Easy Consensus Algorithms for the Crash-Recovery Model

In the crash-recovery failure model of asynchronous distributed systems, processes can temporarily stop to execute steps and later restart their computation from a predefined local state. The crash-recovery model is much more realistic than the crash-stop failure model in which processes merely are allowed to stop executing steps. The additional complexity is reflected in the multitude of assumptions and the technical complexity of algorithms which have been developed for that model. We focus on the problem of consensus in the crash-recovery model, but instead of developing completely new algorithms from scratch, our approach aims at reusing existing crash-stop consensus algorithms in a modular way using the abstraction of failure detectors. As a result, we present three new and relatively simple consensus algorithms for the crash-recovery model for different types of assumptions

Distributed operating systems

In the past five years, distributed operating systems research has gone through a consolidation phase. On a large number of design issues there is now considerable consensus between different research groups.\ud \ud In this paper, an overview of recent research in distributed systems is given. In turn, the paper discusses overall system structure, protection issues, file system designs, problems and solutions for fault tolerance and a mechanism that is rapidly becoming very important for efficient distributed systems design: hints.\ud \ud An attempt was made to provide sufficient references to interesting research projects for the reader to find material for more detailed study

Agreement in wider environments with weaker assumptions.

The set agreement problem states that from n proposed values at most n?1 can be decided. Traditionally, this problem is solved using a failure detector in asynchronous systems where processes may crash but do not recover, where processes have different identities, and where all processes initially know the membership. In this paper we study the set agreement problem and the weakest failure detector L used to solve it in asynchronous message passing systems where processes may crash and recover, with homonyms (i.e., processes may have equal identities) and without a complete initial knowledge of the membership

Combining Blockchain and Swarm Robotics to Deploy Surveillance Missions

Current swarm robotics systems are not utilized as frequently in surveillance missions due to the limitations of the existing distributed systems\u27 designs. The main limitation of swarm robotics is the absence of a framework for robots to be self-governing, secure, and scalable. As of today, a swarm of robots is not able to communicate and perform tasks in transparent and autonomous ways. Many believe blockchain is the imminent future of distributed autonomous systems. A blockchain is a system of computers that stores and distributes data among all participants. Every single participant is a validator and protector of the data in the blockchain system. The data cannot be modified since all participants are storing and watching the same records. In this thesis, we will focus on blockchain applications in swarm robotics using Ethereum smart contracts because blockchain can make a swarm globally connected and secure. A decentralized application (DApp) is used to deploy surveillance missions. After mission deployment, the swarm uses blockchain to communicate and make decisions on appropriate tasks within Ethereum private networks. We set a test swarm robotics system and evaluate the blockchain for its performance, scalability, recoverability, and responsiveness. We conclude that, although blockchain enables a swarm to be globally connected and secure, there are performance limitations that can become a critical issue

Failure Detection and Consensus in the Crash-Recovery Model

We study the problems of failure detection and consensus in asynchronous systems in which processes may crash and recover, and links may lose messages. We first propose new failure detectors that are particularly suitable to the crash-recovery model. We next determine under what conditions stable storage is necessary to solve consensus in this model. Using the new failure detectors, we give two consensus algorithms that match these conditions: one requires stable storage and the other does not. Both algorithms tolerate link failures and are particularly efficient in the runs that are most likely in practice --- those with no failures or failure detector mistakes. In such runs, consensus is achieved within 3d time and with 4n messages, where d is the maximum message delay and n is the number of processes in the system

Set agreement and the loneliness failure detector in crash-recovery systems

The set agreement problem states that from n proposed values at most n-1 can be decided. Traditionally, this problem is solved using a failure detector in asynchronous systems where processes may crash but not recover, where processes have different identities, and where all processes initially know the membership. In this paper we study the set agreement problem and the weakest failure detector L used to solve it in asynchronous message passing systems where processes may crash and recover, with homonyms (i.e., processes may have equal identities) and without a complete initial knowledge of the membership

Distributed eventual leader election in the crash-recovery and general omission failure models.

102 p.Distributed applications are present in many aspects of everyday life. Banking, healthcare or transportation are examples of such applications. These applications are built on top of distributed systems. Roughly speaking, a distributed system is composed of a set of processes that collaborate among them to achieve a common goal. When building such systems, designers have to cope with several issues, such as different synchrony assumptions and failure occurrence. Distributed systems must ensure that the delivered service is trustworthy.Agreement problems compose a fundamental class of problems in distributed systems. All agreement problems follow the same pattern: all processes must agree on some common decision. Most of the agreement problems can be considered as a particular instance of the Consensus problem. Hence, they can be solved by reduction to consensus. However, a fundamental impossibility result, namely (FLP), states that in an asynchronous distributed system it is impossible to achieve consensus deterministically when at least one process may fail. A way to circumvent this obstacle is by using unreliable failure detectors. A failure detector allows to encapsulate synchrony assumptions of the system, providing (possibly incorrect) information about process failures. A particular failure detector, called Omega, has been shown to be the weakest failure detector for solving consensus with a majority of correct processes. Informally, Omega lies on providing an eventual leader election mechanism