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About 10,000 papers for “Distributed computing”

Supervised Distributed Computing

John Augustine, Christian Scheideler, Julian Werthmann · ArXiv.org · 2025

We introduce a new framework for distributed computing that extends and refines the standard master-worker approach of scheduling multi-threaded computations. In this framework, there are different roles: a supervisor, a source, a target, and a collection of workers. Initially, the source stores some instance $I$ of a computational problem, and at the end, the target is supposed to store a correct solution $S(I)$ for that instance. We assume that the computation required for $S(I)$ can be modeled as a directed acyclic graph $G=(V,E)$, where $V$ is a set of tasks and $(v,w) \in E$ if and only if task $w$ needs information from task $v$ in order to be executed. Given $G$, the role of the supervisor is to schedule the execution of the tasks in $G$ by assigning them to the workers. If all workers are honest, information can be exchanged between the workers, and the workers have access to the source and target, the supervisor only needs to know $G$ to successfully schedule the computations. I.e., the supervisor does not have to handle any data itself like in standard master-worker approaches, which has the tremendous benefit that tasks can be run massively in parallel in large distributed environments without the supervisor becoming a bottleneck. But what if a constant fraction of the workers is adversarial? Interestingly, we show that under certain assumptions a data-agnostic scheduling approach would even work in an adversarial setting without (asymptotically) increasing the work required for communication and computations. We demonstrate the validity of these assumptions by presenting concrete solutions for supervised matrix multiplication and sorting.

Cited by 0Open access

Distributed Computing (MPI)

Charles Garrett · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016

This powerpoint is part of a Parallel Summer Computing Research Internship lecture on distributed computing (MPI). It goes over how MPI works and then shows examples.

Cited by 0Open access

Agentic Distributed Computing

Ajay D. Kshemkalyani, Manish Kumar, Anisur Rahaman Molla et al. · ArXiv.org · 2025

The most celebrated and extensively studied model of distributed computing is the {\em message-passing model,} in which each vertex/node of the (distributed network) graph corresponds to a static computational device that communicates with other devices through passing messages. In this paper, we consider the {\em agentic model} of distributed computing which extends the message-passing model in a new direction. In the agentic model, computational devices are modeled as relocatable or mobile computational devices (called agents in this paper), i.e., each vertex/node of the graph serves as a container for the devices, and hence communicating with another device requires relocating to the same node. We study two fundamental graph level tasks, leader election, and minimum spanning tree, in the agentic model, which will enhance our understanding of distributed computation across paradigms. The objective is to minimize both time and memory complexities. Following the literature, we consider the synchronous setting in which each agent performs its operations synchronously with others, and hence the time complexity can be measured in rounds. In this paper, we present two deterministic algorithms for leader election: one for the case of $k<n$ and another for the case of $k=n$, minimizing both time and memory complexities, where $k$ and $n$, respectively, are the number of agents and number of nodes of the graph. Using these leader election results, we develop deterministic algorithms for agents to construct a minimum spanning tree of the graph, minimizing both time and memory complexities. To the best of our knowledge, this is the first study of distributed graph level tasks in the agentic model with $k\leq n$. Previous studies only considered the case of $k=n$.

Cited by 0Open access

Distributed Computing Overview

Quoin · 1998

The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the abilityofmoving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field, code mobility is generating a growing body of scientific literature and industrial developments. Nevertheless, the field is still characterized by the lack of a sound and comprehensive body of concepts and terms. As a consequence, it is rather difficult to understand, assess, and compare the existing approaches. In turn, this limits our ability to fully exploit them in practice, and to f...

Cited by 0

Deploying Distributed Computing

Steve Sawyer, William Gibbons · IGI Global eBooks · 2000

This teaching case describes the efforts of one department in a large organization to migrate from an internally developed, mainframe-based, computing system to a system based on purchased software running on a client/server architecture. The case highlights issues with large scale software implementations such as those demanded by enterprise resource package (ERP) installations. Often, the ERP selected by an organization does not have all the required functionality. This demands purchasing and installing additional packages (known colloquially as “bolt-ons”) to provide the needed functionality. These implementations lead to issues regarding oversight of the technical architecture, both project and technology governance, and user department capability for managing the installation of new systems.

Cited by 0

Distributed Computing Infrastructures

Kassian Plankensteiner, Radu Prodan, Matthias Janetschek et al. · 2013

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Cited by 0

Distributed Computing Group

David Huber, Prof Dr, Roger Wattenhofer et al. · 2007

I would like to thank Prof. Roger Wattenhofer giving me the opportunity to make my master thesis in the Distributed Computing Group. Special thanks go to Olga Goussevskaia and Michael Kuhn, which supervised this thesis throughout the last six months, for their guidance, help and excellent support. Their office door was always open for me, from which I especially benefit during the second part of this thesis. Last but not least, I would like to thank my parents, Barbara and Martin Huber, for Many systems in the world can be usefully represented as networks. Examples are social networks, link structures in encyclopedias or the world wide web, or data structures in computer science. Embedding this networks into Euclidean space provides several advantages like visualization, fast distance calculations, a notion of direction, etc., and enables new applications. First, a generic and extensible framework, together with a graphical user interface, for embedding, analyzing, and visualizing graphs is presented. It enables a simple way of defining and plugging layout measurements to determine the quality of embeddings and be able to compare different embeddings. It helps finding

Cited by 0

in Distributed Computing

Can Use Grid · 2005

ed with application a i is equal to # i requests per second. The resource demand of application a i is characterized by the tuple (p i , d i , n i ), where p i is the application&amp;apos;s processing time at one of the servers in S, taken as a reference server; d i is the application&amp;apos;s I/O demand measured in seconds of disk service time; and n i is the application&amp;apos;s network demand measured in bps. Let&amp;apos;s consider that all servers have the same capacity. Relaxing this condition is pretty straightforward. Not all possible allocations are feasible. If the network demands of the applications allocated to a given server exceed that server&amp;apos;s bandwidth, for example, the allocation isn&amp;apos;t feasible. We express this constraint as , (1) where #(s j ) is the set of applications allocated to server s j . An allocation isn&amp;apos;t feasible if at least one of the applications&amp;apos; QoS goals is violated. Let r i be the average response time of application a i . Then, for any feasible allocation, the following relation

Cited by 0

Distributed computing manipulatives

Bouchard, David, S.M. Massachusetts Institute of Technology · DSpace@MIT (Massachusetts Institute of Technology) · 2007

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.

Cited by 0Open access

Distributed Computing Challenges

Mr. Bhushan Talekar, Miss Sonali Chaudhari, Prof. Vinayak Shinde et al. · IOSR Journal of Computer Engineering · 2014

In recent years, distributed computing systems has been widely explored in order to improve system performance in terms of scalability and reliability.A distributed computing system allows the user to share, select, and aggregate the distributed heterogeneous computational and storage resources, which are under the control of different sites or networks. A distributed system also provides solutions to the complex scientific or engineering problems, such as weather forecasting, medical diagnoses, and stock portfolio management. However, the distributed nature of the system also raises some serious challenges. This paper covers the major challenges of security& design.However, complete distributed nature of the system raises serious challenges in domains of security like Scheduling,Objective Function,Security and Trust and also in domain of design like Heterogeneity,Openness,Reliability and Fault Tolerance, etcIn this paper, we aim to identify the challenges of distributed systems.

Cited by 3Open access

Distributed Computing Group

Thomas Locher, Prof Dr, Roger Wattenhofer et al. · 2008

Peer-to-peer systems (p2p) are highly dynamic in nature and the topology of these networks changes rapidly. Such a system may consist of millions of peers joining only for a limited period of time, resulting in hundreds of join and leave events per second. While there has been a lot of research on peer-topeer computing over the last few years, we believe that the dynamics of such systems has not received the appropriate amount of attention. In this thesis, we introduce eQuus, a novel distributed hash table (DHT) suitable for highly dynamic environments. eQuus guarantees that lookups are always fast—as far as both the total number of hops as well as the effective distance traveled is concerned—, although peers may join and leave the network at any time and concurrently. All desirable properties such as a small peer degree, small network diameter and small stretch are maintained without incurring a large message

Cited by 0

Distributed Computing Group

Stefan Weber, Prof Dr, Roger Wattenhofer et al. · 2006

In this thesis, we present an approach to perform measurements in the worldwide research network PlanetLab. We describe a framework for developing and deploying Java applications on PlanetLab and show how it can be used to develop data gathering applications. In particular, we implement a latency and a bandwidth test of which we also present some ndings derived from its analyses.

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