A New Analytical Model for Calculating Elasticity in Cloud Computing

International audienceOne of the fundamental characteristics of Cloud Computing is its elasticity. It is about the ability to dynamically adapt computer resources consumption to workload while maintaining performance and quality of service. Most current industrial as well as academic solutions have limitations in terms of elasticity control, which affects the availability and performance of systems. In this paper, we propose a modeling of an elastic Cloud platform in terms of the markovian queuing model where the number of active servers depends on the current workload. A quantitative analysis of the steady state of our model allows to analyze and calculate the value of the elasticity in a precise way

Program of Research in Aeronautics

A prospectus of the educational and research opportunities available at the Joint Institute for Advancement of Flight Sciences, operated at NASA Langley Research Center in conjunction with George Washington University's School of Engineering and Applied Sciences is presented. Requirements of admission to various degree programs are given as well as the course offerings in the areas of acoustics, aeronautics, environmental modelling, materials science, and structures and dynamics. Research facilities for each field of study are described. Presentations and publications (including dissertations and theses) generated by each program are listed as well as faculty members visting scientists and engineers

SensorCloud: Towards the Interdisciplinary Development of a Trustworthy Platform for Globally Interconnected Sensors and Actuators

Although Cloud Computing promises to lower IT costs and increase users' productivity in everyday life, the unattractive aspect of this new technology is that the user no longer owns all the devices which process personal data. To lower scepticism, the project SensorCloud investigates techniques to understand and compensate these adoption barriers in a scenario consisting of cloud applications that utilize sensors and actuators placed in private places. This work provides an interdisciplinary overview of the social and technical core research challenges for the trustworthy integration of sensor and actuator devices with the Cloud Computing paradigm. Most importantly, these challenges include i) ease of development, ii) security and privacy, and iii) social dimensions of a cloud-based system which integrates into private life. When these challenges are tackled in the development of future cloud systems, the attractiveness of new use cases in a sensor-enabled world will considerably be increased for users who currently do not trust the Cloud.Comment: 14 pages, 3 figures, published as technical report of the Department of Computer Science of RWTH Aachen Universit

Comparison of a Material Point Method and a Galerkin meshfree method for the simulation of cohesive-frictional materials

The simulation of large deformation problems, involving complex history-dependent constitutive laws, is of paramount importance in several engineering fields. Particular attention has to be paid to the choice of a suitable numerical technique such that reliable results can be obtained. In this paper, a Material Point Method (MPM) and a Galerkin Meshfree Method (GMM) are presented and verified against classical benchmarks in solid mechanics. The aim is to demonstrate the good behavior of the methods in the simulation of cohesive-frictional materials, both in static and dynamic regimes and in problems dealing with large deformations. The vast majority of MPM techniques in the literature are based on some sort of explicit time integration. The techniques proposed in the current work, on the contrary, are based on implicit approaches, which can also be easily adapted to the simulation of static cases. The two methods are presented so as to highlight the similarities to rather than the differences fromPeer ReviewedPostprint (published version

Cloud Computing cost and energy optimization through Federated Cloud SoS

2017 Fall.Includes bibliographical references.The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks

A Taxonomy of Quality Metrics for Cloud Services

[EN] A large number of metrics with which to assess the quality of cloud services have been proposed over the last years. However, this knowledge is still dispersed, and stakeholders have little or no guidance when choosing metrics that will be suitable to evaluate their cloud services. The objective of this paper is, therefore, to systematically identify, taxonomically classify, and compare existing quality of service (QoS) metrics in the cloud computing domain. We conducted a systematic literature review of 84 studies selected from a set of 4333 studies that were published from 2006 to November 2018. We specifically identified 470 metric operationalizations that were then classified using a taxonomy, which is also introduced in this paper. The data extracted from the metrics were subsequently analyzed using thematic analysis. The findings indicated that most metrics evaluate quality attributes related to performance efficiency (64%) and that there is a need for metrics that evaluate other characteristics, such as security and compatibility. The majority of the metrics are used during the Operation phase of the cloud services and are applied to the running service. Our results also revealed that metrics for cloud services are still in the early stages of maturity only 10% of the metrics had been empirically validated. The proposed taxonomy can be used by practitioners as a guideline when specifying service level objectives or deciding which metric is best suited to the evaluation of their cloud services, and by researchers as a comprehensive quality framework in which to evaluate their approaches.This work was supported by the Spanish Ministry of Science, Innovation and Universities through the Adapt@Cloud Project under Grant TIN2017-84550-R. The work of Ximena Guerron was supported in part by the Universidad Central del Ecuador (UCE), and in part by the Banco Central del Ecuador.Guerron, X.; Abrahao Gonzales, SM.; Insfran, E.; Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2020). A Taxonomy of Quality Metrics for Cloud Services. IEEE Access. 8:131461-131498. https://doi.org/10.1109/ACCESS.2020.3009079S131461131498

Proposing Optimus Scheduler Algorithm for Virtual Machine Placement Within a Data Center

With the evolution of the Internet, we are witnessing the birth of an increasing number of applications that rely on the network; what was previously executed on the user's computers as stand-alone programs has been redesigned to be executed on servers with permanent connections to the Internet, making the information available from any device that has network access. Instead of buying a copy of a program, users can now pay to obtain access to it through the network, which is one of the models of cloud computing, Software as a Service (SaaS). The continuous growth of Internet bandwidth has also given rise to new multimedia applications, such as social networks and video over the Internet; and to complete this new paradigm, mobile platforms provide the ubiquity of information that allows people to stay connected. Service providers may own servers and data centers or, alternatively, may contract infrastructure providers that use economies of scale to offer access to servers as a service in the cloud computing model, i.e., Infrastructure as a Service (IaaS). As users become more dependent on cloud services and mobile platforms increase the ubiquity of the cloud, the quality of service becomes increasingly important. A fundamental metric that defines the quality of service is the delay of the information as it travels between the user computers and the servers, and between the servers themselves. Along with the quality of service and the costs, the energy consumption and the CO2 emissions are fundamental considerations in regard to planning cloud computing networks. In this research work, an Optimus Scheduler algorithm to be proposed for Add, Remove or Resize an application by using Tabu Search Algorithm