Capacity planning for large scale people movement: Models and application to the Ujjain Kumbh Mela

Computational simulation is a powerful tool for performance evaluation of computational systems. It is useful to make capacity planning of data center clusters, to obtain profiling reports of software applications and to detect bottlenecks. It has been used in different research areas like large scale Web search engines, natural disaster evacuations, computational biology, human behavior and tendency, among many others. However, properly tuning the parameters of the simulators, defining the scenarios to be simulated and collecting the data traces is not an easy task. It is an incremental process which requires constantly comparing the estimated metrics and the flow of simulated actions against real data. In this work, we present an experimental framework designed for the development of large scale simulations of two applications used upon the occurrence of a natural disaster strikes. The first one is a social application aimed to register volunteers and manage emergency campaigns and tasks. The second one is a benchmark application a data repository named MongoDB. The applications are deployed in a distributed platform which combines different technologies like a Proxy, a Containers Orchestrator, Containers and a NoSQL Database. We simulate both applications and the architecture platform. We validate our simulators using real traces collected during simulacrums of emergency situations.

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Integration of Fleet Assignment and Aircraft Routing

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105191500110 ◽

2005 ◽

Vol 1915 (1) ◽

pp. 79-84

Author(s):

Yihua Li ◽

Xiubin Wang

Keyword(s):

Capacity Planning ◽

Large Scale ◽

Numerical Test ◽

Real Data ◽

Fleet Assignment ◽

Significant Cost ◽

Sequential Method ◽

Aircraft Routing ◽

Assignment Model ◽

Real World Problems

Fleet assignment and aircraft routing are two sequential steps in airline capacity planning. The fleet assignment model allots the scheduled flights covered by a type of aircraft on the basis of aircraft availability, and the aircraft routing model generates a route for each particular aircraft to ensure that the path-specific requirements for maintenance and connection times are satisfied. Although it is known that the sequential method is not able to minimize the overall cost, no results have been reported on the integration of the two steps. Here these two steps are completed simultaneously. A path-based integrated model is presented and tested on real data. A heuristic is proposed to solve the formulation. The numerical test indicates that a significant cost saving can be achieved and that the heuristic shows encouraging promise in solving large-scale real-world problems.

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Visualizing Large-Scale Streaming Applications

Information Visualization ◽

10.1057/ivs.2009.5 ◽

2009 ◽

Vol 8 (2) ◽

pp. 87-106 ◽

Cited By ~ 6

Author(s):

Wim De Pauw ◽

Henrique Andrade

Keyword(s):

Performance Optimization ◽

Capacity Planning ◽

Large Scale ◽

Stream Processing ◽

Trading Systems ◽

Streaming Applications ◽

Computing Paradigm ◽

Management Of Resources ◽

Real Time Visualization ◽

Adaptive Nature

Stream processing is a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (for example, environment monitoring), to business intelligence (for example, fraud detection and trend analysis), to financial markets (for example, algorithmic trading systems). In this paper we describe Streamsight, a new visualization tool built to examine, monitor and help understand the dynamic behavior of streaming applications. Streamsight can handle the complex, distributed and large-scale nature of stream processing applications by using hierarchical graphs, multi-perspective visualizations, and de-cluttering strategies. To address the dynamic and adaptive nature of these applications, Streamsight also provides real-time visualization as well as the capability to record and replay. All these features are used for debugging, for performance optimization, and for management of resources, including capacity planning. More than 100 developers, both inside and outside IBM, have been using Streamsight to help design and implement large-scale stream processing applications.

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Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors

Energies ◽

10.3390/en13133327 ◽

2020 ◽

Vol 13 (13) ◽

pp. 3327

Author(s):

Victor H. Hinojosa ◽

Joaquín Sepúlveda

Keyword(s):

Power Systems ◽

Capacity Planning ◽

Large Scale ◽

Solution Space ◽

Transmission Capacity ◽

Planning Problem ◽

Transmission Planning ◽

Stochastic Generation ◽

Generalized Shift ◽

Planning Methodology

In this study, we successfully develop the transmission planning problem of large-scale power systems based on generalized shift-factors. These distribution factors produce a reduced solution space which does not need the voltage bus angles to model new transmission investments. The introduced formulation copes with the stochastic generation and transmission capacity expansion planning problem modeling the operational problem using a 24-hourly load behaviour. Results show that this formulation achieves an important reduction of decision variables and constraints in comparison with the classical disjunctive transmission planning methodology known as the Big M formulation without sacrificing optimality. We test both the introduced and the Big M formulations to find out convergence and time performance using a commercial solver. Finally, several test power systems and extensive computational experiments are conducted to assess the capacity planning methodology. Solving deterministic and stochastic problems, we demonstrate a prominent reduction in the solver simulation time especially with large-scale power systems.

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The Siting and Sizing Problem of Distributed Generation Based on a Novel Fractional Particle Swarm Optimization Algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202125701036 ◽

2021 ◽

Vol 257 ◽

pp. 01036

Author(s):

Fengming Zhang ◽

Lingyan Que ◽

Xinxin Zhang ◽

Fumian Wang ◽

Bing Wang

Keyword(s):

Particle Swarm Optimization ◽

Distributed Generation ◽

Optimization Algorithm ◽

Capacity Planning ◽

Large Scale ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Environmental Benefits ◽

Swarm Optimization

Large-scale distributed generation grid-connection brings huge economic and environmental benefits, but also threatens the stability of the grid. To make the grid consume a higher proportion of distributed generation, it is necessary to optimize the location and capacity of the distributed generation connected to the grid. Firstly, the uncertainty analysis model of wind speed, illumination intensity, and load of grid is established. Secondly, a distributed generation location and capacity planning model with the lowest annual comprehensive cost as the objective function is constructed. Then, a novel fractional particle swarm optimization algorithm is proposed, and the performance of the algorithm on complex optimization problems is tested. Finally, the simulation results of the IEEE 33-bus system example verify the rationality of the established model and the effectiveness of the proposed algorithm.

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