Pleroma

Tariq, Muhammad; Koldehofe, Boris; Bhowmik, Sukanya; Rothermel, Kurt

doi:10.1145/2663165.2663338

Cited by 35 publications

(1 citation statement)

References 18 publications

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“…Retrieving information from a distributed system is a well-studied research problem [20,25,21,13,22,24,28,27]. In this section, we review the related work in a chronological order.…”

Section: Related Workmentioning

confidence: 99%

Utilizing the Hive Mind – How to Manage Knowledge in Fully Distributed Environments

Bach

Tariq

Mayer

et al. 2015

Lecture Notes in Computer Science

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By 2020, the Internet of Things will consist of 26 Billion connected devices. All these devices will be collecting an innumerable amount of raw observations, for example, GPS positions or communication patterns. In order to benefit from this enormous amount of information, machine learning algorithms are used to derive knowledge from the gathered observations. This benefit can be increased further, if the devices are enabled to collaborate by sharing gathered knowledge. In a massively distributed environment, this is not an easy task, as the knowledge on each device can be very heterogeneous and based on a different amount of observations in diverse contexts. In this paper, we propose two strategies to route a query for specific knowledge to a device that can answer it with high confidence. To that end, we developed a confidence metric that takes the number and variance of the observations of a device into account. Our routing strategies are based on local routing tables that can either be learned from previous queries over time or actively maintained by interchanging knowledge models. We evaluated both routing strategies on real world and synthetic data. Our evaluations show that the knowledge retrieved by the presented approaches is up to 96.7% as accurate as the global optimum.

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“…Retrieving information from a distributed system is a well-studied research problem [20,25,21,13,22,24,28,27]. In this section, we review the related work in a chronological order.…”

Section: Related Workmentioning

confidence: 99%

Utilizing the Hive Mind – How to Manage Knowledge in Fully Distributed Environments

Bach

Tariq

Mayer

et al. 2015

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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A general‐purpose distributed computing Java middleware

Almeida

Cimino

Resende

et al. 2018

Concurrency and Computation

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Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

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