InFeMo: Flexible Big Data Management Through a Federated Cloud System

Mobile computing provides new ways to help people in daily routine through ubiquituos, collaborative and social computing. Herein, we combine collaboration with ubiquitous and pervasive sensing to provide a service that helps on prediting waiting time in service queues. We argue that combining GPS information with collaboration can improve queue waiting time prediction. Our experimental evaluation uses an actual minimarket service queue, achieving a mean error of 59.56 seconds for predicting queue time in queues with observed waiting time from 120 to 240 seconds. Our results show that this tool could be useful and can be extended for other different classes of service queues.

In business decision-making, the queuing system is one of the options employed to satisfy the demands of a particular service system for resource needs. The queuing theory has been used successfully by various business service industries. There are many types of queuing systems that can predict waiting times and queue lengths. The First In First Out (FIFO) algorithm works like the queue principle, where the first customer to arrive is served first. To mitigate the negative effects of waiting, service managers have implemented various solutions, such as informing customers of the wait length, reducing waiting time, and offering entertainment options like television during the wait. Service operations managers continually strive to reduce customer waiting times to enhance customer satisfaction, which can be achieved through real waiting time reduction or improvement in the customer waiting for the experience. This study presents a simulation of a mobile-based bank teller application for managing customer wait times. The application is built using back-end and cloud computing and is based on a FIFO algorithm. It has been shown that the proposed system is efficient and contributes to the real workflow process.

This paper describes a system for improving user experience when accessing overloaded web servers. While several techniques exist today to build high-capacity web servers, little attention is paid to the fact that servers often crash when faced with transient overload, causing user experience to degrade sharply when incoming load exceeds capacity. Existing overload control mechanisms focus on some form of admission control to protect the web server from overload. However, all such techniques result in user requests failing, either due to timing out or receiving a service unavailable message. More importantly, there is no feedback to the frustrated user about when to retry again, leading to adhoc retries. This paper describes WebQ, a system consisting of two web proxies, that together simulate a virtual queue of web requests, and shape incoming load to match server capacity. Users accessing a server protected by WebQ receive a HTTP redirect response specifying a wait time in the virtual queue, and are automatically redirected to the web server upon expiration of the wait time. The wait times are calculated using an estimate of the server's capacity that is computed by WebQ. Users in the virtual queue are provided with a secure cryptographic token, which is checked to guarantee that the user has waited his prescribed time in the queue. We evaluate the ideas of WebQ using a real prototype implementation. Our experiments show that, with WebQ in place, users experience zero server failures and significantly better response times from a web server, even when the peak load is several times the provisioned capacity.

Endogenous materials open a learning window toward contemporary architects to use ancient wisdom to develop a more sustainable architecture. Regarding to aim of this research, this paper is to analyze interaction of adoption of ancient materials and a more efficient architectural space. The result of paper shows that the usage of natural materials in rock cu architecture of Kandovan can be considered as a way to a more energy efficient architecture which presents interaction of energy efficient materials and sustainable architecture.

Cloud Computing offers services over the Internet to support business processes, based on deployment models and service, meet business requirements in an efficient and cost-effective. A general context of the types of service models that it, as well as the models of deployment, are not known, so the following research questions are stated: Q1) How many studies refer to service models in Cloud Computing and Models of cloud computing deployment?, Q2) How the service models are classified in relation to the types of capabilities Application, Infrastructure y Platform in a Cloud?, and Q3) What types of cloud computing deployment models currently exist?. The objective of this paper is to investigate the service and deployment models that currently exist in cloud computing, for every which a process of systematic review of the literature has been used as a research methodology. The results show that 45 service models and 4 deployment models were found in Cloud Computing, this allows us to conclude that the offered models give a lot and diverse solutions for the business processes.

Smart transportation network development with environmental issues into consideration has brought Industry 4.0 based solutions on priority. In this direction, battery-powered electric bus systems have been considered widely for ensuring flexibility, operation cost, and lesser pollutants emission. Industry 4.0 provides automation through a cyber-physical system (CPS), the interconnection of bus system entities with industrial internet-of-things (IIoT), remote information availability through cloud computing and scientific disciplines (human-computer interaction, artificial intelligence, machine learning etc.) integration. In this work, a discrete event-based simulation-optimization approach is integrated that take care of bus energy consumption according to real-time city's passenger needs and on-road friction levels. The proposed simulation optimization methodology utilizes multi-objective with dependent and independent variables for optimizing the overall system performance. In simulation optimization, objective functions are designed to tackle battery consumption, Internet-of-Thing (IoT) network performance, cloud operations efficiency and smart scientific discipline integration. Simulation parameters are based on a real-time bus system which is further analyzed, filtered and adapted as per the needs of the system. In another analysis, supercharger's capacities are varied to evaluate the performance of the proposed system and identify the low cost and efficient smart transportation system. Simulation results show different scenarios for variations in the number of buses, charging stations, bus-depots, mobile charging facilities, and bus-schedules. Simulation results show that the average passenger's waiting time in the waiting is (after ticket booking) varies between 0.2 minutes to 0.7 minutes in real-time traffic conditions. In similar traffic conditions, total passenger's time in system (ticket booking to travel) varies between 41.6 minutes (for 24 hours) to 45.5 minutes (for 1 year). In the simulation, priorities are given to those dependent and independent variables which save the battery consumption and elongate the utilization of buses. Lastly, it is also observed that the proposed system is suitable for resource-constraint devices because Gate Equivalent (GE) calculation shows that the proposed system can be implemented between 1986 GEs (communicational cost without confidentiality and authentication) and 7939 GEs (computational cost with HMAC for authentication in data storage). This ensures varies security primitivs such as confidentiality, availability and authentication.

A simulation-based heuristic for the electric vehicle routing problem with time windows and stochastic waiting times at recharging stations

"A one-stop guide for implementing cloud computing. Cloud computing can save your library time and money by enabling convenient, on-demand network access to resources like servers and applications. Libraries that take advantage of the cloud have fewer IT headaches because data centers provide continuous updates and mobility that standard computing cannot easily provide, which means less time and energy spent on software, and more time and energy to devote to the library's day to day mission and services. Here, leading LITA experts demystify language, deflate hype, and provide library-specific examples of real-world success you can emulate to guarantee efficiency and savings. Among several features, this book helps you select data access and file sharing services, build digital repositories, and utilize other cloud computing applications in your library. Together, you and the cloud can save time and money, and build the information destination your patrons will love."--Publisher's website.

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user’s behaviour and from sharing resources – more specifically Virtual Machines (VM) – among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.

Credential Based Authentication Approach for Dynamic Group in Cloud Environment

PurposeThis study examined the waiting times to board an attraction at a theme park (Tokyo DisneySea in Japan) using a simulation based on measured values. Park visitors often complain that waiting times are too long; guests (Disney's term for park visitors) must stand in long, slow-moving queues outdoors in all weather, enduring heat, cold, rain and wind. This can undermine their health and reduce customer satisfaction. To date, no research has offered a scientific approach to solve the problem in the context of theme park queues.Design/methodology/approachThe attraction examined two queues: a short waiting queue for guests with priority entry tickets and a long waiting queue for guests without priority entry tickets. The total number of guests with priority entry tickets remained a constant value, as in the current system; however, the author designed the number as a monotonically increasing function to reduce the waiting times for nonpriority entry. It was impractical to analyze queues or try to explain proposed wait time reduction methods using theories and mathematical models alone. Therefore, the author used a simulation study based on real data to demonstrate the proposed method of this study.FindingsThe simulation results indicated that the proposed method significantly decreased guests' waiting times in the nonpriority entry queue, without changing the number of guests in both priority and nonpriority entry queues.Research limitations/implicationsSimple queues can be analyzed using theoretical calculations, but complicated queue systems require simulation methods. Therefore, this paper cannot provide a theoretical basis for the method.Practical implicationsThe proposed method offers benefits to managers of any event or location seeking to manage queue times and not just theme parks (e.g. exhibitions, concerts, etc.). Advance tickets are equivalent to priority entry tickets, so applying the proposed method can shorten waiting times on the day of the event.Originality/valueThis study has important practical implications for queues management, and the proposed approach is a unique system that reduces waiting times, thus increasing customer satisfaction. The proposed method can be applied to similar types of priority entry systems.

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

Waiting time and headway modeling considering unreliability in transit service