Scientific instruments for climate change adaptation: estimating and optimizing the efficiency of ecosystem service provision

Adaptation to the consequences of climate change can depend on efficient use of ecosystem services (ES), i.e. a better use of natural services through management of the way in which they are delivered to society. While much discussion focuses on reducing consumption and increasing production of services, a lack of scientific instruments has so far prevented other mechanisms to improve ecosystem services efficiency from being addressed systematically as an adaptation strategy. This paper describes new methodologies for assessing ecosystem services and quantifying their values to humans, highlighting the role of ecosystem service flow analysis in optimizing the efficiency of ES provision.Ecosystem services, flow analysis, Bayesian modeling, spatial analysis, Environmental Economics and Policy, Q01, Q54, Q55, Q57,

Holistic Resource Management for Sustainable and Reliable Cloud Computing:An Innovative Solution to Global Challenge

Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers towards reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing state-of-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters

METROPOLITAN GOVERNMENT AND IMPROVEMENT POTENTIALS OF URBAN BASIC SERVICES GOVERNANCE IN DHAKA CITY, BANGLADESH: RHETORIC OR REALITY?

Urban basic services, namely water supply and sewerage, power supply, telecommunication, road network etc. are the prerequisites for city living. Efficiency in managing and maintaining urban basic services ultimately dictates the quality of an urban government. In the last three decades, Dhaka‘s urban basic services governance has been tumbling behind the required standard. Specially, in the last five/six years the situation has reached to an alarming state— resulting to a high degree of inconvenience in urban living and loss of potentials in city economy. Frequent malfunctioning, limited capacity to expand, bureaucratic complexity in availing services, lack of public accountability etc. are some of the common characteristics of urban basic services in Dhaka. There is a general understanding in the concerned sphere that fragmented mode of governing these has attributed the service environment with a complex texture. In fact, fragmentation contributed in terms of multiparty involvement, overlapping of responsibility, obsolete organizational planning, monopoly management etc. Presently, Dhaka‘s urban basic services governance can‘t assure better living, competitive business environment and sustainable economic growth. As a potential remedy, adoption of a general purpose ?metropolitan government? mechanism with prerogatives to plan, develop, maintain service provisions; adequately command the service providers; govern development initiatives; realize taxes and revenues have been in discussion for quite a period within the concerned political and administrative domain. This article discusses the rationale, potentials of a general purpose ?metropolitan government? mechanism to improve Dhaka‘s present state of urban basic services. Additionally, the paper attempted to frame out the structure and operational mechanism of the proposed government.Urban basic services, metropolitan government, fragmented governance.

Scientific instruments for climate change adaptation: Estimating and optimizing the efficiency of ecosystem service provision

Adaptation to the consequences of climate change can depend on efficient use of ecosystem services (ES), i.e. a better use of natural services through management of the way in which they are delivered to society. While much discussion focuses on reducing consumption and increasing production of services, a lack of scientific instruments has so far prevented other mechanisms to improve ecosystem services efficiency from being addressed systematically as an adaptation strategy. This paper describes new methodologies for assessing ecosystem services and quantifying their values to humans, highlighting the role of ecosystem service flow analysis in optimizing the efficiency of ES provision

Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work

Improved modelling of the freshwater provisioning ecosystem service in water scarce river basins

[EN] Freshwater provisioning by the landscape contributes to human well-being through water use for drinking, irrigation and other purposes. The assessment of this ecosystem service involves the quantification of water resources and the valuation of water use benefits. Models especially designed to assess ecosystem services can be used. However, they have limitations in representing the delivery of the service in water scarce river basins where water management and the temporal variability of water resource and its use are key aspects to consider. Integrating water resources management tools represents a good alternative to ecosystem services models in these river basins. We propose a modelling framework that links a rainfall-runoff model and a water allocation model which allow accounting for the specific requirements of water scarce river basins. Moreover, we develop a water tracer which rebounds the value of the service from beneficiaries to water sources, allowing the spatial mapping of the service.The authors acknowledge the support of Universitat Politecnica de Valencia through its Support Programme for Research and Development. We also wish to thank Confederacion Hidrogr afica del Duero (belonging to the Spanish Ministry of Agriculture, Food and Environment) for the data provided in developing this study and the Spanish Ministry of Economy and Competitiveness for its financial support through the projects SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and NUTEGES (CGL2012-34978). We also value the support provided by the European Community in financing the Seventh Framework Program projects DROUGHTR&SPI (FP7-ENV-2011, 282769), ENHANCE (FP7-ENV-2012, 308438), the H2020 project IMPREX (H2020-WATER-2014-2015, 641811), the grant WAMCD (EC-DG Environment No. 07.0329/2013/ 671291/SUB/ENV.C1) and the Life þ project LIFE ALBUFERA (LIFE12 ENV/ES/000685).Momblanch Benavent, A.; Andreu Álvarez, J.; Paredes Arquiola, J. (2017). Improved modelling of the freshwater provisioning ecosystem service in water scarce river basins. Environmental Modelling & Software. 94:87-99. https://doi.org/10.1016/j.envsoft.2017.03.033S87999

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications