Managed Control of Composite Cloud Systems

Cloud providers have just begun to provide primitive functionality enabling users to configure and easily provision resources, primarily in the infrastructure as a service domain. In order to effectively manage cloud resources in an automated fashion, systems must automate quality-of-service (QoS) metric measurement as a part of a larger usage management strategy. Collected metrics can then be used within control loops to manage and provision cloud resources. This basic approach can be scaled to monitor the use of system artifacts as well as simple QoS parameters, and can also address the needs of large systems spanning the boundaries of single service providers though the problem seems to moving toward intractability

Development of a Dynamic Performance Management Framework for Naval Ship Power System using Model-Based Predictive Control

Medium-Voltage Direct-Current (MVDC) power system has been considered as the trending technology for future All-Electric Ships (AES) to produce, convert and distribute electrical power. With the wide employment of highrequency power electronics converters and motor drives in DC system, accurate and fast assessment of system dynamic behaviors , as well as the optimization of system transient performance have become serious concerns for system-level studies, high-level control designs and power management algorithm development. The proposed technique presents a coordinated and automated approach to determine the system adjustment strategy for naval power systems to improve the transient performance and prevent potential instability following a system contingency. In contrast with the conventional design schemes that heavily rely on the human operators and pre-specified rules/set points, we focus on the development of the capability to automatically and efficiently detect and react to system state changes following disturbances and or damages by incooperating different system components to formulate an overall system-level solution. To achieve this objective, we propose a generic model-based predictive management framework that can be applied to a variety of Shipboard Power System (SPS) applications to meet the stringent performance requirements under different operating conditions. The proposed technique is proven to effectively prevent the system from instability caused by known and unknown disturbances with little or none human intervention under a variety of operation conditions. The management framework proposed in this dissertation is designed based on the concept of Model Predictive Control (MPC) techniques. A numerical approximation of the actual system is used to predict future system behaviors based on the current states and the candidate control input sequences. Based on the predictions the optimal control solution is chosen and applied as the current control input. The effectiveness and efficiency of the proposed framework can be evaluated conveniently based on a series of performance criteria such as fitness, robustness and computational overhead. An automatic system modeling, analysis and synthesis software environment is also introduced in this dissertation to facilitate the rapid implementation of the proposed performance management framework according to various testing scenarios

Parallel Patterns for Adaptive Data Stream Processing

In recent years our ability to produce information has been growing steadily, driven by an ever increasing computing power, communication rates, hardware and software sensors diffusion. This data is often available in the form of continuous streams and the ability to gather and analyze it to extract insights and detect patterns is a valuable opportunity for many businesses and scientific applications. The topic of Data Stream Processing (DaSP) is a recent and highly active research area dealing with the processing of this streaming data. The development of DaSP applications poses several challenges, from efficient algorithms for the computation to programming and runtime systems to support their execution. In this thesis two main problems will be tackled: * need for high performance: high throughput and low latency are critical requirements for DaSP problems. Applications necessitate taking advantage of parallel hardware and distributed systems, such as multi/manycores or cluster of multicores, in an effective way; * dynamicity: due to their long running nature (24hr/7d), DaSP applications are affected by highly variable arrival rates and changes in their workload characteristics. Adaptivity is a fundamental feature in this context: applications must be able to autonomously scale the used resources to accommodate dynamic requirements and workload while maintaining the desired Quality of Service (QoS) in a cost-effective manner. In the current approaches to the development of DaSP applications are still missing efficient exploitation of intra-operator parallelism as well as adaptations strategies with well known properties of stability, QoS assurance and cost awareness. These are the gaps that this research work tries to fill, resorting to well know approaches such as Structured Parallel Programming and Control Theoretic models. The dissertation runs along these two directions. The first part deals with intra-operator parallelism. A DaSP application can be naturally expressed as a set of operators (i.e. intermediate computations) that cooperate to reach a common goal. If QoS requirements are not met by the current implementation, bottleneck operators must be internally parallelized. We will study recurrent computations in window based stateful operators and propose patterns for their parallel implementation. Windowed operators are the most representative class of stateful data stream operators. Here computations are applied on the most recent received data. Windows are dynamic data structures: they evolve over time in terms of content and, possibly, size. Therefore, with respect to traditional patterns, the DaSP domain requires proper specializations and enhanced features concerning data distribution and management policies for different windowing methods. A structured approach to the problem will reduce the effort and complexity of parallel programming. In addition, it simplifies the reasoning about the performance properties of a parallel solution (e.g. throughput and latency). The proposed patterns exhibit different properties in terms of applicability and profitability that will be discussed and experimentally evaluated. The second part of the thesis is devoted to the proposal and study of predictive strategies and reconfiguration mechanisms for autonomic DaSP operators. Reconfiguration activities can be implemented in a transparent way to the application programmer thanks to the exploitation of parallel paradigms with well known structures. Furthermore, adaptation strategies may take advantage of the QoS predictability of the used parallel solution. Autonomous operators will be driven by means of a Model Predictive Control approach, with the intent of giving QoS assurances in terms of throughput or latency in a resource-aware manner. An experimental section will show the effectiveness of the proposed approach in terms of execution costs reduction as well as the stability degree of a system reconfiguration. The experiments will target shared and distributed memory architectures

On the application of predictive control techniques for adaptive performance management of computing systems

This paper addresses adaptive performance management of real-time computing systems. We consider a generic model-based predictive control approach that can be applied to a variety of computing applications in which the system performance must be tuned using a finite set of control inputs. The paper focuses on several key aspects affecting the application of this control technique to practical systems. In particular, we present techniques to enhance the speed of the control algorithm for real-time systems. Next we study the feasibility of the predictive control policy for a given system model and performance specification under uncertain operating conditions. The paper then introduces several measures to characterize the performance of the controller, and presents a generic tool for system modeling and automatic control synthesis. Finally, we present a case study involving a real-time computing system to demonstrate the applicability of the predictive control framework

On the application of predictive control techniques for adaptive performance management of computing systems

This paper addresses adaptive performance management of real-time computing systems. We consider a generic model-based predictive control approach that can be applied to a variety of computing applications in which the system performance must be tuned using a finite set of control inputs. The paper focuses on several key aspects affecting the application of this control technique to practical systems. In particular, we present techniques to enhance the speed of the control algorithm for real-time systems. Next we study the feasibility of the predictive control policy for a given system model and performance specification under uncertain operating conditions. The paper then introduces several measures to characterize the performance of the controller, and presents a generic tool for system modeling and automatic control synthesis. Finally, we present a case study involving a real-time computing system to demonstrate the applicability of the predictive control framework