Cloud Computing and Quality of Service: Issues and Developments

Cloud computing is a dynamic information technology (IT) paradigm that delivers on demand computing resources to a user over a network infrastructure. The Cloud Service Provider (CSP) offers applications which can be accessed online to users. Such applications can be shared by more than one user. CSPs provides programming interfaces that allows customers to build and deploy applications on the cloud; as well as providing massive storage and computing infrastructure to users. Users usually have no control on how data is stored on the cloud or where the underlying resources are located. With this limited control, customers’ requirements and Quality of Service (QoS) expectations from CSPs are spelt out using a Service Level Agreement (SLA). It is thus imperative to have the adequate QoS guarantees from a CSP. This paper examines trends in the area of Cloud computing QoS and provides a guide for future research. A review and survey of existing works in literature is done in order to identify these Cloud QoS trends. The finding is that the ultimate expectation of any QoS metrics or model is the related to cost concern for both the CSP and user

ОПТИМИЗАЦИЯ ПОДДЕРЖКИ ВЫЧИСЛИТЕЛЬНЫХ РЕСУРСОВ НА ЖЕЛЕЗНОДОРОЖНОМ ТРАНСПОРТЕ

In this article the author focuses on peculiarities of providing virtual resources, used in cloud computing systems for guaranteed quality of service, taking into account the requirements of QoS. The article contains the description of adaptive mechanism and comparative analysis of static and adaptive mechanisms to provide resources through simulation models. Moreover it covers such computing figures for models as the average time for request processing, the level of service denial, the significance of general application of system resources with different inbound parameters.Статья посвящена особенностям предоставления виртуальных ресурсов в системах, основанных на облачных технологиях, но с учетом выполнения требований QoS. Описан адаптивный механизм, а также проведен сравнительный анализ работы адаптивного и статических механизмов предоставления ресурсов с помощью имитационных моделей. Рассмотрены такие вычислительные показатели для моделей, как среднее время обработки запроса, уровень отказа обслуживания, значение общего использования ресурсов системы при различных входных параметрах

Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

A Genetic Based Resource Management Algorithm Considering Energy Efficiency in Cloud Computing Systems

Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insufficient cooling systems and inefficient, causing overheating sources, shortening the life of the machine and too much carbon dioxide is produced. In this paper, we aim to improve system performance; Cloud Computing based on a decrease in migration of among virtual machines (VM), and reduce energy consumption to be able to manage resources to achieve optimal energy efficiency. For this reason, various techniques such as genetic algorithms (GAs), virtual machine migration and ways Dynamic voltage and frequency scaling (DVFS), and resize virtual machines to reduce energy consumption and fault tolerance are used. The main purpose of this article, the allocation of resources with the aim of reducing energy consumption in cloud computing. The results show that reduced energy consumption and hold down the rate of virtual machines breach of contract, reduces migration as well

Self-adaptive and sensitivity-aware QoS modeling for the cloud

Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models

Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments

Alocação de Máquinas Virtuais em Ambientes de Computação em Nuvem Baseada em Requisitos de Service Level Agreement

A computação em nuvem teve um avanço considerável nos últimos anos, trazendo grandes benefícios incluindo escalabilidade, flexibilidade, acessibilidade global, melhor utilização de recursos e redução de custos, entre outros. Apesar de todos os benefícios, esta adesão e crescimento trás consigo grandes desafios como otimização do uso de recursos computacionais, redução de custos, garantia da qualidade de serviço (Quality of Service (QoS)), segurança, etc. As garantias da qualidade de serviço são estabelecidas através de Service Level Agreements (SLAs), que são contratos estabelecidos entre o cliente e o fornecedor do serviço de computação em nuvem, visando especificar de forma mensurável as metas de nível de serviço a serem cumpridas, além dos papéis e responsabilidades das partes envolvidas. Este trabalho apresenta um estudo sobre cumprimento de SLAs por algoritmos de alocação de máquinas virtuais em ambientes de computação em nuvem. O trabalho tem em consideração métricas como disponibilidade, custo, tempo de conclusão de uma aplicação (task completion time) e nível de tolerância a faltas, avaliando o cumprimento de tais métricas em diferentes cenários. O estudo é realizado utilizando o framework CloudSim Plus para modelação e execução de simulações de computação em nuvem. São introduzidos dois módulos no framework visando: (i) especificação de SLAs e templates de máquinas virtuais em formato JavaScript Object Notation (JSON), seguindo padrões do Amazon Elastic Compute Cloud (Amazon EC2); (ii) injeção de faltas aleatórias, permitindo avaliar como os SLAs são afetados perante o surgimento de faltas nos servidores. Por fim, o trabalho apresenta uma proposta para automação da criação e alocação de máquinas virtuais, visando cumprir os SLAs e libertar o cliente da necessidade de especificar a quantidade mínima de máquinas virtuais para atendimento dos níveis de serviço exigidos. Mesmo com todo o nível de automação que os fornecedores de computação em nuvem possam oferecer, os resultados obtidos mostram que é possível melhorar a automação destes serviços, reduzindo a necessidade de intervenção do cliente e as violações de SLA devido a uma inadequada configuração de máquinas virtuais realizada pelo cliente.Cloud computing has made considerable progress in recent years, bringing great benefits including scalability, flexibility, global accessibility, improved resource utilization and cost savings, among others. Despite all the benefits, this adhesion and growth carries with it great challenges such as optimization of the use of computational resources, reduction of costs, Quality of Service (QoS) assurance, security, etc. Guarantees are provided through Service Level Agreements (SLAs), which are agreements between the customer and the cloud computing service provider to measurably specify the service level goals to be fulfilled, as well as the roles and responsibilities of the parties involved. This work presents a study on compliance with service level agreements by algorithms for allocating virtual machines in cloud computing environments. The work takes into account metrics such as availability, cost, task completion time and level of fault tolerance, evaluating the compliance of such metrics in different scenarios. The study is conducted using the CloudSim Plus framework for modeling and running cloud computing simulations. Two modules are introduced in the framework about: (i) specification of SLAs and virtual machine templates in JSON format, following Amazon Elastic Compute Cloud (Amazon EC2) standards; (ii) injection of random faults, allowing to evaluate how the SLAs are affected by the occurrence of faults in servers. Finally, this work presents a proposal for automation of the creation and allocation of virtual machines, aiming to comply with the SLAs and free the client from the need to specify the minimum number of virtual machines to meet the required service levels. Even with all the automation level provided by cloud service providers, the obtained results show it is possible to further improve the automation of these services by reducing the need for customer intervention and SLA violations due to an inadequate configuration of virtual machines performed by the client

Virtual machine provisioning based on analytical performance and QoS in cloud computing environments

Cloud computing is the latest computing paradigm that delivers IT resources as services in which users are free from the burden of worrying about the low-level implementation or system administration details. However, there are significant problems that exist with regard to efficient provisioning and delivery of applications using Cloud-based IT resources. These barriers concern various levels such as workload modeling, virtualization, performance modeling, deployment, and monitoring of applications on virtualized IT resources. If these problems can be solved, then applications can operate more efficiently, with reduced financial and environmental costs, reduced underutilization of resources, and better performance at times of peak load. In this paper, we present a provisioning technique that automatically adapts to workload changes related to applications for facilitating the adaptive management of system and offering endusers guaranteed Quality of Services (QoS) in large, autonomous, and highly dynamic environments. We model the behavior and performance of applications and Cloud-based IT resources to adaptively serve end-user requests. To improve the efficiency of the system, we use analytical performance (queueing network system model) and workload information to supply intelligent input about system requirements to an application provisioner with limited information about the physical infrastructure. Our simulation-based experimental results using production workload models indicate that the proposed provisioning technique detects changes in workload intensity (arrival pattern, resource demands) that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets