Representativeness and seasonality of major ion records derived from NEEM firn cores

The seasonal and annual representativeness of ionic aerosol proxies (among others, calcium, sodium, ammonium and nitrate) in various firn cores in the vicinity of the NEEM drill site in northwest Greenland have been assessed. Seasonal representativeness is very high as one core explains more than 60 % of the variability within the area. The inter-annual representativeness, however, can be substantially lower (depending on the species) making replicate coring indispensable to derive the atmospheric variability of aerosol species. A single core at the NEEM site records only 30 % of the inter-annual atmospheric variability in some species, while five replicate cores are already needed to cover approximately 70 % of the inter-annual atmospheric variability in all species. The spatial representativeness is very high within 60 cm, rapidly decorrelates within 10 m but does not diminish further within 3 km. We attribute this to wind reworking of the snow pack leading to sastrugi formation. Due to the high resolution and seasonal representativeness of the records we can derive accurate seasonalities of the measured species for modern (AD 1990–2010) times as well as for pre-industrial (AD 1623–1750) times. Sodium and calcium show similar seasonality (peaking in February and March respectively) for modern and pre-industrial times, whereas ammonium and nitrate are influenced by anthro pogenic activities. Nitrate and ammonium both peak in May during modern times, whereas during pre-industrial times ammonium peaked during July–August and nitrate during June–July.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). {SLAs} define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Evaluation of aeolian dust records obtained from Polar Ice Cores

When an ice core sample is analysed for its aeolian dust content, it is melted and the particles detected are suspended in water. Consequently, dust measurement techniques employed in the ice core community differ from those used for in-situ studies of airborne dust. Methods commonly used to classify insolubles suspended in a liquid are either based on the particles’ interaction with light or on the detection of resistive pulses by means of Coulter counting. Data sets obtained with Coulter counters are widely accepted as references and other techniques are judged against their ability to reproduce these. Unfortunately, optically acquired ice core dust records were found to differ. By analyzing two sections of the NEEM dust record, two different evaluation procedures are discussed before a third protocol is proposed. It is found that relative changes in the archived dust load can be reproduced, while the simultaneous attainment of absolute concentrations or changes in the grain size frequency histograms in high resolution remains difficult.

New LA-ICP-MS cryocell and calibration technique for sub-millimeter analysis of ice cores

Ice cores provide a robust reconstruction of past climate. However, development of timescales by annual-layer counting, essential to detailed climate reconstruction and interpretation, on ice cores collected at low-accumulation sites or in regions of compressed ice, is problematic due to closely spaced layers. Ice-core analysis by laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) provides sub-millimeter-scale sampling resolution (on the order of 100μm in this study) and the low detection limits (ng L–1) necessary to measure the chemical constituents preserved in ice cores. We present a newly developed cryocell that can hold a 1m long section of ice core, and an alternative strategy for calibration. Using ice-core samples from central Greenland, we demonstrate the repeatability of multiple ablation passes, highlight the improved sampling resolution, verify the calibration technique and identify annual layers in the chemical profile in a deep section of an ice core where annual layers have not previously been identified using chemistry. In addition, using sections of cores from the Swiss/Italian Alps we illustrate the relationship between Ca, Na and Fe and particle concentration and conductivity, and validate the LA-ICP-MS Ca profile through a direct comparison with continuous flow analysis results.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Demo -- Closer to Cloud-RAN: RAN as a Service

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers (e.g., MNO, MVNO) move from proprietary and bespoke hardware and software platforms toward an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently created through cloudification by leveraging the existing infrastructure. In this work, we present RANaaS, which is a cloudified radio access network delivered as a service. RANaaS provides the service life-cycle of an ondemand, elastic, and pay as you go 3GPP RAN instantiated on top of the cloud infrastructure. We demonstrate an example of realtime cloudified LTE network deployment using the OpenAirInterface LTE implementation and OpenStack running on commodity hardware as well as the flexibility and performance of the platform developed.

EASE: EPC as a service to ease mobile core network deployment over cloud

The objective of this article is to demonstrate the feasibility of on-demand creation of cloud-based elastic mobile core networks, along with their lifecycle management. For this purpose the article describes the key elements to realize the architectural vision of EPC as a Service, an implementation option of the Evolved Packet Core, as specified by 3GPP, which can be deployed in cloud environments. To meet several challenging requirements associated with the implementation of EPC over a cloud infrastructure and providing it “as a Service,” this article presents a number of different options, each with different characteristics, advantages, and disadvantages. A thorough analysis comparing the different implementation options is also presented.

Software Development in the Cloud: Exploring the Affordances of Platform-as-a-Service

Software development teams increasingly adopt platform-as-a-service (PaaS), i.e., cloud services that make software development infrastructure available over the internet. Yet, empirical evidence of whether and how software development work changes with the use of PaaS is difficult to find. We performed a grounded-theory study to explore the affordances of PaaS for software development teams. We find that PaaS enables software development teams to enforce uniformity, to exploit knowledge embedded in technology, to enhance agility, and to enrich jobs. These affordances do not arise in a vacuum. Their emergence is closely interwoven with changes in methodologies, roles, and norms that give rise to self-organizing, loosely coupled teams. Our study provides rich descriptions of PaaS-based software development and an emerging theory of affordances of PaaS for software development teams.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.