Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

A Prototype of Lecturer Course Allocation System

This project is an implementation of a lecturer course allocation system (LECAS) which enables every lecturer can register courses by online as well as can view course schedule. The goal of is prototype is to provide the course schedules that would allow lectures to register courses that they plan to teach for the coming semester in the fast and possible ways. The objective of LECAS is to provide lecturers to get a convenient and efficient way during registering the courses. There are four users that can access the system such as; admin, lecturers, coordinator, and head department, each of them have different privileges in which they can access through internet and a web browser anytime and anywhere. Furthermore, this prototype has been developed by using java Servlet page (JSP) with MySQL for database. This prototype was developed based on the proposed requirement of system and was evaluated by using perceived usefulness and ease of use where the respondents are from University Utara Malaysia (UUM) lecturers. The feedbacks from 30 respondents about the system prototype are quite good which are showed with 5.46 grand mean for perceived usefulness factor and 5.37 grand mean for perceived ease of use factor with the scale of 7. Therefore, it can be concluded that the prototype is useful and easy to use. This prototype is expected to facilitate the other university to develop and enhance the system for lecturer registration time

Do product market regulations in upstream sectors curb productivity growth? Panel data evidence for OECD countries

The paper focuses on the influence of upstream competition for productivity outcomes in downstream sectors. This relation is illustrated with a neo-Schumpeterian theoretical model of innovation (Aghion et al., 1997) with market imperfections in the production of intermediate goods. In this context, upstream market imperfections create barriers to competition in downstream markets and upstream producers use their market power to share innovation rents sought by downstream firms. Thus, lack of competition in upstream markets curbs incentives to improve productivity downstream, negatively affecting productivity outcomes. We test this prediction by estimating an error correction model that differentiates the potential downstream effects of lack of upstream competition in situations close and far from the global technological frontier. We measure competition upstream with regulatory burden indicators derived from OECD data on sectoral product market regulation and the industry-level efficiency improvement and the distance to frontier variables by means of a multifactor productivity (MFP) index. Panel regressions are run for 15 OECD countries and 20 sectors over the 1985-2007 period with country, sector and year fixed effects. We find clear evidence that anticompetitive regulations in upstream sectors have curbed MFP growth downstream over the past 15 years. These effects tend to be strongest for observations (i.e. country/sector/period triads) that are close to the global technological frontier. Our results suggest that, measured at the average distance to frontier and average level of anticompetitive regulations, the marginal effect of increasing competition by easing such regulations is to increase MFP growth by between 1 and 1.5 per cent per year in the OECD countries covered by our sample. Our results are robust to changes in the way MFP and the regulatory burden indicators are constructed, as well as to variations in the sample of countries and/or sectors.Productivity, Growth, Regulations, Competition, Catch-up.

Integrated Analytical Hierarchy Process and Objective Matrix in Balanced Scorecard Dashboard Model for Performance Measurement

Measuring organizational performance is pivotal for a comprehensive understanding of strengths, weaknesses and to improve the quality of any organization’s performance. Balanced Scorecard (BSC) is the strategic evolution tool that is widely used to measure the organizational performances, and achievements from various aspects, both financial and non-financial. In this research, BSC was not only a straight jacket concept but also a high potential tool for measuring and managing tangible and accurate data through the application of several methods. This research weighted the variables of BSC based on significance values of Analytical Hierarchy Process (AHP) and Optimization of Measurement with Objective Matrix (OMAX). Moreover, a recommendation analysis was given based on the cause and effect analysis of variables and the achievement of Key Performance Indicators (KPIs). The flow of information, data, and performance measurement processes were designed into Business Intelligence (BI) software development i.e. BI-MonevDash. The framework and software BI-MonevDash proposed can be used as a new chosen tool for measuring and monitoring organizational performance. Recommendations could facilitate the leaders in decision making to improve the organizational performance and reduce risks

An Options-Based Method to Solve the Composability Problem in Sequential Auctions

Current auctions often expose bidding agents to two difficult, yet common, problems. First, bidding agents often have the opportunity to participate in successive auctions selling the same good, with no dominant bidding strategy in any single auction. Second, bidding agents often need to acquire a bundle of goods by bidding in multiple auctions, again with no dominant bidding strategy in any single auction. This paper introduces an options-based infrastructure that respects the autonomy of individual sellers but still enables bidders to utilize a dominant, truthful strategy across multiple auctions.Engineering and Applied Science

Applying revenue management to agent-based transportation planning

We consider a multi-company, less-than-truckload, dynamic VRP based on the concept of multi-agent systems. We focus on the intelligence of one vehicle agent and especially on its bidding strategy. We address the problem how to price loads that are offered in real-time such that available capacity is used in the most profitable way taking into account possible future revenues. We develop methods to price loads dynamically based on revenue management concepts.\ud We consider a one leg problem, i.e., a vehicle travels from i to j and can wait at most τ time units in which it can get additional loads from i to j. We develop a DP to price loads given a certain amount of remaining capacity and an expected number of auctions in the time-to-go. Because a DP might be impractical if parameters change frequently and bids has to be determined in real-time, we derived two approximations to speed up calculations. The performance of these approximations are compared with the performance of the DP. Besides we introduce a new measure to calculate the average vehicle utilisation in consolidated shipments. This measure can be calculated based on a limited amount of data and gives an indication of the efficiency of schedules and the performance of vehicles

Portfolio Analysis Models: A Review

This study, which is qualitative in nature, examined the concept of portfolio analysis with focus on business portfolio analysis. Four portfolio analysis models: Boston Consulting growth-share matrix, General Electric industry-attractiveness matrix, Shell directional policy matrix, and Arthur D. Little strategic condition matrix, were discussed in terms of their nature, characteristics, relevance and strategic implications to marketing and management. The limitations of each of these strategic tools were expounded and some newer variants of portfolio analysis models that emerged as a result of these limitations were briefly discussed. The study revealed that each of the four portfolio models has its own merits and demerits and none can be said to be superior as their proponents had advanced.  Instead, each can be applied depending on the need of the organization and the environment in which it operates and they could sometime be employed in an integrative manner. It also revealed that the newer variants though theoretically sound are not popular in marketing and management literatures and in practice.  The study concluded that most of the criticisms leveled against these strategic tools are as a result of the misplaced role these tools are expected to play.  Portfolio analysis, the study concluded is not to dictate or recommend strategic decision but to provide strategists with the data needed to making informed decision. Keywords: Portfolio analysis, Boston Consulting growth-share matrix, General Electric industry-attractiveness matrix, Shell directional policy matrix, Arthur D. Little strategic condition matri

Otimização do teor de corte e do sequenciamento de minas subterrâneas

Orientador: Priscila Cristina Berbert RampazzoDissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação CientíficaResumo: Métodos de lavra subterrânea são aplicados na extração de vários metais e minerais. O planejamento de métodos subterrâneos difere do planejamento de métodos de superfície pelo fato de que não é necessário extrair todas as áreas de produção dentro dos limites econômicos finais para se ter uma sequência factível, ou seja, nos métodos subterrâneos é fisicamente possível que algumas áreas permaneçam não lavradas mesmo estando dentro do limites econômicos finais. O planejamento estratégico é a área central do planejamento de longo prazo de uma mina e visa definir estratégias de escala de produção, métodos de lavra e de beneficiamento mineral, selecionar as áreas que serão lavradas e otimizar a sequência de lavra destas áreas de produção. Para garantir a viabilidade econômica do empreendimento, o planejamento estratégico deve considerar as características-chave dos empreendimentos de mineração, que são: a necessidade de capital intensivo, o longo período de retorno do investimento e o ativo (reserva) limitado. Essas características devem ser consideradas durante o processo de valoração de um empreendimento mineiro, que normalmente é feito através do cálculo do VPL, valor presente líquido. Dentre as principais alavancas do planejamento estratégico, o teor de corte utilizado na seleção dos blocos que serão lavrados e o sequenciamento de mina são os que geram maior número de opções, fazendo com que avaliações de cenários demandem muito tempo e se tornem inviáveis na prática dada a necessidade de respostas rápidas para tomadas de decisão. Neste trabalho, três diferentes modelos matemáticos são propostos para abordar, de forma conjunta, o problema da seleção dos blocos de lavra de uma mina subterrânea e a otimização do sequenciamento destes blocos. Tais modelos consideram o VPL como principal objetivo a ser maximizado e resultam no uso do teor de corte como fator que equilibra as capacidades de produção dos diferentes estágios de um sistema de mineração. A abordagem matemática adapta a modelagem clássica de problemas de sequenciamento considerando os blocos de lavra como tarefas e as atividades de escavação de galerias (desenvolvimento de acessos) e de produção de minério (lavra) como máquinas. Os modelos propostos são testados com base em casos reais, utilizando-se métodos de solução exata e um algoritmo genético. Os resultados computacionais mostram que o algoritmo genético é mais eficiente do que os métodos exatos, sobretudo para instâncias maiores, mais próximas da realidadeAbstract: Underground mining methods are used at the extraction of many metals and minerals. Underground mining planning differs from surface mining planning mainly because, in the first case, it is not necessary to extract all mining blocks within the ultimate economic limits to have a feasible sequence, i.e., it is physically possible to an underground mine to have some areas left \textit{in situ} even if they are inside the ultimate economic limits. Strategic planning is the core area of long-term mining planning and aims to define the scale of production, mining and processing methods, to select areas that will be mined, and to optimize the mining sequence. To guarantee the economic feasibility of a mining asset, strategic planners must also consider the key aspects of mining businesses, which are: capital-intensive requirements, long-term payback, and limited asset (reserves) life. These characteristics must be considered during the valuation process of a mining asset, which is normally conducted through NPV, net present value, calculations. Among the main strategic planning levers, cut-off grades (used at the selection of blocks that will be mined) and the mine sequencing are the ones that generate the greatest number of options. As scheduling multiple scenarios requires a great deal of time, this is infeasible in real situations given the need for quick responses. In this dissertation, three mathematical models are proposed to tackle, at the same time, two problems: the selection of the mining blocks in an underground mine, and the optimization of their sequence. These models consider NPV as the main objective to be maximized and result in using cut-off grades as a factor that balances the main capabilities of a mining system. The mathematical approach adapts classical scheduling models considering mining blocks as jobs; and tunnels excavation (access development) and ore production (mining) activities as machines. The proposed models are tested, with real cases, using exact-solution methods and a genetic algorithm. Results show that the genetic algorithm is more efficient than the exact methods, especially for greater instances that are similar to real problemsMestradoMatematica Aplicada e ComputacionalMestre em Matemática Aplicada e Computaciona