Computational Mobile Grid

2011 ◽  
pp. 40-51 ◽  
Author(s):  
Deo Prakash Vidyarthi
Keyword(s):  
Mobile Devices ◽  
Computational Grid ◽  
Web Portal ◽  
Short Term ◽  
Computing Power ◽  
Mobile Grid

The proliferation of the capable mobile devices has given the opportunity to utilize these devices for various purposes. The mobile devices being used as a Web portal is its short-term use as these devices have added many features and facility that does not only facilitate communication, but also adds to the huge computing power put together. The chapter proposes how a huge computational grid of these compute capable mobile devices can be formed, and the computing power from such a grid can be extracted. This kind of computational mobile grid put fourth many issues that require great attention before such a concept is fully functional.

Visualising Data for Smart Cities

2017 ◽  
pp. 453-475
Author(s):  
Michael Batty ◽  
Andrew Hudson-Smith ◽  
Stephan Hugel ◽  
Flora Roumpani
Keyword(s):  
Social Media ◽  
Mobile Devices ◽  
Smart City ◽  
Smart Cities ◽  
Short Term ◽  
Data Infrastructures ◽  
The City

This chapter introduces a range of analytics being used to understand the smart city, which depends on data that can primarily be understood using new kinds of scientific visualisation. We focus on short term routine functions that take place in cities which are being rapidly automated through various kinds of sensors, embedded into the physical fabric of the city itself or being accessed from mobile devices. We first outline a concept of the smart city, arguing that there is a major distinction between the ways in which technologies are being used to look at the short and long terms structure of cities, and we then focus on the shorter term, first examining the immediate visualisation of data through dashboards, then examining data infrastructures such as map portals, and finally introducing new ways of visualising social media which enable us to elicit the power of the crowd in providing and supplying data. We conclude with a brief focus on how new urban analytics is emerging to make sense of these developments.

A Mobile Cloud Middleware to Support Mobility and Cloud Interoperability

2016 ◽  
Vol 7 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Khadija Akherfi ◽  
Hamid Harroud ◽  
Michael Gerndt
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Relevant Information ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Computing Power ◽  
Cloud Interoperability

With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, the authors first present the specific concerns and key challenges in mobile cloud computing. They then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.

Increasing the Performability of Wireless Web Services

2009 ◽  
pp. 518-528
Author(s):  
Wenbing Zhao
Keyword(s):  
Wireless Communication ◽  
Web Services ◽  
Mobile Devices ◽  
Future Research ◽  
Battery Life ◽  
Research Directions ◽  
Computing Power ◽  
Network Bandwidth ◽  
Technical Issues ◽  
Future Research Directions

Wireless Web services are becoming a reality, if they have not already. The unique characteristics of the mobile devices and wireless communication medium, such as limited computing power, limited network bandwidth, limited battery life, unpredictable online time, mobility, and so forth,, imply that the infrastructure for wireless Web services will be very different from its wired counterpart. This chapter discusses the challenges and the stateof- the-art solutions to ensure highly performable wireless Web services. In particular, this chapter’s focus is on three technical issues: optimization of the wireless Web services messaging protocol, caching, and fault tolerance. Finally, limitations of the current approaches and an outline of future research directions on wireless Web services are also discussed.

Dynamic Network Optimization for Effective Qos Support in Large Grid Infrastructures

2011 ◽  
pp. 28-45
Author(s):  
Francesco Palmieri ◽  
Ugo Fiore
Keyword(s):  
Communication Networks ◽  
Electrical Power ◽  
Dynamic Network ◽  
Service Level ◽  
Computational Grid ◽  
Computational Grids ◽  
Collaborative Computing ◽  
Software Infrastructure ◽  
Remarkable Change ◽  
Computing Power

In the past decade there has been a remarkable change from mainframe-based centralized computing to a distributed client/server approach. In the coming decade this trend is likely to continue with further shifts towards network centric collaborative computing. At the state of the art, the key technology in collaborative computing is the computational grid paradigm. Like an electrical power grid, the computational Grid will aim to provide a steady, reliable source of computing power. More precisely, the term grid is now adopted to designate a common computational and/or data processing infrastructure built on distributed resources, highly heterogeneous (in their role, computing power and architecture), interconnected by heterogeneous communication networks and communicating through some basic services realized by a middleware stratum that offers a reliable, simple, uniform and often transparent interface to its resources such that an unaware user can submit jobs to the Grid just as if he/she was facing a large virtual supercomputer, so that large computing endeavors, consisting of one or more related jobs or tasks, are then transparently distributed over the network on the available computing resources. Such a workload distribution strategy, that is, to balance the tasks on different idle computers on the underlying networks, is the most important functionality in computational Grids, usually provided at the service level of the grid software infrastructure.

A Mobility-Aware Task Scheduling Model in Mobile Grid

2013 ◽  
Vol 336-338 ◽  
pp. 1786-1791 ◽  
Author(s):  
Yong Qiang Xu ◽  
Ming Yin
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Task Scheduling ◽  
Average Distance ◽  
Base Station ◽  
The Other ◽  
Mobile Grid ◽  
Scheduling Model ◽  
Energy Constrained ◽  
Mobile Grids

The mobile grids bring some additional features into the grid, such as mobility, energy-constrained, etc. And the task scheduling becomes a more challenge thing. We propose a mobile grid task scheduling model considering the mobility of both user and resource, and the resource energy consumption. Through analyzing the architecture of mobile grid, a mathematical model is built to calculate the average distance between the resource and Base Station (BS). Then, it can decide which mobile grid the mobile devices are apt to stay in, which can deal with the mobility of mobile devices. On the other hand, the resource energy consumption is also considered, which ensure that the resources have enough energy to finish the task. As a result, the task can be assigned to the best resources in the suitable mobile grids. The failures may happen in the task scheduling because of many unpredictable factors. So the fault-tolerance scheme based on the notion of replication is proposed.

scholarly journals Forecasting the volatility of Financial Time Series by Tree Ensembles

2019 ◽  
Vol 12 (3) ◽  
pp. 82-89
Author(s):  
O. S. Vidmant
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Capital Investment ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Time Interval ◽  
Short Term ◽  
Computing Power ◽  
Financial Time

The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. The article discusses the possibility of improving the efficiency of calculations in predicting the volatility by the models of tree ensembles using various methods of data analysis. As the key points of efficiency growth, the author studied the possibility of aggregation of financial time series data using several methods of calculation and prediction of variance: Standard, EWMA, ARCH, GARCH, and also analyzed the possibility of simplifying the calculations while reducing the correlation between the series. The author demonstrated the application of calculation methods on the basis of an array of historical price data (Open, High, Low, Close) and volume indicators (Volumes) of futures trading on the RTS index with a five-minute time interval and an annual set of historical data. The proposed method allows to reduce the cost of computing power and time for data processing in the analysis of short-term positions in the financial markets and to identify risks with a certain level of confidence probability.

Resource Allocation and Provisioning in Computational Mobile Grid

2015 ◽  
Vol 6 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Dinesh Prasad Sahu ◽  
Karan Singh ◽  
Shiv Prakash
Keyword(s):  
Resource Allocation ◽  
Mobile Devices ◽  
Mobile Agent ◽  
Fixed Number ◽  
Mobile Grid ◽  
Java Language ◽  
Computational Capability ◽  
Proposed Model ◽  
Drastic Increase

Recent years have seen drastic increase in number of mobile devices which are becoming popular not only by their communication flexibility but also for their computational capability. A collection of mobile devices together form a grid. In the proposed model, it is assumed that the set of jobs are accumulated to the primary machine, though they might have been submitted anywhere in the grid. It is also assumed that each job consists of one or more number of sub jobs. Mobile Grid comprises with number of machines and speed of execution of individual processor may be different. Each machine can handle fixed number of sub jobs. A set of jobs accumulated at the primary machines are distributed to different secondary machines. A rigorous set of experiment has been carried out by simulating the model using java language on Eclipse IDE integrated with Gridsim. The model has been tested with various numbers of inputs in different cases and result has been observed. The authors found some of the key findings of the experiments. In most of the cases, resource allocation is better when mobile agent is employed for the work.

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