Designing a consulting services architecture model

textDuring my years of experience in the technology industry, it has become obvious that standard processes and methodologies within the engineering discipline are at a mature state. The realization though is that software engineering specifically lags behind. Most software engineering methodologies that I have studied focus on the mission of software development. It is this realization and the need for structure that led me to review existing methodologies used within my company's software services organization. The definition of what a successful software services methodology entails is rather limited. This report will provide a history of existing software engineering methodologies that I have studied, describe an initial services method that was being developed within my organization, develop a new model that addresses previous shortcomings and identify additional components required to further define a strong software services-oriented delivery methodology.Electrical and Computer Engineerin

Activity-Centric Computing Systems

• Activity-Centric Computing (ACC) addresses deep-rooted information management problems in traditional application centric computing by providing a unifying computational model for human goal-oriented ‘activity,’ cutting across system boundaries. • We provide a historical review of the motivation for and development of ACC systems, and highlight the need for broadening up this research topic to also include low-level system research and development. • ACC concepts and technology relate to many facets of computing; they are relevant for researchers working on new computing models and operating systems, as well as for application designers seeking to incorporate these technologies in domain-specific applications

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how Azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms.

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies