Florin Pop

HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

Today, there is a substantial need for population evolution modeling in multidisciplinary research areas, such as social sciences (sociology, anthropology etc.), which can neither be solved formally, nor empirically at global scale, thus requiring the development of heuristic techniques, like evolutionary algorithms. Therefore, existent methodologies of social simulation can be extended from microenvironments to large scale modeling of extremely complex systems, as it is the case of human evolution. The modeling of population evolution prediction is currently used in high interest areas, from migration flows, to financial crisis simulation, to the free-market economy models, as well as multi-national family dynamics and cultural aspects. The high performance of evolutionary computing has been already proven in the context of virus population evolution, be they biological or cybernetic, thus making them the perfect method for our endeavour. This paper presents the design and implementation of a soft computing application, namely HELGA—heterogeneous encoding lifelike genetic algorithm. HELGA was designed for the modeling and simulation of Earth population evolution, on a global scale, throughout multiple historical eras. The model shows the tendency of human evolution towards one mixed race. The algorithm takes into account the influence of social factors, such as the Industrial Revolution or the discovery of the New World. Our method, for which all constraints have been based on validated research, has aligned precisely to real historical events: it anticipated the end of antiquity, the industrial era, the baby boom phenomenon and so on. For example, the poor health conditions of the Middle Ages have caused a drastic drop in population size. The presented model shows the valiant result of one final race, and although this theory cannot be formally proved in the present, we consider that our results can be used as hypothesis for future social science research. Also, HELGA can be extended with new capabilities and constraints.

Enabling Mobile Cloud Wide Spread Through an Evolutionary Market-Based Approach

Mobile clouds are an ongoing research topic that has yet to become ubiquitous as the now popular cloud paradigm. This is because of a number of issues with mobile clouds that still need to be addressed such as: incentives, security, privacy, context, data management, usability, and cost benefits. Out of these issues, the most important one that needs to be addressed is the issue of incentives, without which mobile clouds cannot gain enough users for the concept to be useful. Unlike public, company-owned cloud systems, in mobile clouds, the amount of resources or processing power is directly dependent on mobile cloud users that are in the proximity of the individual that requires extra resources. With an increase in the number of mobile cloud users willing to share resources or willing to use the service offered by others, comes an increase in the likeliness that enough mobile-cloud-enabled devices will be available. In this paper, we study incentives for mobile cloud systems and consider as a solution an evolutionary market-based approach to create these incentives. Creating a market for these systems is particularly difficult because of the large number of individuals that need to be involved and their high mobility.

Cyber Defence Capabilities in Complex Networks

This chapter presents a quick overview about the existing cyber Defence capabilities and cyber ranges in complex networks where operations testbeds meant to bring improvements in cyber security training. The current chapter gives a brief on the problem area where cyber developments within the Ministry of Defence (MoD) are introduced along with the test range. The research goal is introduced as well as the study limitations and desired results. The chapter ends with some recommendations and suggestions that the researcher came up with based on the results of the study for complex networks.

Adapting MCP and HLFET Algorithms to Multiple Simultaneous Scheduling

We deal with the following scheduling problem: an infinite number of tasks must be scheduled for processing on a finite number of heterogeneous machines, such as all tasks are sent to execution with a minimum delay. The tasks have causal dependencies and are generated in the context of biomedical applications, and produce results relevant for the medical domain, such as diagnosis support or drug dose adjust measures. The proposed scheduling model had a starting point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Several steps were added to the original implementation along with a merge stage in order to combine the results obtained for each of the previously scheduled tasks. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms.

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

In the last years there has been considerable interest in using distributed systems, especially Cloud Systems, in any domains. Resource management contributes to ensure quality of services for any type of application, especially when a system involves elements of heterogeneity characterized by a variety of resources that may or may not be coupled with specific platforms or environments. A problem very close to the industry is the capability to allocate resources in an efficient way and estimate costs, especially when switching from one provider to another. In this chapter we present an extended work oriented on agreement–based resource allocation and a scheduling algorithm, aimed to bring an adaptive fault tolerant distributed system. For the agreement protocol we describe and analyze a 3-Tier structure of resources (hosts and virtual machines). Then an adaptive mechanism for agreement establishment is described. The allocation method considers workload distribution, resources heterogeneity, transparency, adaptability and also the ease to extend by combining with other scheduling algorithms.

Electric Mobility in Green and Smart Cities

Because the electric mobility has its focus on eco-friendly means of transport, a distributed platform designed for a smart city environment that can manage the electrical charging stations is vital. Even though there is only one de facto protocol that is widely implemented, there are many vendors who have chosen different interfaces, protocols and implementations. The purpose of this paper is to present a flexible architecture of a distributed system that can face the addition of new features, interfaces, protocols with focus on REST over WebSocket, a good alternative in terms of efficiency, traffic and costs. The proposed platform is distributed, being divided into several components, each responsible for dedicated parts of the communication and business logic. Our solution is an interactive networked environment for green and smart cities offering flexible support to electrical vehicles management, with a high impact in decarbonizing transport and in keeping the environment clean.

Advance Multispectral Analysis for Segmentation of Satellite Image

This paper presents an application with record performance for electromagnetic spectrum analysis of multispectral satellite image. The analysis method is an application-specific pixels oriented to images segmentation. This kind of segmentation is used in remote sensing for land cover and land use classification and change detection. Regions of the image are clustered separately and then the results are combined, for this the processing method employs two types of clustering algorithms, each specialized to its task and steered towards obtaining a final meaningful segmentation. The results show good spatial coherency in segments and coherent borders between regions that were segmented separately.

Monitoring-based auto-scalability across hybrid clouds

Cloud computing is a relatively new type of Internet-based computing that becomes more and more popular. Using methods like virtualization, adopting architectures based on microservices, automation of building and deployment processes, Cloud could provide on-demand resources and scale them accordingly to a Service Level Agreement (SLA). Even if there are a lot of Cloud computing providers (like Amazon AWS, Microsoft Azure, IBM, Google etc.), many of them providing advanced services, already deployed technologies, auto-scaling and monitoring tools, on the long run a private Cloud could cost less than a public Cloud. On the other hand, a private Cloud has the disadvantage of lacking hardware resources for scaling an unexpected spike of usage or periodical growths in the number of requests. This problem could be solved using a public Cloud provider API for extending the hardware by buying and selling resources, using a pay-per-use or a pay-as-you-go model. This paper describes and evaluates a private Cloud architecture capable of on demand deployment of microservices based applications, monitoring every instance using CPU, memory, networking and disk data collectors software, monitoring every task queue and horizontally auto-scaling the number of instances per service. Using Cloud monitoring data, SLA and budget constraints we decide if a new instance should be spawned or terminated in the private Cloud or in a public Cloud. From the customer point of view the process of auto-scaling across the hybrid Cloud is transparent, but requires a horizontally scalable service. We will illustrate our work running an application packaged as microservices in containers in a Kubernetes private Cloud that will be extended using public resources from AWS. The results of the paper offers an architectural model that can be used to build an in-house, scalable Cloud.

Evaluation of Cloud Systems

Modelling and simulation represent suitable instruments for evaluation of distributed system. These essential tools in science are used in Cloud systems design and performance evaluation. The chapter covers the fundamental skills for a practitioner working in the field of Cloud Systems to have, for the development of a correct methodology for the evaluation using simulation of Cloud services and components. We concentrate on subjects related to tasks scheduling and resource allocation with the focus on scalability and elasticity, the constraints imposed by SLA and the use of CloudSim for performance evaluation of Cloud Systems. Several metrics used in modelling and simulation are presented in this chapter.

SCADA Security: Concepts and Recommendations

SCADA systems are the computers that control essential, complicated, and often dangerous physical processes, many of which constitute the physical infrastructure critical to modern societies. These physical processes are powerful tools, and their misuse generally has unacceptable consequences. Preventing such abuse is the goal of SCADA security. To understand misuse and how to avoid it, we need some understanding of what a SCADA system is, and how it works under different forms of attacks. These are the primary objectives of this paper.