Atomic Appends: Selling Cars and Coordinating Armies with Multiple Distributed Ledgers

The various applications using Distributed Ledger Technologies (DLT) or blockchains, have led to the introduction of a new "marketplace" where multiple types of digital assets may be exchanged. As each blockchain is designed to support specific types of assets and transactions, and no blockchain will prevail, the need to perform interblockchain transactions is already pressing. In this work we examine the fundamental problem of interoperable and interconnected blockchains. In particular, we begin by introducing the Multi-Distributed Ledger Objects (MDLO), which is the result of aggregating multiple Distributed Ledger Objects - DLO (a DLO is a formalization of the blockchain) and that supports append and get operations of records (e.g., transactions) in them from multiple clients concurrently. Next we define the AtomicAppends problem, which emerges when the exchange of digital assets between multiple clients may involve appending records in more than one DLO. Specifically, AtomicAppend requires that either all records will be appended on the involved DLOs or none. We examine the solvability of this problem assuming rational and risk-averse clients that may fail by crashing, and under different client utility and append models, timing models, and client failure scenarios. We show that for some cases the existence of an intermediary is necessary for the problem solution. We propose the implementation of such intermediary over a specialized blockchain, we term Smart DLO (SDLO), and we show how this can be used to solve the AtomicAppends problem even in an asynchronous, client competitive environment, where all the clients may crash

A Distributed Algorithm for Gathering Many Fat Mobile Robots in the Plane

In this work we consider the problem of gathering autonomous robots in the plane. In particular, we consider non-transparent unit-disc robots (i.e., fat) in an asynchronous setting. Vision is the only mean of coordination. Using a state-machine representation we formulate the gathering problem and develop a distributed algorithm that solves the problem for any number of robots. The main idea behind our algorithm is for the robots to reach a configuration in which all the following hold: (a) The robots' centers form a convex hull in which all robots are on the convex, (b) Each robot can see all other robots, and (c) The configuration is connected, that is, every robot touches another robot and all robots together form a connected formation. We show that starting from any initial configuration, the robots, making only local decisions and coordinate by vision, eventually reach such a configuration and terminate, yielding a solution to the gathering problem.Comment: 39 pages, 5 figure

Brief Announcement: Implementing Byzantine Tolerant Distributed Ledger Objects

Comunicación presentada en DISC 2019 International Symposium on Distributed Computing (Budapest, Hungary, 14-18 October 2019

Network uncertainty in selfish routing

We study the problem of selfish routing in the presence of incomplete network information. Our model consists of a number of users who wish to route their traffic on a network of m parallel links with the objective of minimizing their latency. However, in doing so, they face the challenge of lack of precise information on the capacity of the network links. This uncertainty is modelled via a set of probability distributions over all the possibilities, one for each user. The resulting model is an amalgamation of the KP-model of [13] and the congestion games with user-specific functions of [17]. We embark on a study of Nash equilibria and the price of anarchy in this new model. In particular, we propose polynomial-time algorithms for computing some special cases of pure Nash equilibria and we show that negative results of [17], for the non-existence of pure Nash equilibria in the case of three users, do not apply to our model. Consequently, we propose an interesting open problem in this area, that of the existence of pure Nash equilibria in the general case of our model. Furthermore, we consider appropriate notions for the social cost and the price of anarchy and obtain upper bounds for the latter. With respect to fully mixed Nash equilibria, we propose a method to compute them and show that when they exist they are unique. Finally we prove that the fully mixed Nash equilibrium maximizes the social welfare. 1

Multi-round Master-Worker Computing: a Repeated Game Approach

We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic game among workers. That is, we assume that workers are rational in a game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely repeated game of the mixed extension of the strategic game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the repeated games model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experi- mentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.Comment: 21 pages, 3 figure

Fragmented ARES: Dynamic Storage for Large Objects

Data availability is one of the most important features in distributed storage systems, made possible by data replication. Nowadays data are generated rapidly and developing efficient, scalable and reliable storage systems has become one of the major challenges for high performance computing. In this work, we develop and prove correct a dynamic, robust and strongly consistent distributed shared memory suitable for handling large objects (such as files) and utilizing erasure coding. We do so by integrating an Adaptive, Reconfigurable, Atomic memory framework, called Ares, with the CoBFS framework, which relies on a block fragmentation technique to handle large objects. With the addition of Ares, we also enable the use of an erasure-coded algorithm to further split the data and to potentially improve storage efficiency at the replica servers and operation latency. Our development is complemented with an in-depth experimental evaluation on the Emulab and AWS EC2 testbeds, illustrating the benefits of our approach, as well as interesting tradeoffs