An Emergent Architecture for Scaling Decentralized Communication Systems (DCS)

With recent technological advancements now accelerating the mobile and wireless Internet solution space, a ubiquitous computing Internet is well within the research and industrial community's design reach - a decentralized system design, which is not solely driven by static physical models and sound engineering principals, but more dynamically, perhaps sub-optimally at initial deployment and socially-influenced in its evolution. To complement today's Internet system, this thesis proposes a Decentralized Communication System (DCS) architecture with the following characteristics: flat physical topologies with numerous compute oriented and communication intensive nodes in the network with many of these nodes operating in multiple functional roles; self-organizing virtual structures formed through alternative mobility scenarios and capable of serving ad hoc networking formations; emergent operations and control with limited dependency on centralized control and management administration. Today, decentralized systems are not commercially scalable or viable for broad adoption in the same way we have to come to rely on the Internet or telephony systems. The premise in this thesis is that DCS can reach high levels of resilience, usefulness, scale that the industry has come to experience with traditional centralized systems by exploiting the following properties: (i.) network density and topological diversity; (ii.) self-organization and emergent attributes; (iii.) cooperative and dynamic infrastructure; and (iv.) node role diversity. This thesis delivers key contributions towards advancing the current state of the art in decentralized systems. First, we present the vision and a conceptual framework for DCS. Second, the thesis demonstrates that such a framework and concept architecture is feasible by prototyping a DCS platform that exhibits the above properties or minimally, demonstrates that these properties are feasible through prototyped network services. Third, this work expands on an alternative approach to network clustering using hierarchical virtual clusters (HVC) to facilitate self-organizing network structures. With increasing network complexity, decentralized systems can generally lead to unreliable and irregular service quality, especially given unpredictable node mobility and traffic dynamics. The HVC framework is an architectural strategy to address organizational disorder associated with traditional decentralized systems. The proposed HVC architecture along with the associated promotional methodology organizes distributed control and management services by leveraging alternative organizational models (e.g., peer-to-peer (P2P), centralized or tiered) in hierarchical and virtual fashion. Through simulation and analytical modeling, we demonstrate HVC efficiencies in DCS structural scalability and resilience by comparing static and dynamic HVC node configurations against traditional physical configurations based on P2P, centralized or tiered structures. Next, an emergent management architecture for DCS exploiting HVC for self-organization, introduces emergence as an operational approach to scaling DCS services for state management and policy control. In this thesis, emergence scales in hierarchical fashion using virtual clustering to create multiple tiers of local and global separation for aggregation, distribution and network control. Emergence is an architectural objective, which HVC introduces into the proposed self-management design for scaling and stability purposes. Since HVC expands the clustering model hierarchically and virtually, a clusterhead (CH) node, positioned as a proxy for a specific cluster or grouped DCS nodes, can also operate in a micro-capacity as a peer member of an organized cluster in a higher tier. As the HVC promotional process continues through the hierarchy, each tier of the hierarchy exhibits emergent behavior. With HVC as the self-organizing structural framework, a multi-tiered, emergent architecture enables the decentralized management strategy to improve scaling objectives that traditionally challenge decentralized systems. The HVC organizational concept and the emergence properties align with and the view of the human brain's neocortex layering structure of sensory storage, prediction and intelligence. It is the position in this thesis, that for DCS to scale and maintain broad stability, network control and management must strive towards an emergent or natural approach. While today's models for network control and management have proven to lack scalability and responsiveness based on pure centralized models, it is unlikely that singular organizational models can withstand the operational complexities associated with DCS. In this work, we integrate emergence and learning-based methods in a cooperative computing manner towards realizing DCS self-management. However, unlike many existing work in these areas which break down with increased network complexity and dynamics, the proposed HVC framework is utilized to offset these issues through effective separation, aggregation and asynchronous processing of both distributed state and policy. Using modeling techniques, we demonstrate that such architecture is feasible and can improve the operational robustness of DCS. The modeling emphasis focuses on demonstrating the operational advantages of an HVC-based organizational strategy for emergent management services (i.e., reachability, availability or performance). By integrating the two approaches, the DCS architecture forms a scalable system to address the challenges associated with traditional decentralized systems. The hypothesis is that the emergent management system architecture will improve the operational scaling properties of DCS-based applications and services. Additionally, we demonstrate structural flexibility of HVC as an underlying service infrastructure to build and deploy DCS applications and layered services. The modeling results demonstrate that an HVC-based emergent management and control system operationally outperforms traditional structural organizational models. In summary, this thesis brings together the above contributions towards delivering a scalable, decentralized system for Internet mobile computing and communications

Aqpet — An R package for air quality policy evaluation

Evaluating the effectiveness of clean air policies is important in the cycle of air quality management, ensuring policy accountability and informing future policy-making processes. However, such evaluations are challenging due to complex confounding factors such as varying weather conditions or seasonal or annual changes in air quality unrelated to the policy implementation. To address this challenge, we developed 'aqpet', a R package designed to streamline the quantification of policy effects on air quality using observational data. The package 'aqpet' includes: (1) automated-machine learning to predict air pollutants under average weather conditions – a process term as "weather normalisation"; (2) augmented synthetic control method (ASCM) to quantify the actual policy impact on air pollution. 'aqpet' offers functions for data collection and preparation, building auto-machine learning models, conducting weather normalisation, model performance evaluation and explanation, and causal impact analysis using ASCM. 'aqpet' enables fast, efficient, and interactive policy analysis for air quality management.</p

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D

Data Mining; A Conceptual Overview

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners

Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Machine Learning for Resource-Constrained Computing Systems

Die verfügbaren Ressourcen in Informationsverarbeitungssystemen wie Prozessoren sind in der Regel eingeschränkt. Das umfasst z. B. die elektrische Leistungsaufnahme, den Energieverbrauch, die Wärmeabgabe oder die Chipfläche. Daher ist die Optimierung der Verwaltung der verfügbaren Ressourcen von größter Bedeutung, um Ziele wie maximale Performanz zu erreichen. Insbesondere die Ressourcenverwaltung auf der Systemebene hat über die (dynamische) Zuweisung von Anwendungen zu Prozessorkernen und über die Skalierung der Spannung und Frequenz (dynamic voltage and frequency scaling, DVFS) einen großen Einfluss auf die Performanz, die elektrische Leistung und die Temperatur während der Ausführung von Anwendungen. Die wichtigsten Herausforderungen bei der Ressourcenverwaltung sind die hohe Komplexität von Anwendungen und Plattformen, unvorhergesehene (zur Entwurfszeit nicht bekannte) Anwendungen oder Plattformkonfigurationen, proaktive Optimierung und die Minimierung des Laufzeit-Overheads. Bestehende Techniken, die auf einfachen Heuristiken oder analytischen Modellen basieren, gehen diese Herausforderungen nur unzureichend an. Aus diesem Grund ist der Hauptbeitrag dieser Dissertation der Einsatz maschinellen Lernens (ML) für Ressourcenverwaltung. ML-basierte Lösungen ermöglichen die Bewältigung dieser Herausforderungen durch die Vorhersage der Auswirkungen potenzieller Entscheidungen in der Ressourcenverwaltung, durch Schätzung verborgener (unbeobachtbarer) Eigenschaften von Anwendungen oder durch direktes Lernen einer Ressourcenverwaltungs-Strategie. Diese Dissertation entwickelt mehrere neuartige ML-basierte Ressourcenverwaltung-Techniken für verschiedene Plattformen, Ziele und Randbedingungen. Zunächst wird eine auf Vorhersagen basierende Technik zur Maximierung der Performanz von Mehrkernprozessoren mit verteiltem Last-Level Cache und limitierter Maximaltemperatur vorgestellt. Diese verwendet ein neuronales Netzwerk (NN) zur Vorhersage der Auswirkungen potenzieller Migrationen von Anwendungen zwischen Prozessorkernen auf die Performanz. Diese Vorhersagen erlauben die Bestimmung der bestmöglichen Migration und ermöglichen eine proaktive Verwaltung. Das NN ist so trainiert, dass es mit unbekannten Anwendungen und verschiedenen Temperaturlimits zurechtkommt. Zweitens wird ein Boosting-Verfahren zur Maximierung der Performanz homogener Mehrkernprozessoren mit limitierter Maximaltemperatur mithilfe von DVFS vorgestellt. Dieses basiert auf einer neuartigen {Boostability}-Metrik, die die Abhängigkeiten von Performanz, elektrischer Leistung und Temperatur auf Spannungs/Frequenz-Änderungen in einer Metrik vereint. % ignorerepeated Die Abhängigkeiten von Performanz und elektrischer Leistung hängen von der Anwendung ab und können zur Laufzeit nicht direkt beobachtet (gemessen) werden. Daher wird ein NN verwendet, um diese Werte für unbekannte Anwendungen zu schätzen und so die Komplexität der Boosting-Optimierung zu bewältigen. Drittens wird eine Technik zur Temperaturminimierung von heterogenen Mehrkernprozessoren mit Quality of Service-Zielen vorgestellt. Diese verwendet Imitationslernen, um eine Migrationsstrategie von Anwendungen aus optimalen Orakel-Demonstrationen zu lernen. Dafür wird ein NN eingesetzt, um die Komplexität der Plattform und des Anwendungsverhaltens zu bewältigen. Die Inferenz des NNs wird mit Hilfe eines vorhandenen generischen Beschleunigers, einer Neural Processing Unit (NPU), beschleunigt. Auch die ML Algorithmen selbst müssen auch mit begrenzten Ressourcen ausgeführt werden. Zuletzt wird eine Technik für ressourcenorientiertes Training auf verteilten Geräten vorgestellt, um einen konstanten Trainingsdurchsatz bei sich schnell ändernder Verfügbarkeit von Rechenressourcen aufrechtzuerhalten, wie es z.~B.~aufgrund von Konflikten bei gemeinsam genutzten Ressourcen der Fall ist. Diese Technik verwendet Structured Dropout, welches beim Training zufällige Teile des NNs auslässt. Dadurch können die erforderlichen Ressourcen für das Training dynamisch angepasst werden -- mit vernachlässigbarem Overhead, aber auf Kosten einer langsameren Trainingskonvergenz. Die Pareto-optimalen Dropout-Parameter pro Schicht des NNs werden durch eine Design Space Exploration bestimmt. Evaluierungen dieser Techniken werden sowohl in Simulationen als auch auf realer Hardware durchgeführt und zeigen signifikante Verbesserungen gegenüber dem Stand der Technik, bei vernachlässigbarem Laufzeit-Overhead. Zusammenfassend zeigt diese Dissertation, dass ML eine Schlüsseltechnologie zur Optimierung der Verwaltung der limitierten Ressourcen auf Systemebene ist, indem die damit verbundenen Herausforderungen angegangen werden

ADVANCED SLA MANAGEMENT IN CLOUD COMPUTING

The advent of high-performance technologies and the increase in volume of data used by organizations led to the need for migration from an internal structure to Cloud environment. The continuous development of tools, methods and techniques have expanded the understanding of the various functions, structures and processes related to Cloud Computing. However, the increase in computing power led to the development and use of more complex models, including this scope the complexity of Service Level Agreements (SLA). The need for understanding at a high level of SLAs established between customers and service providers in Cloud led to different studies on the definition and standardization of these agreements. Nowadays, cloud computing technologies are becoming more and more popular, especially with respect to data storage. However, the processes used to determine the Cloud Service Agreements do not consider the final customer\u2019s needs, considering only the supply capacity of the service provider. For these reasons, the development of service agreements that meets the needs of customers should be designed in order to increase the usability of Cloud environments, and enabling the discovery of new areas of application in accordance with market demand. In this context, the use of ontologies that describes the information that composes each type of service, and thus enable an understanding of the agreements reached, is configured as an approach to be considered. Moreover, the generalization and abstraction of information that can be observed in different services allows a broader vision for managing SLAs. For these reasons, this thesis aims to find innovative methods for the composition of Service Level Agreements in Cloud Computing. In particular, the methods presented allow demonstrate the convergence of several consolidated techniques in research on Cloud SLA using a new approach that considers new demands on Cloud and allows control of the established agreements, in addition to effectively ensure the application of the concept of XaaS (everything as a service). The originality of the approach allows the registration, search, composition and control of services in Cloud using the same structure. The new approach presented in this thesis allows the understanding of the impact of the new services requested by customers, giving the provider the possibility of simulating the use of the necessary resources to meet the new services\u2019 requests. From the presentation of a conceptual framework we can demonstrate the use of our approach through the examples of different situations presented in the real world and considering the new market possibilities