Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems

Ardagna, Danilo; Ciavotta, Michele; Passacantando, Mauro

doi:10.1109/tsc.2015.2477836

Cited by 74 publications

(31 citation statements)

References 54 publications

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“…The heterogeneity of cloud workloads could be challenging for choosing a suitable headroom online for minimizing the resources provided while meeting the SLO. Thus, it is important to evaluate the system performance under different types of workload as assessed in [36]. Therefore, while CPU usage headroom varies with different workload patterns and frequencies, adapting the headroom could be beneficial in terms of CPU resource savings while meeting the SLOs.…”

Section: Future Directionsmentioning

confidence: 99%

Robust Dynamic CPU Resource Provisioning in Virtualized Servers

Makridis

Deliparaschos

Kalyvianaki

et al. 2022

IEEE Trans. Serv. Comput.

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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.

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Section: Future Directionsmentioning

confidence: 99%

Robust Dynamic CPU Resource Provisioning in Virtualized Servers

Makridis

Deliparaschos

Kalyvianaki

et al. 2022

IEEE Trans. Serv. Comput.

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“…To conclude, it is worth remarking that the presented multi-Cloud scenario is technologically feasible thanks to, for instance, the software solutions developed within MODAClouds project [16], [17], which encompass full-stack modeling, deployment, and runtime management of multi-Cloud applications, removing technological limitations and lock-ins that have so far prevented the full potential of this approach (high availability and cost saving due to IaaS competition and workload redis-tribution at runtime [24]) from being exploited. In particular, since one of the main inhibitors to the multi-Cloud adoption is the objective technological challenge of keeping updated the geographically distributed and technologically distinct databases, the above-mentioned project provides a distributed middleware (see Figure 1) in charge of reliably synchronizing data among (even technologically) different databases [25].…”

Section: Load Balancer Managermentioning

confidence: 99%

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Ardagna

Ciavotta

Lancellotti

et al. 2021

IEEE Trans. Cloud Comput.

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Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.259% versus up to 17.245% of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.

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“…Problem (P2) can be reasonably relaxed to a continuous formulation as in other literature approaches (see, e.g., [31]). Furthermore, the problem can be additionally simplified with a couple of simple algebraic transformations.…”

Section: Identifying An Initial Solutionmentioning

confidence: 99%

Optimizing Quality-Aware Big Data Applications in the Cloud

Gianniti

Ciavotta

Ardagna

2021

IEEE Trans. Cloud Comput.

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The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data-intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. This paper proposes a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effectively explores the space of alternative Cloud configurations, seeking the minimum cost deployment that satisfies quality of service constraints. The soundness of the proposed solution has been thoroughly validated in a vast experimental campaign encompassing different applications and Big Data platforms.

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Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems

Cited by 74 publications

References 54 publications

Robust Dynamic CPU Resource Provisioning in Virtualized Servers

Robust Dynamic CPU Resource Provisioning in Virtualized Servers

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Optimizing Quality-Aware Big Data Applications in the Cloud

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