Novel models and tools are required to support the engineering of systems that are self-aware of their current situations and capable of dynamically modifying their behavior and structure using ...feedback loops. In this paper, with the help of a case study in the area of e-mobility and based on our past work in self-adaptive systems modeling, we introduce a new tool (i.e., an Eclipse-based simulation plug-in) that we are developing for engineering and simulating architectural patterns based on feedback loops. Our plug-in can facilitate modeling of self-adaptive patterns using UML 2, visual animation of behavior to expose run-time information, animating composite structure, model-level debugging, simulating event-driven models, and run-time prompting.
Adding Byzantin tolerance to large scale distributed systems is considered non-practical. The time, message and space requirements are very high. Recently, researches have investigated the broadcast ...problem in the presence of a fl-local Byzantin adversary. The local adversary cannot control more than fl neighbors of any given node. This paper proves sufficient conditions as to when the synchronous Byzantinconsensus problem can be solved in the presence of a fl-local adversary.
Moreover, we show that for a family of graphs, the Byzantin consensus problem can be solved using a relatively small number of messages, and with time complexity proportional to the diameter of the network. Specifically, for a family of bounded-degree graphs with logarithmic diameter, O(logn) time and O(n logn) messages. Furthermore, our proposed solution requires constant memory space at each node.
Bizur is a consensus algorithm exposing a key-value interface. It is used by a distributed file-system that scales to 100s of servers, delivering millions of IOPS, both data and metadata, with ...consistent low-latency. Bizur is aimed for services that require strongly consistent state, but do not require a distributed log; for example, a distributed lock manager or a distributed service locator. By avoiding a distributed log scheme, Bizur outperforms distributed log based consensus algorithms, producing more IOPS and guaranteeing lower latencies during normal operation and especially during failures. Paxos-like algorithms (e.g., Zab and Raft) which are used by existing distributed file-systems, can have artificial contention points due to their dependence on a distributed log. The distributed log is needed when replicating a general service, but when the desired service is key-value based, the contention points created by the distributed log can be avoided. Bizur does exactly that, by reaching consensus independently on independent keys. This independence allows Bizur to handle failures more efficiently and to scale much better than other consensus algorithms, allowing the file-system that utilizes Bizur to scale with it.
Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, etc., can be ...formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods.
In this work, we extend the settings of the above approaches, by adding another dimension to the problem. Specifically, we are interested in self-stabilizing algorithms, that continuously run and converge to a solution from any initial state. This aspect of the problem is highly important due to the dynamic nature of the network and the frequent changes in the measured environment.
In this paper, we link together algorithms from two different domains. On the one hand, we use the rich linear algebra literature of linear iterative methods for solving systems of linear equations, which are naturally distributed with rapid convergence properties. On the other hand, we are interested in self-stabilizing algorithms, where the input to the computation is constantly changing, and we would like the algorithms to converge from any initial state. We propose a simple novel method called SS-Iterative as a self-stabilizing variant of the linear iterative methods. We prove that under mild conditions the self-stabilizing algorithm converges to a desired result. We further extend these results to handle the asynchronous case.
As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach.
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