3,508 research outputs found
Holistic cloud computing environmental quantification and behavioural analysis
Cloud computing has been characterized to be large-scale multi-tenant systems that are able to dynamically scale-up and scale-down computational resources to consumers with diverse Quality-of-Service requirements. In recent years, a number of dependability and resource management approaches have been proposed for Cloud computing datacenters. However, there is still a lack of real-world Cloud datasets that analyse and extensively model Cloud computing characteristics and quantify their effect on system dimensions such as resource utilization, user behavioural patterns and failure characteristics. This results in two research problems: First, without the holistic analysis of real-world systems Cloud characteristics, their dimensions cannot be quantified resulting in inaccurate research assumptions of Cloud system behaviour. Second, simulated parameters used in state-of-the-art Cloud mechanisms currently rely on theoretical values which do not accurately represent real Cloud systems, as important parameters such as failure times and energy-waste have not been quantified using empirical data. This presents a large gap in terms of practicality and effectiveness between developing and evaluating mechanisms within simulated and real Cloud systems.
This thesis presents a comprehensive method and empirical analysis of large-scale production Cloud computing environments in order to quantify system characteristics in terms of consumer submission and resource request patterns, workload behaviour, server utilization and failures. Furthermore, this work identifies areas of operational inefficiency within the system, as well as quantifies the amount of energy waste created due to failures. We discover that 4-10% of all server computation is wasted due to Termination Events, and that failures contribute to approximately 11% of the total datacenter energy waste. These analyses of empirical data enables researchers and Cloud providers an enhanced understanding of real Cloud behaviour and supports system assumptions and provides parameters that can be used to develop and validate the effectiveness of future energy-efficient and dependability mechanisms
Visions and Challenges in Managing and Preserving Data to Measure Quality of Life
Health-related data analysis plays an important role in self-knowledge,
disease prevention, diagnosis, and quality of life assessment. With the advent
of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices
(wearables, home-medical sensors, etc) facilitates data collection and provide
cloud storage with a central administration. More recently, blockchain and
other distributed ledgers became available as alternative storage options based
on decentralised organisation systems. We bring attention to the human data
bleeding problem and argue that neither centralised nor decentralised system
organisations are a magic bullet for data-driven innovation if individual,
community and societal values are ignored. The motivation for this position
paper is to elaborate on strategies to protect privacy as well as to encourage
data sharing and support open data without requiring a complex access protocol
for researchers. Our main contribution is to outline the design of a
self-regulated Open Health Archive (OHA) system with focus on quality of life
(QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System
A holistic multi-methodology for sustainable renovation
A review of the barriers for building renovation has revealed a lack of methodologies, which can promote sustainability objectives and assist various stakeholders during the design stage of building renovation/retrofitting projects. The purpose of this paper is to develop a Holistic Multi-methodology for Sustainable Renovation, which aims to deal with complexity of renovation projects. It provides a framework through which to involve the different stakeholders in the design process to improve group learning and group decision-making, and hence make the building renovation design process more robust and efficient. Therefore, the paper discusses the essence of multifaceted barriers in building renovation regarding cultural changes and technological/physical changes. The outcome is a proposal for a multi-methodology framework, which is developed by introducing, evaluating and mixing methods from Soft Systems Methodologies (SSM) with Multiple Criteria Decision Making (MCDM). The potential of applying the proposed methodology in renovation projects is demonstrated through a case study
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Measuring Psychosocial and Behavioural Factors Improves Attack Potential Estimates
Cyber risk assessment standards and methodologies do not consider psychological, social and behavioural parameters in their classifications of the attackers' types, profiles, and competencies. In this paper, we present a holistic, multidimensional approach to examine the likelihood for an attackers' behaviour to occur by considering all influential factors (e.g., technical, social, behavioural, psychological). Furthermore, the quantification of the attackers' behaviours may lead to better estimate attacks' potential
Coastal management and adaptation: an integrated data-driven approach
Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from the input of a partner organisation, responsible for the regionâs coasts: Coastal Partnership East.
An initial review revealed how data can be utilised effectively within coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk.
The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast
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