Robust dynamic CPU resource provisioning in virtualized servers

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e. H∞ filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time (mRT) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications

Feedback-control & queueing theory-based resource management for streaming applications

Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming large amounts of data at unprecedented rates. A number of distinct streaming data models have been proposed. Typical applications for this include smart cites & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We propose an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. Evaluation is carried out using a federated Cloud-based infrastructure (implemented using CometCloud) – where the allocation of new resources can be based on: (i) differences between sites, i.e. types of resources supported (e.g. GPU vs. CPU only), (ii) cost of execution; (iii) failure rate and likely resilience, etc. In particular, we demonstrate how Little’s Law –a widely used result in queuing theory– can be adapted to support dynamic control in the context of such resource provisioning

Avaliação de máquinas preemptáveis nos provedores de nuvem pública Amazon e Google

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.No contexto contemporâneo, no qual diversas empresas e organizações possuem grandes demandas por recursos computacionais, cada vez mais provedores de nuvem pública surgem no mercado. Diante disso, a escolha do serviço e do provedor mais adequados se torna um desafio não trivial para usuários. Nesse contexto, este trabalho propõe uma análise comparativa sobre máquinas preemptáveis oferecidas por provedores de nuvem pública, as quais podem ser finalizadas em situações onde seus recursos computacionais são necessários em outras tarefas do provedor do serviço. Para isso, são executados testes experimentais em instâncias oferecidas pelos provedores, utilizando benchmarks. O tra- balho conclui, a partir dos resultados de custo e de performance obtidos, quais instâncias, provedores e regiões são mais indicados para cargas de trabalho similares aos benchmarks executados.In the contemporary context, in which several companies and organizations have great demands for computational resources, more and more public cloud providers arise on the market. Therefore, choosing the right service and provider becomes a non-trivial challenge for users. In this context, this paper proposes a comparative analysis on preemptible machines offered as a service by public cloud providers. For that goal, experimental tests are executed in instances offered by the providers, using benchmarks. The work concludes, from the results of cost and performance obtained, which instances, providers, and regions are best suited for benchmarked workloads

Technical debt-aware elasticity management in cloud computing environments

Elasticity is the characteristic of cloud computing that provides the underlying primitives to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of the economies of scale in the cloud, which refers to a drop in the average costs of these computing capacities as a result of the dynamic sharing capability. However, in practice, it is impossible to achieve elasticity adaptations that obtain perfect matches between resource supply and demand, which produces dynamic gaps at runtime. Moreover, elasticity is only a capability, and consequently it calls for a management process with far-sighted economics objectives to maximise the value of elasticity adaptations. Within this context, we advocate the use of an economics-driven approach to guide elasticity managerial decisions. We draw inspiration from the technical debt metaphor in software engineering and we explore it in a dynamic setting to present a debt-aware elasticity management. In particular, we introduce a managerial approach that assesses the value of elasticity decisions to adapt the resource provisioning. Additionally, the approach pursues strategic decisions that value the potential utility produced by the unavoidable gaps between the ideal and actual resource provisioning over time. As part of experimentation, we built a proof of concept and the results indicate that value-oriented adaptations in elasticity management lead to a better economics performance in terms of lower operating costs and higher quality of service over time. This thesis contributes (i) an economics-driven approach towards elasticity management; (ii) a technical debt-aware model to reason about elasticity adaptations; (iii) a debt-aware learning elasticity management approach; and (iv) a multi-agent elasticity management for multi-tenant applications hosted in the cloud