Artificial Intelligence in Business: how has AI affected business? Use and implications of autonomic computing for business information systems

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ABSTRACT

The advances in computing and communication technologies and software tools have resulted in an explosive growth in networked applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. In a similar way, the underlying information infrastructure, e.g. the Internet, is large, complex, heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources, data stores and sensor networks. The combination of the two results in application development, configuration and management complexities that break current computing paradigms, which are based on static behaviors, interactions and compositions of components and/or services. As a result, applications, programming environments and information infrastructures are rapidly becoming fragile, unmanageable and insecure. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems to deal with complexity, dynamism, heterogeneity and uncertainty. Autonomic programming paradigm is inspired by the human autonomic nervous system that handles complexity, uncertainties and abnormality, and aims at realizing computing systems and applications capable of managing themselves with minimum human intervention. The overarching goal of the autonomic programming paradigm is to help building systems and applications capable of self-management. In this thesis we focus on the Self-optimizing, and Self-Configuring properties of the autonomic system. Firstly, we investigated the large-scale scientific computing applications which generally experience different execution phases at runtime and each phase has different computational, communication and storage requirements as well as different physical characteristics. Consequently, an optimal solution or numerical scheme for one execution phase might not be appropriate for the next phase of the application execution. In this dissertation, we present Physics Aware Optimization (PAO) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Optimization Manager to exploit the PAO paradigm. On the other hand we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring server and networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means it can be self-managed component) by using our autonomic programming paradigm. Our POA technique for medical application yielded about 3X improvement of performance with 98.3% simulation accuracy compared to traditional techniques for performance optimization. Also, our Self-configuration management for power and performance management in GPU cluster demonstrated 53.7% power savings for CUDA workload while maintaining the cluster performance within given acceptable thresholds.

TABLE OF CONTENTS

ABSTRACT2

CHAPTER 1: INTRODUCTION5

Problem Statement5

Research Objectives8

CHAPTER 2: LITERATURE REVIEW10

Autonomic Computing10

Autonomic Computing Architecture10

Properties of an Autonomic Computing System11

Autonomic Computing Properties in Our Research13

Levels of Autonomy in Computing Systems13

Artificial Intelligence14

Computers as Intelligent Artifacts16

Intelligence as Problem Solving18

Creative AI: Intelligence as Analogy Making20

Emmy: The Computer Composer21

Aaron: The Figurative Painter22

Situated AI: Intelligence as Embedded Interaction23

The Future of AI24

Problem Solving and Intelligence30

Advantages for Artificial Intelligence36

Disadvantages for ...