Conventional cloud services and infrastructures are mainly designed to maximize utilization of resources and provide best-effort Quality-of-Service levels. However, many emerging use cases in both public and private cloud computing scenarios are time-critical in nature. For example, automated vehicles, smart cities, and automated factories, are all application domains characterized by the need for highly reliable and consistent low-latency services. The incorporation of predictable execution properties in cloud solutions is essential to meet these requirements. This paper provides an overview of the current research landscape in cloud computing, summarizing the key aspects to enable support of time-critical applications. The paper explores various levels of the typical cloud software stack: machine virtualization and containers, resource management and orchestration, fault tolerance, serverless computing, data storage and management, and communications.

This paper tackles the problem of optimal configuration and deployment of fault-tolerant time-critical service chains with arbitrary DAG-alike topologies. We propose RTilience, designed according to a scalable cloud microservice paradigm, and prototyped on top of the well-known Kubernetes cloud orchestrator. It features real-time reservation scheduling of containers to guarantee temporal isolation of time-critical tasks, leading to fine-grained control of compute latencies, while allowing for sharing physical CPUs among containers. A distributed routing library, ReqRoute, is configured with a timeout and primary and secondary routes, enabling autonomous and decentralized handling of failing requests. The routes are configured by a centralized controller that performs admission control, resource management of microservice instances, task placement, and fault detection and recovery, extending the features available in Kubernetes. Admission control is based on a theoretical framework enclosing a worst-case performance model for the experienced end-to-end response-time under various fault handling options, and an optimization framework that computes the optimum resource allocation for admitted services. Extensive experimentation of the proposed solution has been performed with synthetic examples, and an autonomous transport robot use-case, verifying that end-to-end deadlines are effectively respected, even in presence of high fault rates of individual microservice instances, according to the theoretical expectations. RTilience is made available as open-source software, released under a MIT license.

Many edge applications rely on expensive Deep-Neural-Network (DNN) inference-based video analytics. Typically, a single instance of an inference service analyzes multiple realtime camera streams concurrently. In many cases, only a fraction of these streams contain objects-of-interest at a given time. Hence, it is a waste of computational resources to process all frames from all cameras using the DNNs. On-camera filtration of frames has been suggested as a possible solution to improve the system efficiency and reduce resource wastage. However, many cameras do not have on-camera processing or filtering capabilities. In addition, filtration can be enhanced if frames across the different feeds are selected and prioritized for processing based on the system load and the available resource capacity. This paper introduces CVF, a Cross-video Filtration framework designed around video content and resource constraints. The CVF pipeline leverages compressed-domain data from encoded video formats, lightweight binary classification models, and an efficient prioritization algorithm. This enables the effective filtering of cross-camera frames from multiple sources, processing only a fraction of frames using resource-intensive DNN models. Our experiments show that CVF is capable of reducing the overall response time of video analytics pipelines by up to 50% compared to state-of-the-art solutions while increasing the throughput by up to 120%.

This work presents a model for designing and deploying time-critical, cloud-native applications under fault conditions. Our model considers the interactions and interferences among service components, as well as the possible occurrence of faults. Given a set of to-be-deployed applications with precise temporal constraints and a predefined configuration of the service components, we devised an optimizer to verify at design time if the cloud services guarantee compliance with the timing constraints while minimizing the resources needed to achieve fault tolerance.

Numerous edge applications that rely on video analytics demand precise, low-latency processing of multiple video streams from cameras. When these cameras are mobile, such as when mounted on a car or a robot, the processing load on the shared edge GPU can vary considerably. Provisioning the edge with GPUs for the worst-case load can be expensive and, for many applications, not feasible.

This paper presents the Fault-Tolerant Real-Time Cloud (FTRTC) project that aims to design cloud computing infrastructures capable of hosting highly reliable and real-time applications. These applications are characterized by strict timing and reliability constraints, as well as critical failure scenarios. For instance, such requirements are commonly found in the context of Industry 4.0. We present a formalization of the problem of designing real-time cloud applications supporting an adjustable level of fault tolerance throughout their distributed execution in a cloud infrastructure. The contributions presented in this paper indicate important research directions when building cloud infrastructures able to supporting ultra-reliable real-time applications.

Calvin is an IoT framework for application development, deployment and execution in heterogeneous environments, that includes clouds, edge resources, and embedded or constrained resources. Inside Calvin, all the distributed resources are viewed as one environment by the application. The framework provides multi-tenancy and simplifies development of IoT applications, which are represented using a dataflow of application components (named actors) and their communication. The idea behind Calvin poses similarity with the serverless architecture and can be seen as Actor as a Service instead of Function as a Service. This makes Calvin very powerful as it does not only scale actors quickly but also provides an easy actor migration capability. In this work, we propose Calvin Constrained, an extension to the Calvin framework to cover resource-constrained devices. Due to limited memory and processing power of embedded devices, the constrained side of the framework can only support a limited subset of the Calvin features. The current implementation of Calvin Constrained supports actors implemented in C as well as Python, where the support for Python actors is enabled by using MicroPython as a statically allocated library, by this we enable the automatic management of state variables and enhance code re-usability. As would be expected, Python-coded actors demand more resources over C-coded ones. We show that the extra resources needed are manageable on current off-the-shelve micro-controller-equipped devices when using the Calvin framework.

Today's telecommunications systems use a limited audio signal bandwidth. A typical bandwidth is 0.3-3.4 kHz, but recently it has been suggested that mobile phone networks will facilitate an audio signal bandwidth of 50 Hz-7 kHz. This is suggested since an increased bandwidth will increase the sound quality of the speech signals. Since only few telephones initially will have this facility, a method extending the conventional narrow frequency-band speech signal into a wide-band speech signal utilizing the receiving telephone only is suggested. This will give the impression of a wide-band speech signal. The proposed speech bandwidth extension method is based on models of speech acoustics and fundamentals of human hearing. The extension maps each speech feature separately. Care has been taken to deal with implementation aspects, such as noisy speech signals, speech signal delays, computational complexity, and processing memory usage.

This paper presents a method for least squares design of nonuniform filter banks for application in subband signal processing. Design objectives aim to optimize the filter bank frequency response while minimizing subband and output aliasing. Aliasing is minimized although magnitude and phase changes affect the aliasing terms. Filter banks with increasing bandwidth are designed with the proposed method and evaluated in speech enhancement using a spectral subtraction algorithm. When using a nonuniform frequency resolution approximating that of the human auditory system it is shown that an increased noise reduction and SNR improvement is achieved while maintaining the speech quality for a fixed number of frequency-bands.

A common narrow-band speech signal is expanded into a wide-band speech signal. The expanded signal gives the impression of a wide-band speech signal regardless of what type of vocoder is used in a receiver. The robust techniques suggested herein are based on speech acoustics and fundamentals of human hearing. That is the techniques extend the harmonic structure of the speech signal during voiced speech segments and introduce a linearly estimated amount of speech energy in the wide frequency-band. During unvoiced speech segments, a fricated noise may be introduced in the upper frequency-band.

In hands-free speech communication, the signal-to-noise ratio (SNR) is often poor, which makes it difficult to have a relaxed conversation. By using noise suppression, the conversation quality can be improved. This paper describes a noise suppression algorithm based on spectral subtraction. The method employs a noise and speech-dependent gain function for each frequency component. Proper measures have been taken to obtain a corresponding causal filter and also to ensure that the circular convolution originating from fast Fourier transform (FFT) filtering yields a truly linear filtering. A novel method that uses spectrum-dependent adaptive averaging to decrease the variance of the gain function is also presented. The results show a 10-dB background noise reduction for all input SNR situations tested in the range -6 to 16 dB, as well as improvement in speech quality and reduction of noise artifacts as compared with conventional spectral subtraction methods.