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Register a new user10 Years Later: Cloud Computing is Closing the Performance Gap
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Added by
Giulia Guidi
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing high-performance computing. We present a multi-level approach to investigate the performance gap between HPC and cloud computing, isolating different variables that contribute to this gap. Our experiments are divided into (i) hardware and system microbenchmarks and (ii) user application proxies. The results show that todays high-end cloud computing can deliver HPC-competitive performance not only for computationally intensive applications but also for memory- and communication-intensive applications - at least at modest scales - thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.
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Forty years ago, Richard Feynman proposed harnessing quantum physics to build a more powerful kind of computer. Realizing Feynmans vision is one of the grand challenges facing 21st century science and technology. In this article, well recall Feynmans contribution that launched the quest for a quantum computer, and assess where the field stands 40 years later.
This paper has been withdrawn by the author
Cloud storage plays an important role in social computing. This paper aims to develop a cloud storage management system for mobile devices to support an extended set of file operations. Because of the limit of storage, bandwidth, power consumption, and other resource restrictions, most existing cloud storage apps for smartphones do not keep local copies of files. This efficient design, however, limits the application capacities. In this paper, we attempt to extend the available file operations for cloud storage service to better serve smartphone users. We develop an efficient and secure file management system, Skyfiles, to support more advanced file operations. The basic idea of our design is to utilize cloud instances to assist file operations. Particularly, Skyfiles supports downloading, compressing, encrypting, and converting operations, as well as file transfer between two smartphone users cloud storage spaces. In addition, we propose a protocol for users to share their idle instances. All file operations supported by Skyfiles can be efficiently and securely accomplished with either a self-created instance or shared instance.
Big data applications and analytics are employed in many sectors for a variety of goals: improving customers satisfaction, predicting market behavior or improving processes in public health. These applications consist of complex software stacks that are often run on cloud systems. Predicting execution times is important for estimating the cost of cloud services and for effectively managing the underlying resources at runtime. Machine Learning (ML), providing black box solutions to model the relationship between application performance and system configuration without requiring in-detail knowledge of the system, has become a popular way of predicting the performance of big data applications. We investigate the cost-benefits of using supervised ML models for predicting the performance of applications on Spark, one of todays most widely used frameworks for big data analysis. We compare our approach with textit{Ernest} (an ML-based technique proposed in the literature by the Spark inventors) on a range of scenarios, application workloads, and cloud system configurations. Our experiments show that Ernest can accurately estimate the performance of very regular applications, but it fails when applications exhibit more irregular patterns and/or when extrapolating on bigger data set sizes. Results show that our models match or exceed Ernests performance, sometimes enabling us to reduce the prediction error from 126-187% to only 5-19%.
Businesses have made increasing adoption and incorporation of cloud technology into internal processes in the last decade. The cloud-based deployment provides on-demand availability without active management. More recently, the concept of cloud-native application has been proposed and represents an invaluable step toward helping organizations develop software faster and update it more frequently to achieve dramatic business outcomes. Cloud-native is an approach to build and run applications that exploit the cloud computing delivery models advantages. It is more about how applications are created and deployed than where. The container-based virtualization technology, such as Docker and Kubernetes, serves as the foundation for cloud-native applications. This paper investigates the performance of two popular computational-intensive applications, big data, and deep learning, in a cloud-native environment. We analyze the system overhead and resource usage for these applications. Through extensive experiments, we show that the completion time reduces by up to 79.4% by changing the default setting and increases by up to 96.7% due to different resource management schemes on two platforms. Additionally, the resource release is delayed by up to 116.7% across different systems. Our work can guide developers, administrators, and researchers to better design and deploy their applications by selecting and configuring a hosting platform.
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