Energy-efficient bandwidth allocation for multiuser scalable video streaming over WLAN

We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while meeting the user required visual quality constraints. The framework relies on the IEEE 802.11 standard and on the embedded bitstream structure of the scalable video coding scheme. It integrates an application-level video quality metric as QoS constraint (instead of a communication layer quality metric) with energy consumption optimization through link layer scaling and sleeping. Both energy minimization and min-max energy optimization strategies are discussed. Simulation results demonstrate significant energy gains compared to the state-of-the-art approaches

Performance and Reliability Evaluation of Apache Kafka Messaging System

Streaming data is now flowing across various devices and applications around us. This type of data means any unbounded, ever growing, infinite data set which is continuously generated by all kinds of sources. Examples include sensor data transmitted among different Internet of Things (IoT) devices, user activity records collected on websites and payment requests sent from mobile devices. In many application scenarios, streaming data needs to be processed in real-time because its value can be futile over time. A variety of stream processing systems have been developed in the last decade and are evolving to address rising challenges. A typical stream processing system consists of multiple processing nodes in the topology of a DAG (directed acyclic graph). To build real-time streaming data pipelines across those nodes, message middleware technology is widely applied. As a distributed messaging system with high durability and scalability, Apache Kafka has become very popular among modern companies. It ingests streaming data from upstream applications and store the data in its distributed cluster, which provides a fault-tolerant data source for stream processors. Therefore, Kafka plays a critical role to ensure the completeness, correctness and timeliness of streaming data delivery. However, it is impossible to meet all the user requirements in real-time cases with a simple and fixed data delivery strategy. In this thesis, we address the challenge of choosing a proper configuration to guarantee both performance and reliability of Kafka for complex streaming application scenarios. We investigate the features that have an impact on the performance and reliability metrics. We propose a queueing based prediction model to predict the performance metrics, including producer throughput and packet latency of Kafka. We define two reliability metrics, the probability of message loss and the probability of message duplication. We create an ANN model to predict these metrics given unstable network metrics like network delay and packet loss rate. To collect sufficient training data we build a Docker-based Kafka testbed with a fault injection module. We use a new quality-of-service metric, timely throughput to help us choosing proper batch size in Kafka. Based on this metric, we propose a dynamic configuration method, which reactively guarantees both performance and reliability of Kafka under complex operation conditions

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

Content-Aware Multimedia Communications

The demands for fast, economic and reliable dissemination of multimedia information are steadily growing within our society. While people and economy increasingly rely on communication technologies, engineers still struggle with their growing complexity. Complexity in multimedia communication originates from several sources. The most prominent is the unreliability of packet networks like the Internet. Recent advances in scheduling and error control mechanisms for streaming protocols have shown that the quality and robustness of multimedia delivery can be improved significantly when protocols are aware of the content they deliver. However, the proposed mechanisms require close cooperation between transport systems and application layers which increases the overall system complexity. Current approaches also require expensive metrics and focus on special encoding formats only. A general and efficient model is missing so far. This thesis presents efficient and format-independent solutions to support cross-layer coordination in system architectures. In particular, the first contribution of this work is a generic dependency model that enables transport layers to access content-specific properties of media streams, such as dependencies between data units and their importance. The second contribution is the design of a programming model for streaming communication and its implementation as a middleware architecture. The programming model hides the complexity of protocol stacks behind simple programming abstractions, but exposes cross-layer control and monitoring options to application programmers. For example, our interfaces allow programmers to choose appropriate failure semantics at design time while they can refine error protection and visibility of low-level errors at run-time. Based on some examples we show how our middleware simplifies the integration of stream-based communication into large-scale application architectures. An important result of this work is that despite cross-layer cooperation, neither application nor transport protocol designers experience an increase in complexity. Application programmers can even reuse existing streaming protocols which effectively increases system robustness.Der Bedarf unsere Gesellschaft nach kostengünstiger und zuverlässiger Kommunikation wächst stetig. Während wir uns selbst immer mehr von modernen Kommunikationstechnologien abhängig machen, müssen die Ingenieure dieser Technologien sowohl den Bedarf nach schneller Einführung neuer Produkte befriedigen als auch die wachsende Komplexität der Systeme beherrschen. Gerade die Übertragung multimedialer Inhalte wie Video und Audiodaten ist nicht trivial. Einer der prominentesten Gründe dafür ist die Unzuverlässigkeit heutiger Netzwerke, wie z.B.~dem Internet. Paketverluste und schwankende Laufzeiten können die Darstellungsqualität massiv beeinträchtigen. Wie jüngste Entwicklungen im Bereich der Streaming-Protokolle zeigen, sind jedoch Qualität und Robustheit der Übertragung effizient kontrollierbar, wenn Streamingprotokolle Informationen über den Inhalt der transportierten Daten ausnutzen. Existierende Ansätze, die den Inhalt von Multimediadatenströmen beschreiben, sind allerdings meist auf einzelne Kompressionsverfahren spezialisiert und verwenden berechnungsintensive Metriken. Das reduziert ihren praktischen Nutzen deutlich. Außerdem erfordert der Informationsaustausch eine enge Kooperation zwischen Applikationen und Transportschichten. Da allerdings die Schnittstellen aktueller Systemarchitekturen nicht darauf vorbereitet sind, müssen entweder die Schnittstellen erweitert oder alternative Architekturkonzepte geschaffen werden. Die Gefahr beider Varianten ist jedoch, dass sich die Komplexität eines Systems dadurch weiter erhöhen kann. Das zentrale Ziel dieser Dissertation ist es deshalb, schichtenübergreifende Koordination bei gleichzeitiger Reduzierung der Komplexität zu erreichen. Hier leistet die Arbeit zwei Beträge zum aktuellen Stand der Forschung. Erstens definiert sie ein universelles Modell zur Beschreibung von Inhaltsattributen, wie Wichtigkeiten und Abhängigkeitsbeziehungen innerhalb eines Datenstroms. Transportschichten können dieses Wissen zur effizienten Fehlerkontrolle verwenden. Zweitens beschreibt die Arbeit das Noja Programmiermodell für multimediale Middleware. Noja definiert Abstraktionen zur Übertragung und Kontrolle multimedialer Ströme, die die Koordination von Streamingprotokollen mit Applikationen ermöglichen. Zum Beispiel können Programmierer geeignete Fehlersemantiken und Kommunikationstopologien auswählen und den konkreten Fehlerschutz dann zur Laufzeit verfeinern und kontrolliere

Distributed deep learning inference in fog networks

Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs). Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks. This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency

Real-time performance diagnosis and evaluation of big data systems in cloud datacenters

PhD ThesisModern big data processing systems are becoming very complex in terms of largescale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems’ performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. Performance diagnosis and prediction of big data systems are highly complex as these frameworks are typically deployed in cloud data centers that are large-scale, highly concurrent, and follows a multi-tenant model. Several factors, including hardware heterogeneity, stochastic networks and application workloads may impact the performance of big data systems. The current state-of-the-art does not sufficiently address the challenge of determining complex, usually stochastic and hidden relationships between these factors. To handle performance diagnosis and evaluation of big data systems in cloud environments, this thesis proposes multilateral research towards monitoring and performance diagnosis and prediction in cloud-based large-scale distributed systems by involving a novel combination of an effective and efficient deployment pipeline.The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors. The key contributions of this dissertation are listed below: - i - • Designing a real-time big data monitoring system called SmartMonit that efficiently collects the runtime system information including computing resource utilization and job execution information and then interacts the collected information with the Execution Graph modeled as directed acyclic graphs (DAGs). • Developing AutoDiagn, an automated real-time diagnosis framework for big data systems, that automatically detects performance degradation and inefficient resource utilization problems, while providing an online detection and semi-online root-cause analysis for a big data system. • Designing a novel root-cause analysis technique/system called BigPerf for big data systems that analyzes and characterizes the performance of big data applications by incorporating Bayesian networks to determine uncertain and complex relationships between performance related factors.State of the Republic of Turkey and the Turkish Ministry of National Educatio

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model