Hyperscale

Abstract: In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented as a means to introduce further cost-efficiencies, reduce configuration and management overheads, and rapidly increase an application's ability to speed up, scale up and scale down in the cloud. The potential of this new computation model is reflected in the introduction of serverless computing platforms by the main hyperscale cloud service providers. This paper provides an overview and multi-level feature analysis of seven enterprise serverless computing platforms. It reviews extant research on these platforms and identifies the emergence of AWS Lambda as a de facto base platform for research on enterprise serverless cloud computing. The paper concludes with a summary of avenues for further research.

Abstract: FPGAs (Field Programmable Gate Arrays) are making their way into data centers (DCs) and are used to offload and accelerate specific services, but they are not yet available to cloud users. This puts the cloud deployment of compute-intensive workloads at a disadvantage compared with on-site infrastructure installations, where the performance and energy efficiency of FPGAs are increasingly being exploited for application-specific accelerators and heterogeneous computing. The cloud is housed in DCs, and DCs are based on ever shrinking servers. Today, we observe the emergence of hyper scale data centers, which are based on densely packaged servers. The shrinking form factor brings the potential to deploy FPGAs on a large scale in such DCs. These FPGAs must be deployed as independent DC resources, and they must be accessible to the cloud users. Therefore, we propose to change the traditional paradigm of the CPU-FPGA interface by decoupling the FPGA from the CPU and connecting the FPGA as a standalone resource to the DC network. This allows cloud vendors to offer an FPGA to users in a similar way as a standard server. As existing infrastructure-as-a-service (IaaS) mechanisms are not suitable, we propose a new Open Stack (open source cloud computing software) service to integrate FPGAs in the cloud. This proposal is complemented by a framework that enables cloud users to combine multiple FPGAs into a programmable fabric. The proposed architecture and framework address the scalability problem that makes it difficult to provision large numbers of FPGAs. Together, they offer a novel solution for processing large and heterogeneous data sets in the cloud.

Abstract: Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, as well as collocation of compute and data for high bandwidth and low-latency access. It brings together key components and features of Microsoft?s Cosmos file system-long used by internal customers at Microsoft and HDFS, and is a unified file storage solution for analytics on Azure. Internal and external workloads run on this unified platform. Distinguishing aspects of ADLS include its design for handling multiple storage tiers, exabyte scale, and comprehensive security and data sharing features. We present an overview of ADLS architecture, design points, and performance.

Abstract: Fifty years of fluvial studies have posited a variety of conceptual frameworks for characterizing river forms and processes throughout entire basins, including hydraulic geometry, the river continuum concept, self-organized criticality, and sediment links. This article uses basin-extent, high resolution observations of fluvial forms in the Nueces River basin, Texas, and Yellowstone National Park to evaluate the ability of these frameworks to characterize system behavior across a multitude of scales. The Nueces data were collected with remote sensing methods and the Yellowstone data were collected through extensive field surveys. The data resolution, spatial extent, and quality of these data sets allow direct comparison between the two areas. The ‘hyperscale’ comparison supports using of each these frameworks at specific scales, but also indicates an irreducible amount of variation in both datasets across many different scales that is not captured by the conceptual frameworks. Moreover, the scales and locations where one framework, such as hydraulic geometry, works well are often not the same scales and locations where another framework, such as the river continuum concept, works well. Because the conceptual frameworks appear to operate at scales and locations distinct from one another, the measurement approaches necessary to observe them must also be at different scales and locations. For example, ‘seeing’ self-organized criticality in a river system is difficult without an extensive survey through space, whereas the recognition of sediment links requires quite intense sampling in specific river regions. We suggest that these separations between measurement scales represent an incommensurability issue in river studies, making it very difficult to both communicate among and test between two or more competing theories. Making simultaneous hyperscale observations of the river is one approach to minimizing the theory-ladeness of observation, as deviations from different predictions can be plotted at every scale. Copyright © 2010 John Wiley & Sons, Ltd.