http://www.isihome.ir/freearticle/ISIHome.ir-26023.pdf

A framework for ranking of cloud computing services

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined.By analyzing the spot price histories of Amazon’s EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. Our findings suggest that usually prices are not market-driven, as sometimes previously assumed. Rather, they are likely to be generated most of the time at random from within a tight price range via a dynamic hidden reserve price mechanism. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

/pdf/deconstructing-amazon-ec2-spot-instance-pricing-4sfykd81o9.pdf

Deconstructing Amazon EC2 Spot Instance Pricing

Google Colaboratory (also known as Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared with a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory’s accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.

Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).

SMICloud: A Framework for Comparing and Ranking Cloud Services

In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.

FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

/pdf/performance-analysis-of-cloud-computing-services-for-many-3e3awq09a7.pdf

M N Yigitbasi

Papers

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing