Reducing the operational cost of cloud data centers through renewable energy

The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud, a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

Abstracts: HASTAC 2017: The Possible Worlds of Digital Humanities

The document contains abstracts for HASTAC 2017

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity