Analytical modeling of performance indices under epistemic uncertainty applied to cloud computing systems

Antonelli, Fabio; Cortellessa, Vittorio; Gribaudo, Marco; Pinciroli, Riccardo; Trivedi, Kishor S.; Trubiani, Catia

doi:10.1016/j.future.2019.09.006

Cited by 22 publications

(6 citation statements)

References 40 publications

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“…As a new area of research, developing a robust GNN model has attracted several studies in recent years (Table 1), aiming to protect graph representation/embedding and node/link classification from adversarial attacks [27,[49][50][51]. To defend against adversarial attacks, most of the existing methods use one of the following techniques [33]: (1) certifying the robustness of graph structures and node features [9], (2) training the model with adversarial examples [16,49], (3) detecting and removing perturbed edges or nodes [52], and (4) applying attention mechanism to enhance the model's performance [43].…”

Section: Defense On Graphsmentioning

confidence: 99%

Robust graph representation learning via out-of-distribution detection approach

Bastami,

Soltanizadeh,

Rahmanimanesh

et al. 2024

Preprint

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Graph neural networks (GNNs) are powerful models capable of learning from graph-structured data and performing various tasks. GNNs are susceptible to poisoning attacks, in which sophisticated attackers inject malicious nodes or edges into the graph topology to degrade their performance. The existing defense mechanisms, such as adversarial training, are ineffective in improving the robustness of GNN models. Fake nodes can be utilized creatively to deceive traditional GNN neural functions. In this paper, we propose a robust GNN model empowered by a reliable aggregation function based on the OOD detection concept and a novel method. A key idea of RGRO is to train a model that maps the nodes to a latent space in which we can measure the distance between the nodes and their distribution. The Mahalanobis distance is proposed as a superior alternative to cosine distance in order to exploit the homophily rule better and to consider the contextual information of the nodes. The Mahalanobis distance, which considers the covariance of the data and is scale-invariant, allows for a more effective application of the homophily rule. Mahalanobis distance is explored in this study to enhance the robustness and accuracy of outlier detection in graph data, thus contributing to a better understanding of homophily. The RGRO model can improve accuracy by removing poisoned data without knowing any types of poisoning attacks or GNN algorithms. RGRO is evaluated with four typical defense strategies based on two types of poisoning attacks implemented on different realistic datasets. Based on the results, RGRO can detect poisoned data against attacks effectively and efficiently. In the best scenario, RGRO improves the accuracy of the GNN model by 0.86.

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Section: Defense On Graphsmentioning

confidence: 99%

Robust graph representation learning via out-of-distribution detection approach

Bastami,

Soltanizadeh,

Rahmanimanesh

et al. 2024

Preprint

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“…Therefore, we extend our analysis by incorporating fuzzy DEA by using financial ratios as both input and output indicators. Fuzzy logic is a valuable tool for quantifying epistemic uncertainty arising from knowledge gaps, complexity, and limited data availability—characteristics that are prevalent among publicly traded healthcare companies, especially in the pharmaceutical sector (Antonelli et al, 2020; Peykani et al, 2018). Our decision to incorporate fuzzy efficiency analysis is motivated by the inadequacy of traditional assessments of financial ratios, which rely on zero–one (good/bad) assessments and fail to account for the nuanced and ambiguous nature of real‐world events.…”

Section: Motivational Background and Literature Reviewmentioning

confidence: 99%

Financial efficiency of healthcare companies in an emerging market

Cinaroglu,

Pirgaip

2024

Manage Decis Econ

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Understanding the financial dynamics of healthcare companies is essential for reducing costs while improving access to care and patient outcomes. This study aims to explore the financial efficiency of healthcare companies operating in an emerging stock market. We use conventional and fuzzy data envelopment analysis. We consider liquidity and leverage ratios as input criteria and profitability ratios as output criteria, each taken from Borsa Istanbul for the year 2022. The results indicate that financially inefficient healthcare companies can increase their efficiency by effectively managing debt and working capital, restructuring their asset base, and focusing on equity management.

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“…Cloud-based experiments assess the performance of ScaleX and QN-CTRL with respect to industrial controllers and show possible directions to improve both approaches. The response time of real cloud applications is rarely deterministic due to application interference [64] and other noise. Hence, ScaleX may underestimate the application requirements and violate the given SLA since it leverages a feedback-based control mechanism.…”

Section: Lessons Learnedmentioning

confidence: 99%

Autoscaling Solutions for Cloud Applications Under Dynamic Workloads

Quattrocchi,

Incerto,

Pinciroli

et al. 2024

IEEE Trans. Serv. Comput.

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Autoscaling systems provide means to automatically change the resources allocated to a software system according to the incoming workload and its actual needs. Public cloud providers offer a variety of autoscaling solutions, ranging from those based on user-written rules to more sophisticated ones. Originally, these solutions were conceived to manage clusters of virtual machines, while more recently, they have also been employed in the operation of containers. This paper analyses the autoscaling solutions provided by three major cloud providers, namely Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and compares them against two solutions we develop based on control theory (ScaleX ) and queuing theory (QN-CTRL). We evaluate the different approaches using both an in-house simulation engine and cloud deployments by feeding them with various synthetic and real-world workloads. Our extensive evaluation collects both simulation results and real measurements by which we can assess that both ScaleX and QN-CTRL outperform industrial techniques in most cases when considering the trade-offs between the service-level-agreement (SLA) violations and the optimal usage of resources.

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Analytical modeling of performance indices under epistemic uncertainty applied to cloud computing systems

Cited by 22 publications

References 40 publications

Robust graph representation learning via out-of-distribution detection approach

Robust graph representation learning via out-of-distribution detection approach

Financial efficiency of healthcare companies in an emerging market

Autoscaling Solutions for Cloud Applications Under Dynamic Workloads

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