Search results for "hajautetut järjestelmät"

A Survey on Technologies Which Make Bitcoin Greener or More Justified

2022

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactio…

AMQP:n hyödyntäminen mobiileissa hajautetuissa järjestelmissä

2012

Hajautettujen järjestelmien määrä kasvaa pilvipalveluiden ja virtualisoinnin suosion myötä. Kommunikointi hajautetuissa järjestelmissä tapahtuu tyypillisesti erilaisten väliohjelmien avulla. Mobiilien laitteiden yleistyessä tulee ottaa huomioon laitteiden vaatimukset uusia kommunikointiratkaisuja toteuttaessa. AMQP on eräs sovellusprotokolla, jonka avulla ohjelmat voivat kommunikoida keskenään viesteillä. Tässä tutkielmassa tarkastellaan mobiilien hajautettujen järjestelmien haasteita, ja kuinka hyvin AMQP:lla pystytään ratkaisemaan niitä. The amount of distributed systems increases along with the popularity of cloud computing and virtualization. The communication in distributed systems typ…

Requirements for Energy Efficient Edge Computing: A Survey

2018

Internet of Things is evolving heavily in these times. One of the major obstacle is energy consumption in the IoT devices (sensor nodes and wireless gateways). The IoT devices are often battery powered wireless devices and thus reducing the energy consumption in these devices is essential to lengthen the lifetime of the device without battery change. It is possible to lengthen battery lifetime by efficient but lightweight sensor data analysis in close proximity of the sensor. Performing part of the sensor data analysis in the end device can reduce the amount of data needed to transmit wirelessly. Transmitting data wirelessly is very energy consuming task. At the same time, the privacy and s…

Migrating from a Centralized Data Warehouse to a Decentralized Data Platform Architecture

2021

To an increasing degree, data is a driving force for digitization, and hence also a key asset for numerous companies. In many businesses, various sources of data exist, which are isolated from one another in different domains, across a heterogeneous application landscape. Well-known centralized solution technologies, such as data warehouses and data lakes, exist to integrate data into one system, but they do not always scale well. Therefore, robust and decentralized ways to manage data can provide the companies with better value give companies a competitive edge over a single central repository. In this paper, we address why and when a monolithic data storage should be decentralized for imp…

Latency-Oblivious Distributed Task Scheduling for Mobile Edge Computing

2018

Mobile Edge Computing (MEC) is emerging as one of the effective platforms for offloading the resource- and latency-constrained computational services of modern mobile applications. For latency- and resource-constrained mobile devices, the important issues include: 1) minimize end-to-end service latency; 2) minimize service completion time; 3) high quality-of-service (QoS) requirement to offload the complex computational services. To address the above issues, a latencyoblivious distributed task scheduling scheme is designed in this work to maximize the QoS performance and goodput for the MEC services. Unlike most of the existing works, we consider the latency-oblivious property of different …

Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…

Oliopohjaisten väliohjelmistojen rakenne ja tekninen toiminta

2003

Modeling and Mitigating Errors in Belief Propagation for Distributed Detection

2021

We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distribute…

Challenges in Geographically Distributed Information System Development : A Case Study

2021

Geographically distributed information system development (ISD) projects are more and more common, especially among organisations operating in global markets. Distributed ISD yields potential competitive advantages by developing new products near the target markets, utilizing global labour markets, and exposing the organisation to innovations, ideas and new paradigms. However, distributed ISD also presents challenges and problems which organisations must take into consideration. The pivotal challenge is usually communication. People working on the same project in different locations find it difficult to communicate due to lack of formal and informal face-to-face communication, different wor…

Agents driven smart sensors

2017

Any physical area (like schools, home, hospitals etc.) that uses either mobile devices, sensors, embedded systems or computers to gather information from the users and the environment and eventually, adapt according to the new information gained. [1] Smart spaces compromises of heterogeneous hardware which often leads to the issues of interoperability. One way of reducing the heterogeneity between the sensors is to introduce the semantic interface as sensors default interface. Semantic Web provides a common interface and for-mat for data representation so that one can decrease the heterogeneity of data and increase data reusability. [2] With the Smart Spaces, it is important to use only the…