Last week the 12th ACM International Conference on Computing Frontiers (CF’15) took place in Ischia (Italy). There our paper entitled “SARP: producing approximate results with small correctness losses for cloud interactive services” was presented. This work is a result of the collaboration with the Institute of Computing Technology from the Chinese Academy of Sciences, which started during my latest research stay there.
Despite the importance of providing fluid responsiveness to user requests for interactive services, such request processing is very resource expensive when dealing with large-scale input data. These often exceed the application owners’ budget when services are deployed on a cloud, in which resources are charged in monetary terms. Providing approximate processing results is a feasible solution for such problem that trades off request correctness (quantified by output quality) for response time reduction. However, existing techniques in this area either use partial input data or skip expensive computations to produce approximate results, thus resulting in large losses in output quality on a tight resource budget.
In this paper, we propose SARP, a Synopsis-based Approximate Request Processing framework to produce approximate results with small correctness losses even using small amount of resources. To achieve this, SARP conducts full computations over the statistical aggregation of the entire input data using two key ideas:
- Offline synopsis management that generates and maintains a set of synopses that represent the statistical aggregation of original input data at different approximation levels.
- Online synopsis selection that considers both the current resource allocation and the workload status so as to select the synopsis with the maximal length that can be processed within the required response time. We demonstrate the effectiveness of our approach by testing the recommendation services in E-commerce sites using a large, real-world dataset.
Using prediction accuracy as the output quality, the results demonstrate:
- SARP achieves significant response time reduction with very small quality losses compared to the exact processing results.
- Using the same processing time, SARP demonstrates a considerable reduction in quality loss compared to existing approximation techniques.
The International Journal of Cloud Applications and Computing has just published our paper entitled “Cost-Effective Resource Configurations for Multi-Tenant Database Systems in Public Clouds”. This work is the result of a collaboration with Prof. Patrick Martin‘s research group (Queen’s University, Canada).
Cloud computing is a promising paradigm for deploying applications due to its large resource offerings on a pay-as-you-go basis. This paper examines the problem of determining the most cost-effective provisioning of a multi-tenant database system as a service over public clouds. The authors formulate the problem of resource provisioning, and then define a framework to solve it. Their framework uses heuristic based algorithms to select cost-effective configurations. The algorithms can optionally balance resource costs against penalties incurred from the violation of Service Level Agreements (SLAs) or opt for non SLA violating configurations. The specific resource demands on the virtual machines for a workload and SLAs are accounted for by the performance and cost models, which are used to predict performance and expected cost respectively. The work validates our approach experimentally using workloads based on standard TPC database benchmarks in the Amazon EC2 cloud.
The IEEE Xplore Digital Library has made available another of our latest conference papers. This time was at the IEEE International Conference on Cluster Computing 2014, which took place at Madrid past September.
The work was presented in the form of a poster entitled “Performance evaluation of a signal extraction algorithm for the Cherenkov Telescope Array’s Real Time Analysis pipeline” and the paper can be accessed here.
In this paper, several versions of a signal extraction algorithm, pertaining to the entry stage of the Cherenkov Telescope Array‘s Real Time Analysis pipeline, were implemented and optimized using SSE2, POSIX threads and CUDA. Results of this proof of concept let us gain an insight into the suitability of each platform, and the performance each one can deliver, to carry out this particular task.
This work constitutes a first step in the “cloudification” of this application and represents the first publication of my PhD student Juan José Rodríguez-Vázquez in this context.
Springer has published a volume of its Lecture Notes in Computer Science series with our paper entitled “A Multi-Capacity Queuing Mechanism in Multi-Dimensional Resource Scheduling”. This contribution was presented at the International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, held in conjunction with the ACM Symposium on Principles of Distributed Computing, that took place in Paris (France) past July 15th.
The volume can be accessed here and the paper is the result of an ongoing collaboration with the research group led by Prof. Lucio Grandinetti (University of Calabria, Italy).
With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling algorithms based on a single-resource optimization like processor fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of all resource dimensions. Thus, the scheduling algorithms have to fully use all resources. In this paper, we propose a queuing mechanism based on a multi-resource scheduling technique. For that, we model multi-resource scheduling as a multi-capacity bin-packing scheduling algorithm at the queue level to reorder the queue in order to improve the packing and as a result improve scheduling metrics. The experimental results demonstrate performance improvements in terms of waittime and slowdown metrics.
Last week the First HPCLATAM – CLCAR Joint Conference took place in Valparaiso, Chile. There, a joint work with Prof. Carlos García Garino‘s research group (Universidad Nacional de Cuyo, Argentina) was presented. This work, entitled “A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications” has been published by Springer through its Communications in Computer and Information Science series.
Frosts are one of the main causes of economic losses in the Province of Mendoza, Argentina. Although it is a phenomenon that happens every year, frosts can be predicted using Agricultural Monitoring Systems (AMS). AMS provide information to start and stop frosts defense systems and thus reduce economic losses. In recent years, the emergence of infrastructures called Sensor Clouds improved AMS in several aspects such as scalability, reliability, fault tolerance, etc. Sensor Clouds use Wireless Sensor Networks (WSN) to collect data in the field and Cloud Computing to store and process these data. Currently, Cloud providers like Amazon offer different instances to store and process data in a profitable way. Moreover, due to the variety of offered instances arises the need for tools to determine which is the most appropriate instance type, in terms of execution time and economic costs, for running agro-meteorological applications. In this paper we present a model targeted to estimate the execution time and economic cost of Amazon EC2 instances for frosts prediction applications.
In the past month I had the pleasure and the honor to be hosted again by the Chinese Academy of Sciences, Beijing This was 3 years after the previous invitation.
During this period I gave talks on cloud computing at the following institutions:
The talk introduced the basics of cloud computing and displayed real use cases of applications pertaining to emergent areas such as Bioinformatics and Space Exploration in which I have been involved in the past years.
Also, there have been some meetings pursuing collaboration opportunities. As a result, some initial joint work was started by our research group, ICMSEC and ICT.
Summarizing, this period has been very productive. The new opportunities that have arisen are a good example on how cloud computing is a hot technology.
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.