A Strategy for Performance Evaluation and Modeling of Cloud Computing Services

On-demand services and reduced costs made cloud computing a popular mechanism to provide scalable resources according to the user’s expectations. This paradigm is an important role in business and academic organizations, supporting applications and services deployed based on virtual machines and containers, two different technologies for virtualization. Cloud environments can support workloads generated by several numbers of users, that request the cloud environment to execute transactions and its performance should be evaluated and estimated in order to achieve clients satisfactions when cloud services are offered. This work proposes a performance evaluation strategy composed of a performance model and a methodology for evaluating the performance of services configured in virtual machines and containers in cloud infrastructures. The performance model for the evaluation of virtual machines and containers in cloud infrastructures is based on stochastic Petri nets. A case study in a real public cloud is presented to illustrate the feasibility of the performance evaluation strategy. The case study experiments were performed with virtual machines and containers supporting workloads related to social networks transactions

PERFORMANCE MODELING OF BIG DATA ENVIRONMENTS IN THE PRIVATE CLOUD

This work proposes the evaluation of the performanceof big data environments in the private cloud through amethodology and a stochastic model the proposed method-ology considers objective activities and performance mod-eling to assess Hadoop cluster performance in the privatecloud. The stochastic model represents sending datasets tothe Hadoop cluster with diff erent confi gurations, and theseinfrastructures are represented through stochastic Petri nets.A case study based on the CloudStack platform and Hadoopcluster is adopted to demonstrate the feasibility of the method-ology and the proposed model

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

HiRel: Hybrid Automated Reliability Predictor (HARP) integrated reliability tool system, (version 7.0). Volume 1: HARP introduction and user's guide

The Hybrid Automated Reliability Predictor (HARP) integrated Reliability (HiRel) tool system for reliability/availability prediction offers a toolbox of integrated reliability/availability programs that can be used to customize the user's application in a workstation or nonworkstation environment. HiRel consists of interactive graphical input/output programs and four reliability/availability modeling engines that provide analytical and simulative solutions to a wide host of reliable fault-tolerant system architectures and is also applicable to electronic systems in general. The tool system was designed to be compatible with most computing platforms and operating systems, and some programs have been beta tested, within the aerospace community for over 8 years. Volume 1 provides an introduction to the HARP program. Comprehensive information on HARP mathematical models can be found in the references

Mathematical Modelling and Nonstandard Schemes for the Corona Virus Pandemic

The programs used in this Master thesis: https://git.uni-wuppertal.de/1449563/covid-19-modelling/-/tree/master/PROGRAM

Performance of the transmission control protocol (TCP) over wireless with quality of service.

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.The Transmission Control Protocol (TCP) is the most widely used transport protocol in the Internet. TCP is a reliable transport protocol that is tuned to perform well in wired networks where packet losses are mainly due to congestion. Wireless channels are characterized by losses due to transmission errors and handoffs. TCP interprets these losses as congestion and invokes congestion control mechanisms resulting in degradation of performance. TCP is usually layered over the Internet protocol (lP) at the network layer. JP is not reliable and does not provide for any Quality of Service (QoS). The Internet Engineering Task Force (IETF) has provided two techniques for providing QoS in the Internet. These include Integrated Services (lntServ) and Differentiated Services (DiffServ). IntServ provides flow based quality of service and thus it is not scalable on connections with large flows. DiffServ has grown in popularity since it is scalable. A packet in a DiffServ domain is classified into a class of service according to its contract profile and treated differently by its class. To provide end-to-end QoS there is a strong interaction between the transport protocol and the network protocol. In this dissertation we consider the performance of the TCP over a wireless channel. We study whether the current TCP protocols can deliver the desired quality of service faced with the challenges they have on wireless channel. The dissertation discusses the methods of providing for QoS in the Internet. We derive an analytical model for TCP protocol. It is extended to cater for the wireless channel and then further differentiated services. The model is shown to be accurate when compared to simulation. We then conclude by deducing to what degree you can provide the desired QoS with TCP on a wireless channel