Defining Service Level Agreements in Serverless Computing

The emergence of serverless computing has brought significant advancements to the delivery of computing resources to cloud users. With the abstraction of infrastructure, ecosystem, and execution environments, users could focus on their code while relying on the cloud provider to manage the abstracted layers. In addition, desirable features such as autoscaling and high availability became a provider’s responsibility and can be adopted by the user\u27s application at no extra overhead. Despite such advancements, significant challenges must be overcome as applications transition from monolithic stand-alone deployments to the ephemeral and stateless microservice model of serverless computing. These challenges pertain to the uniqueness of the conceptual and implementation models of serverless computing. One of the notable challenges is the complexity of defining Service Level Agreements (SLA) for serverless functions. As the serverless model shifts the administration of resources, ecosystem, and execution layers to the provider, users become mere consumers of the provider’s abstracted platform with no insight into its performance. Suboptimal conditions of the abstracted layers are not visible to the end-user who has no means to assess their performance. Thus, SLA in serverless computing must take into consideration the unique abstraction of its model. This work investigates the Service Level Agreement (SLA) modeling of serverless functions\u27 and serverless chains’ executions. We highlight how serverless SLA fundamentally differs from earlier cloud delivery models. We then propose an approach to define SLA for serverless functions by utilizing resource utilization fingerprints for functions\u27 executions and a method to assess if executions adhere to that SLA. We evaluate the approach’s accuracy in detecting SLA violations for a broad range of serverless application categories. Our validation results illustrate a high accuracy in detecting SLA violations resulting from resource contentions and provider’s ecosystem degradations. We conclude by presenting the empirical validation of our proposed approach, which could detect Execution-SLA violations with accuracy up to 99%

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

SLA management of non-computational services.

El incremento en el uso de arquitecturas orientadas a servicios en los últimos 15 años ha propiciado la propuesta de numerosas técnicas para automatizar y dar soporte al uso de dichos servicios. Un elemento fundamental en la provisión de servicios es el Acuerdo de Nivel de Servicio (ANS), donde se formalizan los requisitos y garantías de consumidor y proveedor respecto del rendimiento del servicio. Las propuestas para servicios computacionales, además de proveer modelos formales para describirlos, proponen la automatización de las diferentes etapas del ciclo de vida del ANS, tales como la negociación de las garantías para crear un ANS, el despliegue de servicios basados en el ANS, o la gestión de los recursos para cumplir las garantías provistas en el mismo. Sin embargo, en los servicios tradicionales, no computacionales, es decir, los servicios que no son ejecutados por recursos computacionales, tales como los servicios de logística o de desarrollo de software, la gestión de sus ANSs todavía se realiza por medios ad-hoc. Así, las soluciones existentes no pueden ser reutilizadas por diferentes servicios. Y, en la mayoría de los casos, esta gestión se hace de manera manual (p.e. revisión de los objetivos acordados en los ANSs de servicios de transporte), por lo que la evaluación de estos ANSs es susceptible a errores y se suele retrasar respecto a la ejecución del servicio (p.e. cuando el ANS ha finalizado), por lo que no se pueden tomar acciones preventivas para evitar el incumplimiento del ANS o estas acciones no son rentables. En estos escenarios, aparecen, además, acuerdos marco para un periodo largo (p.e. 1 aõ), durante el cual pueden aparecen ANSs relacionados con éste para un periodo más específico y el análisis de la coherencia entre acuerdos marco y acuerdos específicos es complicada de hacer durante la ejecución del servicio. En esta tesis, nos proponemos automatizar parcialmente la gestión de los ANSs de servicios no computacionales. Así, por un lado, proponemos que los modelos para servicios computacionales se extiendan a servicios no computacionales, de manera que permitan describir la operativa del servicio y sus garantías. Y, por otro lado, basado en estos modelos, proporcionamos el diseño de operaciones para gestionar el ciclo de vida de los ANS. Concretamente, estas operaciones se basan en las fases de despligue y evaluación del ANS. De forma específica, esta tesis propone tres contribuciones principales. Primero, (A) extender iAgree para dar soporte al modelado de los ANS de servicios no computacionales. Segundo, (B) dar soporte al ciclo de vida de dichos ANS mediante la formalización de las operaciones citadas (configuración del servicio basada en el ANS y monitorización del mismo) y, a partir de estas operaciones, implementamos una arquitectura de referencia para estas operaciones. Y, por último, (C) proveemos el modelado de la relación entre acuerdos marco y específicos que relacione sus términos junto con la formalización de las operaciones para el análisis que aparecen entre ellos. Otros aspectos del ciclo de vida del servicio y del ANS, como la gestión de los recursos para mejorar el rendimiento del servicio o el uso de técnicas (como machine learning) para la predicción del cumplimiento de los ANSs están fuera del contexto de esta tesis, pero se plantean como futuras líneas de extensión. Este trabajo se ha basado en ANSs reales de diferentes dominios, tales como servicios de Transporte y Logística, proveedores de Cloud or outsourcing de desarrollo TIC, que se han utilizado para validar las propuestas. Además, las contribuciones presentadas se han aplicado en el contexto de proyectos reales de soporte de sistemas TIC.The rise of computational services in the last 15 years brought the proposal of a number of techniques to automate and support their enactment. One key element in services is the Service Level Agreement (SLA), where the requirements of service customer are matched with the performance levels from the service provider to define service level guarantees and related responsibilities. The proposals from computational domains are oriented to automate the different stages in the SLA Lifecycle, such as the negotiation of terms which will form the SLA, the deployment of services based on the SLA artifact or the management of computational resources to accomplish SLA goals on runtime. However, traditional non-computational services, that is, services which are not performed by computational resources, such as logistics or software development services, are still supported by ad-hoc mechanisms. Therefore, the existing solutions for the management of their SLAs cannot be reused for other services. This management is usually manually performed (e.g.: reviewing of the goals of an SLA in transport service), so their evaluation is error-prone and delayed regarding the service execution (e.g.: when the SLA is finished), so preemptive actions to avoid SLA violations cannot be taken or/and are expensive to perform. Furthermore, these SLAs are sometimes described on a long term basis (frame agreements), and related SLAs can appear for a shorter term (specific agreements) and the analysis of the validity among them is complex to perform on runtime. In this dissertation, we aim at partially automate the management of SLAs in noncomputational services. On the one hand, we suggest that existing models for computational services can be extended to non computational services and enable the description of the service operative and their guarantees. And, on the other hand, we provide a design for operations to partially support the SLA Lifecycle, based on the previous models. Specifically, these operations are mainly focused on the deployment and fulfillment stages of the SLA. Therefore, the contributions of this dissertation are three. First, (A) providing a model to describe Service Level Agreements of non computational services, as an extension of iAgree, an existing model for SLAs of computational services. Second side, (B) supporting the SLA Lifecycle with the design of the aforementioned operations (service configuration based on SLA and monitoring of SLA) and implementing a reference architecture for such operations. And, lastly, (C) providing a model for frame and specific agreements which relates their terms and formalises the analysis operations among them. Other related operations of the service lifecycle as the management of resources to improve service performance or the use of novel techniques (such as machine learning) to predict the SLA accomplishment are out of the scope of this thesis but planned as future line of extension. The current dissertation has been based on real SLAs from different domains, such as Transport & Logistics, public Cloud providers or IT Maintenance outsourcing, which have been used to validate the proposal. And, furthermore, the contributions have been applied in the context of real IT Maintenance outsourcing projects

Sonic interactions in virtual environments

This book tackles the design of 3D spatial interactions in an audio-centered and audio-first perspective, providing the fundamental notions related to the creation and evaluation of immersive sonic experiences. The key elements that enhance the sensation of place in a virtual environment (VE) are: Immersive audio: the computational aspects of the acoustical-space properties of Virutal Reality (VR) technologies Sonic interaction: the human-computer interplay through auditory feedback in VE VR systems: naturally support multimodal integration, impacting different application domains Sonic Interactions in Virtual Environments will feature state-of-the-art research on real-time auralization, sonic interaction design in VR, quality of the experience in multimodal scenarios, and applications. Contributors and editors include interdisciplinary experts from the fields of computer science, engineering, acoustics, psychology, design, humanities, and beyond. Their mission is to shape an emerging new field of study at the intersection of sonic interaction design and immersive media, embracing an archipelago of existing research spread in different audio communities and to increase among the VR communities, researchers, and practitioners, the awareness of the importance of sonic elements when designing immersive environments

Effective techniques for understanding and improving data structure usage

Turing Award winner Niklaus Wirth famously noted, `Algorithms + Data Structures = Programs', and it follows that data structures should be carefully considered for effective application development. In fact, data structures are the main focus of program understanding, performance engineering, bug detection, and security enhancement, etc. Our research is aimed at providing effective techniques for analyzing and improving data structure usage in fundamentally new approaches: First, detecting data structures; identifying what data structures are used within an application is a critical step toward application understanding and performance engineering. Second, selecting efficient data structures; analyzing data structures' behavior can recognize improper use of data structures and suggest alternative data structures better suited for the current situation where the application runs. Third, detecting memory leaks for data structures; tracking data accesses with little overhead and their careful analysis can enable practical and accurate memory leak detection. Finally, offloading time-consuming data structure operations; By leveraging a dedicated helper thread that executes the operations on the behalf of the application thread, we can improve the overall performance of the application.Ph.D

The role of graph entropy in fault localization and network evolution

The design of a communication network has a critical impact on its effectiveness at delivering service to the users of a large scale compute infrastructure. In particular, the reliability of such networks is increasingly vital in the modern world, as more and more of our commercial and social activity is conducted using digital platforms. Systems to assure service availability have been available since the emergence of Mainframes, with the System 360 in 1964, and although commercially widespread, the scientific understanding is not as deep as the problem warrants. The basic operating principle of most service assurance systems combines the gathering of status messages, which we term as events, with algorithms to deduce from the events where potential failures may be occurring. The algorithms to identify which events are causal, known as root cause analysis or fault localization, usually rely upon a detailed understanding of the network structure in order to determine those events that are most helpful in diagnosing and remediating a service threatening problem. The complex nature of root cause algorithms introduces scalability limits in terms of the number of events that can be processed per second. Unfortunately as networks grow, the volume of events produced continues to increase, often dramatically. The dependence of root cause analysis algorithms on network structure presents a significant challenge as networks continue to grow in scale and complexity. As a consequence of this, and the growing reliance upon networks as part of the key fabric of the modern economy, the commercial importance and the scale of the engineering challenges are increasing significantly. In this thesis I outline a novel approach to improving the scalability of event processing using a mathematical property of networks, graph entropy. In the first two papers described in this thesis, I apply an efficiently computable approximation of graph entropy to the problem of identifying important nodes in a network. In this context, importance is a measure of whether the failure of a node is more likely to result in a significant impact on the overall connectivity of the network, and therefore likely to lead to an interruption of service. I show that by ignoring events from unimportant network nodes it is possible to significantly reduce the event rate that a root cause algorithm needs to process. Further, I demonstrate that unimportant nodes produce very many events, but very few root causes. The consequence is that although some events relating to root causes are missed, this is compensated for by the reduction in overall event rate. This leads to a significant reduction of the event processing load on management systems, and therefore increases the effectiveness of current approaches to root cause analysis on large networks. Analysis of the topology data used in the first two papers revealed interesting anomalies in the degree distribution of the network nodes. This motivated the later focus of my research to investigate how graph entropy and network design considerations could be applied to the dynamical evolution of networks structures, most commonly described using the Preferential Attachment model of Barabási and Albert. A common feature of a communication network is the presence of a constraint on the number of logical or physical connections a device can support. In the last of the three papers in the thesis I develop and present a constrained model of network evolution, which demonstrates better quantitative agreement with real world networks than the preferential attachment model. This model, developed using the continuum approach, still does not address a fundamental question of random networks as a model of network evolution. Why should a node’s degree influence the likelihood of it acquiring connections? In the same paper I attempt to answer that question by outlining a model that links vertex entropy to a node’s attachment probability. The model successfully reproduces some of the characteristics of preferential attachment, and illustrates the potential for entropic arguments in network science. Put together, the two main bodies of work constitute a practical advance on the state of the art of fault localization, and a theoretical insight into the inner workings of dynamic networks. They open up a number of interesting avenues for further investigation

Accelerating Network Functions using Reconfigurable Hardware. Design and Validation of High Throughput and Low Latency Network Functions at the Access Edge

Providing Internet access to billions of people worldwide is one of the main technical challenges in the current decade. The Internet access edge connects each residential and mobile subscriber to this network and ensures a certain Quality of Service (QoS). However, the implementation of access edge functionality challenges Internet service providers: First, a good QoS must be provided to the subscribers, for example, high throughput and low latency. Second, the quick rollout of new technologies and functionality demands flexible configuration and programming possibilities of the network components; for example, the support of novel, use-case-specific network protocols. The functionality scope of an Internet access edge requires the use of programming concepts, such as Network Functions Virtualization (NFV). The drawback of NFV-based network functions is a significantly lowered resource efficiency due to the execution as software, commonly resulting in a lowered QoS compared to rigid hardware solutions. The usage of programmable hardware accelerators, named NFV offloading, helps to improve the QoS and flexibility of network function implementations. In this thesis, we design network functions on programmable hardware to improve the QoS and flexibility. First, we introduce the host bypassing concept for improved integration of hardware accelerators in computer systems, for example, in 5G radio access networks. This novel concept bypasses the system’s main memory and enables direct connectivity between the accelerator and network interface card. Our evaluations show an improved throughput and significantly lowered latency jitter for the presented approach. Second, we analyze different programmable hardware technologies for hardware-accelerated Internet subscriber handling, including three P4-programmable platforms and FPGAs. Our results demonstrate that all approaches have excellent performance and are suitable for Internet access creation. We present a fully-fledged User Plane Function (UPF) designed upon these concepts and test it in an end-to-end 5G standalone network as part of this contribution. Third, we analyze and demonstrate the usability of Active Queue Management (AQM) algorithms on programmable hardware as an expansion to the access edge. We show the feasibility of the CoDel AQM algorithm and discuss the challenges and constraints to be considered when limited hardware is used. The results show significant improvements in the QoS when the AQM algorithm is deployed on hardware. Last, we focus on network function benchmarking, which is crucial for understanding the behavior of implementations and their optimization, e.g., Internet access creation. For this, we introduce the load generation and measurement framework P4STA, benefiting from flexible software-based load generation and hardware-assisted measuring. Utilizing programmable network switches, we achieve a nanosecond time accuracy while generating test loads up to the available Ethernet link speed

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest