Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Energy-efficient cloud computing application solutions and architectures

Environmental issues are receiving unprecedented attention from business and governments around the world. As concern for greenhouse, climate change and sustainability continue to grow; businesses are grappling with improving their environmental impacts while remaining profitable. Many businesses have discovered that Green IT initiatives and strategies can reform the organization, comply with laws and regulations, enhance the public appearance of the organization, save energy cost, and improving their environmental impacts. One of these Green IT initiatives is migrating or building the business applications in the cloud. Cloud computing is a highly scalable and cost-effective infrastructure for running enterprise and web applications. As a result, building enterprise systems on cloud computing platform is increasing significantly today. However, cloud computing is not inherently proposing energy efficiency solutions for these businesses. In this thesis, a concept has been developed to support organizations choosing suitable energy-efficient cloud architecture while moving their application to the cloud or building new cloud applications. Thus, the concept focuses on how to employ the cloud computing technology as an energy efficient solution from the application perspective. The main idea applied in the concept is identifying architectures for cloud applications depending on the inherent properties of cloud computing such as virtualization and the elasticity that can make them green potential, and identifying correlations between these architectures with already identified business process patterns used in green business process design. Alongside with these correlations, the application has been decomposed into basic technical and business attributes that can describe the application. The relations between these attributes and the cloud architectures have been defined. The relations between the different components the application attributes, application architectures, and the green patterns can lead to not only the energy-efficient cloud architecture for the business application, but also to the architectures that can achieve the organization technical and business requirements. Prototypically, a recommender system has been implemented that supports the identification of suitable energy-efficient cloud application architectures in addition to the cloud migration decision

Modeling and simulation of data-driven applications in SDN-aware environments

PhD ThesisThe rising popularity of Software-Defined Networking (SDN) is increasing as it promises to offer a window of opportunity and new features in terms of network performance, configuration, and management. As such, SDN is exploited by several emerging applications and environments, such as cloud computing, edge computing, IoT, and data- driven applications. Although SDN has demonstrated significant improvements in industry, still little research has explored the embracing of SDN in the area of cross-layer optimization in different SDN-aware environments. Each application and computing environment require different functionalities and Quality of Service (QoS) requirements. For example, a typical MapReduce application would require data transmission at three different times while the data transmission of stream-based applications would be unknown due to uncertainty about the number of required tasks and dependencies among stream tasks. As such, the deployment of SDN with different applications are not identical, which require different deployment strategies and algorithms to meet different QoS requirements (e.g., high bandwidth, deadline). Further, each application and environment has unique architectures, which impose different form of complexity in terms of computing, storage, and network. Due to such complexities, finding optimal solutions for SDN-aware applications and environments become very challenging. Therefore, this thesis presents multilateral research towards optimization, modeling, and simulation of cross-layer optimization of SDN-aware applications and environments. Several tools and algorithms have been proposed, implemented, and evaluated, considering various environments and applications[1–4]. The main contributions of this thesis are as follows: • Proposing and modeling a new holistic framework that simulates MapReduce ap- plications, big data management systems (BDMS), and SDN-aware networks in cloud-based environments. Theoretical and mathematical models of MapReduce in SDN-aware cloud datacenters are also proposedThe government of Saudi Arabia represented by Saudi Electronic University (SEU) and the Royal Embassy of Saudi Arabia Cultural Burea

Transformative Effects of IoT, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges

Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesised to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing

EDGE-CoT: next generation cloud computing and its impact on business

Purpose – The main objective of this paper is to analyze the potential impact of future cloud computing trends on business, from the perspective of specialists in the area. Design/ methodology/ approach - Qualitative approach that includes literature review and nine semi-structured interviews with proclaimed influencers and global thought leaders in cloud computing, highlighting Jeff Barr, Vice President of Amazon Web Services. Findings -5G networks will enable the emergence of the Edge-CoT architecture, that will consequently drive the increased application of Artificial Intelligence/ Machine Learning (AI/ML) and Robotics. The combination of Edge-CoT, Robotics and AI/ML triggers the development of Smart Cities and Industry 4.0. Simultaneously, Cloud alone will benefit of increased connectivity and will be the preferred business architecture comparing to EdgeCoT. New industries and businesses will result from the Edge-CoT, and the existing companies will benefit mainly from an improved customer experience. Major business challenges triggered by Edge-CoT include workforce re-skilling, promotion of the agile approach and a cultural shift towards risk-taking. Research limitations/implications - The research study was limited to the analysis of a selected set of cloud computing trends. Moreover, the data collection process was limited to 9 cloud experts, hindering a possible generalization. Originality/value – This study uses a qualitative approach to listen to market experts and cross with the theoretical findings to date, consequently bringing theory and practice closer together.Objetivo - O objetivo deste estudo consiste em analisar o potencial impacto das tendências futuras de cloud computing na gestão das empresas, a partir da visão de especialistas da área. Metodologia- Abordagem qualitativa que engloba revisão de literatura e nove entrevistas semiestruturadas com proclamados influencers e lideres globais em cloud computing, destacando-se Jeff Barr, o Vice-presidente da Amazon Web Services. Resultado - As redes 5G possibilitarão o surgimento da arquitetura Edge-CoT, que consequentemente impulsionará o aumento da aplicação de Inteligência Artificial (AI) e robótica. A combinação de Edge-CoT, Robótica e AI desencadeia o desenvolvimento de Smart Cities e Industry 4.0. Simultaneamente, a Cloud sozinha beneficiará do aumento da conectividade e será a arquitetura preferida comparativamente a Edge-CoT. Novos setores e negócios resultarão do Edge-CoT, e as empresas existentes beneficiarão principalmente de uma melhor experiência do cliente. Os principais desafios organizacionais desencadeados pelo Edge-CoT incluem a requalificação da força de trabalho, a adoção da abordagem agile e uma mudança cultural que estimule experimentos tecnológicos. Restrição da pesquisa - O processo de recolha de dados foi limitado a 9 especialistas em cloud computing, dificultando assim uma possível generalização. Originalidade/ Valor - Este estudo utiliza uma abordagem qualitativa para ouvir os especialistas do mercado e cruzar com os resultados teóricos até o momento, aproximando assim a teoria da prática

Thermal Energy Storage for Datacenters with Phase Change Materials

Datacenters, vast warehouses containing millions of servers that run the internet and the cloud, have experienced double digit growth for almost two decades. Datacenters cost hundreds of millions of dollars, with the largest now exceeding over a billion dollars each, and consume enormous amounts of power–over 2% of all electricity in the US and projected to increase up to 10% by 2030. The impact of such high compute density, with thousands of individual compute nodes packed together in a small space, is heat: every watt of power used by servers must be removed form the datacenter. This requires active cooling: air cooling is by far the most common with an air conditioner or other form of heat exchanger cooling air in the datacenter room then transporting heat outside the facility to heat exchanger or similar fixture. Such a system is simple, common, and functional, but inherently inefficient due to the nature of datacenter workloads. Datacenters primarily server user facing workloads, that is: the user requests a search or sends and email and their query prompts load in the datacenter. The query is handled locally, on a relative geographic scale, to provide a low response time and positive user experience. This necessitates globally distributed datacenter capacity, but also creates a diurnal load pattern whereby datacenters are most heavily loaded during the peak hours when users in their region of service are awake and active online versus the off hours when users are offline or asleep and query requests are low. Because datacenter infrastructure must be provisioned for peak load, servers, power distribution, and cooling infrastructure is significantly underutilized most of the time. This dissertation investigates the cooling needs of datacenters, and proposes to decouple the work and cooling needs. Specifically, we hypothesize that by storing thermal energy we can reshape the thermal profile of a datacenter to better balance cooling load throughout the day. We call this technique Thermal Time Shifting (TTS). First, we discuss how phase change materials (PCMs) enable TTS and evaluate the potential use scenarios of placing a small amount of PCM inside of servers for thermal energy storage. Next we dive deeper into the potential of thermal energy storage and propose Virtual Melting Temperatures (VMT), a technique that uses active job placement to control the melting and cooling of PCM to enable a much greater degree of control over the behavior of the thermal profile. Finally we propose and evaluate Thermal Gradient Transfer (TGT), a technique that uses direct water cooling to move heat straight from CPUs and GPUs to the wax for wider applicability and greater peak cooling load reduction.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/147726/1/skachm_1.pdfDescription of skachm_1.pdf : Restricted to UM users only