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.