The International Journal of Computing has made available our paper entitled “Spot Price prediction for Cloud Computing using Neural Networks”. This work is the result of a collaboration with the research groups led by Prof. Lucio Grandinetti (University of Calabria, Italy) and Associate Prof. Volodymyr O. Turchenko (Ternopil National Economic Universit, Ukraine).
Advances in service-oriented architectures, virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on such systems results in commodity bidding for computing time. Amazon institutionalizes this bidding for its Elastic Cloud Computing (EC2) environment. Similar bidding methods exist for other cloud-computing vendors as well as multi–cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present the predictive models for future short-term and middle-term spot price prediction based on neural networks giving users a high confidence on future prices aiding bidding on commodity computing.
At the end of June the Handbook of Research on Architectural Trends in Service-Driven Computing has been released by IGI Global. This publication, divided in 2 volumes, explores, delineates, and discusses recent advances in architectural methodologies and development techniques in service-driven computing. The handbook is an inclusive reference source for organizations, researchers, students, enterprise and integration architects, practitioners, software developers, and software engineering professionals engaged in the research, development, and integration of the next generation of computing.
We participated in the elaboration of this publication with the 28th Chapter, entitled “Admission Control in the Cloud: Algorithms for SLA-Based Service Model”.
Cloud Computing is a paradigm that allows the flexible and on-demand provisioning of computing resources. For this reason, many institutions have moved their systems to the Cloud, and in particular, to public infrastructures. Unfortunately, an increase in the demand for Cloud results in resource shortages affecting both providers and consumers. With this factor in mind, Cloud service providers need Admission Control algorithms in order to make a good business decision on the types of requests to be fulfilled. At the same time, Cloud providers have a desire to maximize the net income derived from provisioning the accepted service requests and minimize the impact of un-provisioned resources. This chapter introduces and compares Admission Control algorithms and proposes a service model that allows the definition of Service Level Agreements (SLAs).
- Title: Handbook of Research on Architectural Trends in Service-Driven Computing
- Editors: Raja Ramanathan and Kirtana Raja
- Pub. date: June 2014
- Pages: 759
- Volume: 23 of Advances in Parallel Computing
- ISBN13: 9781466661783
- J.L. Vázquez-Poletti
Scalable Computing: Practice and Experience has just published our recent paper entitled “Regulated Condition-Event Matrices for Cloud Environments”. This work is the result of a collaboration with Prof. Patrick Martin (Queen’s University, Canada) and introduces the PhD Thesis core of my student Richard M. Wallace. The paper can be accessed here.
Distributed event-based systems (DEBS) are networks of computing devices. These systems have been successfully implemented by commercial vendors. Cloud applications depend on message passing and inter-connectivity methods exchanging data and performing inter-process communication. Both DEBS and Clouds need time-coordinated methods of control not dependent on a single time domain. While DEBS have specific implementation languages for complex events, Cloud systems do not. Clouds and DEBS have not yet presented an explicit separation of temporally based event processing from computations. Using a regulated, isomorphic, temporal architecture (RITA), a specific language and separation of temporal event processing from processing computation is achieved. RITA provides a functional programming style for developers using familiar language constructs for integration with existing processing code without forcing the developer to work in multiple coding paradigms requiring extensive “glue code” allowing coding paradigms to work together. This paper introduces RITA as a guarded condition-event system that has explicit separation of event processing and computation with constructs allowing integration of time-aware events for multiple time domains found in Cloud or existing distributed computing systems.
From July 7th to 11th Cetraro (Italy) will host again its famous International Advanced Workshop on High Performance Computing. Its main aim is to present and debate advanced topics, open questions, future developments, and challenging applications related to advanced high-performance distributed computing and data systems, encompassing implementations ranging from traditional clusters to warehouse-scale data centers, and with architectures including hybrid, multicore, distributed, and cloud models.
And this year’s motto is “from Clouds and Big Data to Exascale and Beyond”, which is itself a statement of intentions.
For the second time, I’m very honored to attend as invited speaker. This year I’ll give a talk entitled “Clouds for Meteorology, two cases study”.
Meteorology is among the most promising areas that benefit from cloud computing, due to its intersection with society’s critical aspects. Executing meteorological applications involves HPC and HTC challenges, but also economic ones. My talk will introduce two cases with different backgrounds and motivations, but always sharing a similar cloud methodology: the first one is about weather forecasting in the context of planet Mars exploration; and the second one deals with data processing from weather sensor networks, in the context of an agriculture improving plan at Argentina.
I’ll of course take the advantage of this travel to meet again with many colleagues from the previous edition of HPC, in order to continue and expand current collaborations, which have been very productive in past 2 years.
Usually terms such as high performance and high availability are addressed by big corporations and institutions; however, something has changed over the past years as a real revolution is emerging from university classrooms. This week HPCwire has published an article describing some of the promising work being carried out by my students.
This year I have been honored to advise projects that respond to three critical areas that make their way to the media headlines nowadays: communications security, emergency medical services and P2P digital currencies.
Access the article here. And if you are curious about the rest of the projects, access the complete list here.
Last week, the School of Computing at Queen’s University (Canada) published our latest work in the form of a technical report. This technical report, result of the collaboration with Prof. Patrick Martin‘s research group, is entitled “Estimating Resource Costs of Executing Data-Intensive Workloads in Public Clouds” and can be accessed here.
The promise of “infinite” resources given by the cloud computing paradigm has led to recent interest in exploiting clouds for large-scale data-intensive computing. In this technical report, we present an analytical model to estimate the resource costs for executing data-intensive workloads in a public cloud. The cost model quantifies the cost-effectiveness of a resource configuration for a given workload with consumer performance requirements expressed as Service Level Agreements (SLAs), and is a key component of a larger framework for resource provisioning in clouds. We instantiate the cost model for the Amazon cloud, and experimentally evaluate the impact of key factors on the accuracy of the model.
The IEEE Xplore Digital Library has made available our paper entitled “Applications of neural-based spot market prediction for cloud computing”, which was presented at the IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS 2013) last September. It can be accessed here.
Advances in service-oriented architectures (SOA), virtualization, high-speed networks, and cloud computing have resulted in attractive pay-as-you-go services. Job scheduling on these systems results in commodity bidding for computing time. This bidding is institutionalized by Amazon for its Elastic Cloud Computing (EC2) environment and bidding methods exist for other cloud computing vendors as well as multi-cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we discuss vendors who provide spot pricing and bidding and present a predictive model for future spot prices based on neural networking giving users a high confidence on future prices aiding bidding on commodity computing.
This is another work resulting from a collaboration with Prof. Lucio Grandinetti‘s research group at University of Calabria, Italy.
Last week the 4CaaSt project faced its final year review at the European Commission.
This project aims to create an advanced PaaS Cloud platform which supports the optimized and elastic hosting of Internet-scale multi-tier applications. 4CaaSt embeds all the necessary features, easing programming of rich applications and enabling the creation of a true business ecosystem where applications coming from different providers can be tailored to different users, mashed up and traded together.
The result? We passed our final review!
It has been 3 years of hard work and we have held general assemblies almost all over the European territory.
We have fostered innumerable and interesting collaborations.
And of course, we also established many new friendships!
Bye bye 4CaaSt project… and bye bye to all of you who I’m honored to call “my colleagues” since 3 years ago, for now.
“Painful though parting be, I bow to you as I see you off to distant clouds” (Emperor Saga)
Today a new book on Cloud Computing and Big Data has been published by IOS Press. I had the pleasure and honor to team up with Dr. Charlie Catlett, Dr. Wolfgang Gentzsch, Prof. Lucio Grandinetti and Prof. Gerhard R. Joubert for its edition.
Cloud computing offers many advantages to researchers and engineers who need access to high performance computing facilities for solving particular compute-intensive and/or large-scale problems, but whose overall high performance computing (HPC) needs do not justify the acquisition and operation of dedicated HPC facilities. There are, however, a number of fundamental problems which must be addressed, such as the limitations imposed by accessibility, security and communication speed, before these advantages can be exploited to the full.
This book presents 14 contributions selected from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in June 2012. The papers are arranged in three chapters. Chapter 1 includes five papers on cloud infrastructures, while Chapter 2 discusses cloud applications.
The third chapter in the book deals with big data, which is nothing new – large scientific organizations have been collecting large amounts of data for decades – but what is new is that the focus has now broadened to include sectors such as business analytics, financial analyses, Internet service providers, oil and gas, medicine, automotive and a host of others.
This book will be of interest to all those whose work involves them with aspects of cloud computing and big data applications.
- Title: Cloud Computing and Big Data
- Editors: Catlett, C. , Gentzsch, W., Grandinetti, L., Joubert, G.R., Vazquez-Poletti, J.L.
- Pub. date: October 2013
- Pages: 264
- Volume: 23 of Advances in Parallel Computing
- ISBN: 978-1-61499-321-6
- J.L. Vázquez-Poletti
This month the Journal of Software: Practice and Experience has published online our paper entitled “Autonomic resource contention-aware scheduling”. It can be accessed here.
The complexity of computing systems introduces a few issues and challenges such as poor performance and high energy consumption. In this paper, we first define and model resource contention metric for high performance computing workloads as a performance metric in scheduling algorithms and systems at the highest level of resource management stack to address the main issues in computing systems. Second, we propose a novel autonomic resource contention-aware scheduling approach architected on various layers of the resource management stack. We establish the relationship between distributed resource management layers in order to optimize resource contention metric. The simulation results confirm the novelty of our approach.
This work is the result of a collaboration with Prof. Lucio Grandinetti‘s research group from University of Calabria, Italy.