SecIngress: An API gateway framework to secure cloud applications based on N-variant system

AbstractTrans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

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Selective caching: a persistent memory approach for multi-dimensional index structures

Distributed and Parallel Databases ◽

10.1007/s10619-021-07327-0 ◽

2021 ◽

Author(s):

Muhammad Attahir Jibril ◽

Philipp Götze ◽

David Broneske ◽

Kai-Uwe Sattler

Keyword(s):

Main Memory ◽

Index Structure ◽

Index Structures ◽

Cloud Infrastructure ◽

General Technique ◽

Persistent Memory ◽

The Cost ◽

Cloud Applications ◽

Memory Layout ◽

Analytical Index

AbstractAfter the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

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An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽

10.3390/s21051590 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1590

Author(s):

Arnak Poghosyan ◽

Ashot Harutyunyan ◽

Naira Grigoryan ◽

Clement Pang ◽

George Oganesyan ◽

...

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Transfer Learning ◽

Performance Monitoring ◽

Nonlinear Problems ◽

Time Series Forecasting ◽

Stationary Time Series ◽

Learning Gain ◽

Application Performance ◽

Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

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