also low success rate. Should a search fail with Bitswap the fallback alternative resorts to the use of a Distributed Hash Table (DHT) to complete the search with a significantly increased latency cost. We propose, implement, and evaluate Goose, an alternative to the Bitswap protocol that exploits principles of informed search to introduce a distributed lightweight indexing scheme and an indirection layer in the unstructured network; increasing the success rate of searches when compared with Bitswap, significantly reducing the reliance on the DHT and improving the median access latency in IPFS by 27% and reducing the number of messages exchanged by up to 67%.

This paper introduces the IPFS requested content location service, a software service to monitor the operation of IPFS from the perspective of the content requested through IPFS gateways. The software is provided as a docker stack that consumes the logs of one or more IPFS gateways, extracts the CID of the requested content and the IP address of the requester, and queries the IPFS network for the providers of the content. The software also matches the IP addresses of the requesters and providers with their geographic location, and stores the results in a database for later analysis. The software has been used in our previous measurement study, published at DAIS'23, that analyzed the operation of IPFS from the perspective of the content requested through gateways.

Prototyping and implementing distributed algorithms, particularly those that address challenges related with fault-tolerance and dependability, is a time consuming task. This is, in part, due to the need of addressing low level aspects such as management of communication channels, controlling timeouts or periodic tasks, and dealing with concurrency issues. This has a significant impact for researchers that want to build prototypes for conducting experimental evaluation; practitioners that want to compare different design alternatives/solutions; and even for practical teaching activities on distributed algorithms courses. In this paper we present Babel, a novel framework to develop, implement, and execute distributed protocols and systems. Babel promotes an event driven programming and execution model that simplifies the task of translating typical specifications or descriptions of algorithms into performant prototypes, while allowing the programmer to focus on the relevant challenges of these algorithms by transparently handling time consuming low level aspects. Furthermore, Babel provides, and allows the definition of, networking components that can capture different network capabilities (e.g., P2P, Client/Server, p-accrual Failure Detector), making the code mostly independent from the underlying communication aspects. Babel was built to be generic and can be used to implement a wide variety of different classes of distributed protocols. We conduct our experimental work with two relevant case studies, a Peer-to-Peer application and a State Machine Replication application, that show the generality and ease of use of Babel and present competitive performance when compared with significantly more complex implementations.

Edge computing has emerged as a solution to address existing limitations of cloud computing for bandwidth-heavy and time-sensitive applications, by moving (some) computations from bandwidth saturated Cloud infrastructures closer to client devices, where data is effectively produced and consumed. However, existing materializations of the edge computing paradigm take limited advantage of computational and storage power that exists in the edge and between client devices and the cloud. Most of these leverage static hierarchical topologies (e.g., Fog Computing) to pre-process data before sending it to the Cloud, which limits the advantages that can be extracted from the edge computing paradigm. In the past, peer-to-peer systems have sought to tackle the challenges of increasing scalability and availability for very large systems, with a large number of solutions being proposed namely, distributed overlay networks for resource management. In this paper, we argue that the clever adaptation of peer-to-peer solutions can enable novel applications to fully exploit the potential of the edge. In particular, we study the viability of taking advantage of specialized overlay networks in edge environments to enable the management of a large number of computational resources. Contrary to previous proposals, that assume the environment to be composed of mostly homogeneous devices, our proposal embraces existing heterogeneity and exploits the location of computational resources to devise a (partially) self-organizing overlay network that can be exploited both to provide membership information to applications, but also do efficiently disseminate management information across edge devices. We have conducted an experimental evaluation using container-based emulation in an heterogeneous network composed by 100 devices, with results showing that our protocol is able to maximize the bandwidth usage of the system, allowing more data to flow throughout the network, while retaining high robustness to failures.

With the advent of Edge Computing, suitable, practical, and novel abstractions are required for applications to leverage the existing computational power at the edge. In particular, applications in the domains of smart cities and the Internet of Things (IoT) can rely on devices in the vicinity of data consumers and producers for their operation. While these devices are expected to be equipped with wireless radios, network infrastructure might be unavailable in many scenarios. In those cases, devices must rely on wireless ad hoc networks for coordination and cooperation. In this context, one of the most important primitives is the broadcast of messages, that can be leveraged as a building block to devise more complex distributed services and applications. The literature on wireless ad hoc broadcast algorithms is quite vast, with many different algorithms being proposed which explore or combine different techniques or features in their operation. While such protocols are becoming increasingly relevant, understanding how they relate among them is complicated. To address this challenge, in this paper, we introduce a novel framework that allows to abstract the operation of wireless ad hoc broadcast protocols. Leveraging on our framework, we explore a particularly interesting class of these protocols: neighbor-aware ad hoc broadcast protocols; of which we propose 4 novel protocols. Finally, we rely on a materialization of our framework to implement prototypes of these protocols and experimentally study their performance in a testbed composed of 21 Raspberry Pi 3 - model B.

The edge computing paradigm brings the promise of overcoming the practical scalability limitations of cloud computing, that are a result of the high volume of data produced by Internet of Things (IoT) and other large-scale applications. The principle of edge computing is to move computations beyond the data center, closer to end-user devices where data is generated and consumed. This new paradigm creates the opportunity for edge-enabled systems and applications, that have components executing directly and cooperatively on edge devices. Having systems' components, actively and directly, collaborating in the edge, requires some form of distributed monitoring as to adapt to variable operational conditions. Monitoring requires efficient ways to aggregate information collected from multiple devices. In particular, and considering some IoT applications, monitoring will happen among devices that communicate primarily via wireless channels. In this paper we study the practical performance of several distributed continuous aggregation protocols in the wireless ad hoc setting, and propose a novel protocol that is more precise and robust than competing alternative.