The world of computing is in rapid transition, now dominated by a world of smartphones and cloud services, with profound implications for the future of advanced scientific computing. Simply put, high-performance computing (HPC) is at an important inflection point. For the last 60 years, the world's fastest supercomputers were almost exclusively produced in the United States on behalf of scientific research in the national laboratories. Change is now in the wind. While costs now stretch the limits of U.S. government funding for advanced computing, Japan and China are now leaders in the bespoke HPC systems funded by government mandates. Meanwhile, the global semiconductor shortage and political battles surrounding fabrication facilities affect everyone. However, another, perhaps even deeper, fundamental change has occurred. The major cloud vendors have invested in global networks of massive scale systems that dwarf today's HPC systems. Driven by the computing demands of AI, these cloud systems are increasingly built using custom semiconductors, reducing the financial leverage of traditional computing vendors. These cloud systems are now breaking barriers in game playing and computer vision, reshaping how we think about the nature of scientific computation. Building the next generation of leading edge HPC systems will require rethinking many fundamentals and historical approaches by embracing end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping, as was common thirty years ago; and collaborative partnerships with the dominant computing ecosystem companies, smartphone, and cloud computing vendors.
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields - spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA's Perseverance rover on February 18,…
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers…
The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained. Moreover, research opportunities and promising f…
The edge computing paradigm has emerged to handle cloud computing issues such as scalability, security and low response time among others. This new computing trend heavily relies on ubiquitous embedded systems on the edge. Performance and energy consumption are two main factors that should be considered during the design of such systems. Focusing on performance and energy consumption, this paper studies the opportunities and challenges that a heterogeneous embedded system consisting of embedded FPGAs and GPUs (as accelerators) can provide for applications. We study three design, modeling and s…
Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main challenges: privacy and security risks, information silo, and pricing mechanisms and incentives. The recent advances of blockchain bring opportunities to address the challenges of services computing due to its build-in encryption as well as digital signature schemes, decentralization feature, and intrinsic incentive mechanisms. In this paper, we present…
A new class of Second generation high-performance computing applications with heterogeneous, dynamic and data-intensive properties have an extended set of requirements, which cover application deployment, resource allocation, -control, and I/O scheduling. These requirements are not met by the current production HPC platform models and policies. This results in a loss of opportunity, productivity and innovation for new computational methods and tools. It also decreases effective system utilization for platform providers due to unsupervised workarounds and rogue resource management strategies im…
In recent years, there has been a surge in the adoption of serverless computing due to the ease of deployment, attractive pay-per-use pricing, and transparent horizontal auto-scaling. At the same time, infrastructure advancements such as the emergence of 5G networks and the explosion of devices connected to Internet known as Internet of Things (IoT), as well as new application requirements that constrain where computation and data can happen, will expand the reach of Cloud computing beyond traditional data centers into Hybrid Cloud. Digital transformation due to the pandemic, which accelerated…
Revenue generation is the main concern of any business, particularly in the cloud, where there is no direct interaction between the provider and the consumer. Cloud computing is an emerging core for today's businesses, however, Its complications (e.g, installation, and migration) with traditional markets are the main challenges. It earns more but needs exemplary performance and marketing skills. In recent years, cloud computing has become a successful paradigm for providing desktop services. It is expected that more than \$ 331 billion will be invested by 2023, likewise, 51 billion devices are…
Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and e…
Cyber-physical systems increasingly rely on distributed computing platforms where sensing, computing, actuation, and communication resources are shared by a multitude of applications. Such `cyber-physical cloud computing platforms' present novel challenges because the system is built from mobile embedded devices, is inherently distributed, and typically suffers from highly fluctuating connectivity among the modules. Architecting software for these systems raises many challenges not present in traditional cloud computing. Effective management of constrained resources and application isolation w…
The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to criti…
The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the managemen…
This work examines the challenges and opportunities of Machine Learning (ML) for Monitoring and Operational Data Analytics (MODA) in the context of Quantitative Codesign of Supercomputers (QCS). MODA is employed to gain insights into the behavior of current High Performance Computing (HPC) systems to improve system efficiency, performance, and reliability (e.g. through optimizing cooling infrastructure, job scheduling, and application parameter tuning). In this work, we take the position that QCS in general, and MODA in particular, require close exchange with the ML community to realize the fu…
Mobile edge cloud is emerging as a promising technology to the internet of things and cyber-physical system applications such as smart home and intelligent video surveillance. In a smart home, various sensors are deployed to monitor the home environment and physiological health of individuals. The data collected by sensors are sent to an application, where numerous algorithms for emotion and sentiment detection, activity recognition and situation management are applied to provide healthcare- and emergency-related services and to manage resources at the home. The executions of these algorithms …
The application of high-performance computing (HPC) processes, tools, and technologies to Controlled Unclassified Information (CUI) creates both opportunities and challenges. Building on our experiences developing, deploying, and managing the Research Environment for Encumbered Data (REED) hosted by AWS GovCloud, Research Computing at Purdue University has recently deployed Weber, our locally-sited HPC solution for the storage and analysis of CUI data. Weber presents our customer base with advances in data access, portability, and usability at a low, stable cost while reducing administrative o…
As cars are ubiquitous they could play a major role in a next generation communication and computation framework. In the last years, the development of vehicle-to-vehicle communication and vehicle-to-infrastructure communication took huge steps forward and therefore gives us the tools to build "mobile computing service" on cars equipped with computation capabilities. Recently, several groups of researchers independently proposed the design of "vehicular clouds" that materializes the concept. In this paper, we introduce a new paradigm of the vehicular clouds, followed by a case study of data st…
This work provides the basis to understand and select Cloud Computing models applied for the development of IoT solutions using Low-Power Wide Area Network (LPWAN). Cloud Computing paradigm has transformed how the industry implement solution, through the commoditization of shared IT infrastructures. The advent of massive Internet of Things (IoT) and related workloads brings new challenges to this scenario demanding malleable configurations where the resources are distributed closer to data sources. We introduce an analysis of existing solution architectures, along with an illustrative case fro…
National Institute of Technology Silchar（锡尔查尔国家理工学院）；Ericsson（爱立信）
Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support fo…
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
FFT, FMM, and multigrid methods are widely used fast and highly scalable solvers for elliptic PDEs. However, emerging large-scale computing systems are introducing challenges in comparison to current petascale computers. Recent efforts (Dongarra et al. 2011) have identified several constraints in the design of exascale software that includes massive concurrency, resilience management, exploiting the high performance of heterogeneous systems, energy efficiency, and utilizing the deeper and more complex memory hierarchy expected at exascale. In this paper, we perform a model-based comparison of …
Hardware accelerators are available on the Cloud for enhanced analytics. Next generation Clouds aim to bring enhanced analytics using accelerators closer to user devices at the edge of the network for improving Quality-of-Service by minimizing end-to-end latencies and response times. The collective computing model that utilizes resources at the Cloud-Edge continuum in a multi-tier hierarchy comprising the Cloud, the Edge and user devices is referred to as Fog computing. This article identifies challenges and opportunities in making accelerators accessible at the Edge. A holistic view of the Fo…
With the rapid development of in-depth learning, neural network and deep learning algorithms have been widely used in various fields, e.g., image, video and voice processing. However, the neural network model is getting larger and larger, which is expressed in the calculation of model parameters. Although a wealth of existing efforts on GPU platforms currently used by researchers for improving computing performance, dedicated hardware solutions are essential and emerging to provide advantages over pure software solutions. In this paper, we systematically investigate the neural network accelera…
Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can benefit from in-network computing: scientific workloads in high-performance computing. By analyzing canonical examples of HPC applications, we observe unique opportunities and challenges for exploiting in-network computing to accelerate scientific workloads. In particular, we find that the dynamic and demanding nature of scientific workloads is the major obstacle to the adoption of in-network approaches which are mo…
George Mason University（乔治梅森大学）；Microsoft（微软）；University of Pittsburgh（匹兹堡大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Deep Learning (DL) models have achieved superior performance in many application domains, including vision, language, medical, commercial ads, entertainment, etc. With the fast development, both DL applications and the underlying serving hardware have demonstrated strong scaling trends, i.e., Model Scaling and Compute Scaling, for example, the recent pre-trained model with hundreds of billions of parameters with ~TB level memory consumption, as well as the newest GPU accelerators providing hundreds of TFLOPS. With both scaling trends, new problems and challenges emerge in DL inference serving …
