Jesús Carretero

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it’s targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.

Applying RCM in large scale systems: a case study with railway networks

Abstract In 2000, the European Union founded a project named ‘RAIL: Reliability centered maintenance approach for the infrastructure and logistics of railway operation’ aimed to study the application of Reliability centered maintenance (RCM) techniques to the railway infrastructure. In this paper, we present the results obtained into the RAIL project, including a RCM methodology adapted to large infrastructure networks and a RCM toolkit to perform the RCM analysis, including cost aspects and maintenance planning guidance. This paper addresses the problem of applying RCM to large scale railway infrastructure networks to achieve an efficient and effective maintenance concept. Railways use nowadays very traditional preventive maintenance (PM) techniques, relying mostly on ‘blind’ periodic inspection and the ‘know-how’ of maintenance staff. RCM was seen as a promising technique from the beginning of the RAIL project because of several factors. First, technical insights obtained were better than the existing, so that several maintenance processes could be revised and adjusted. Second, the interdisciplinary approach used to make the analysis was very enriching and very encouraging for maintenance staff consulted. Third, using the RCM structured approach allowed to achieve well-documented analysis and clear decision diagrams. Our methodology includes some new features to overcome the problems of RCM observed in other projects. As a whole, our methodology and Computerized Maintenance Management Systems have produced two short-term benefits: reduction of time and paperwork because databases and tools are accessible through Internet, and creation of a permanent, accurate, and better collection of information. It will also have some long-term benefits: better PM will increase equipment life and will help to reduce corrective maintenance costs; Production will increase as unscheduled downtime decreases; purchase costs of parts and materials will be reduced; more effective and up-to-date record of inventory/stores reports; and better knowledge of the systems to help the company to chose those systems with the best LCC. The results have been corroborated with the application of our methodology to signal equipment in several railway network sections, as shown in this paper. Because of the successful conclusion of the project, the Spanish railway company (RENFE) and the German railway company (DB A.G.), not only decided to adopt RCM to enhance PM, but they have started a large project to implement Total Preventive Maintenance relying on the implantation of the RCM methodology.

Branch replication scheme: A new model for data replication in large scale data grids

Data replication is a practical and effective method to achieve efficient and fault-tolerant data access in grids. Traditionally, data replication schemes maintain an entire replica in each site where a file is replicated, providing a read-only model. These solutions require huge storage resources to store the whole set of replicas and do not allow efficient data modification to avoid the consistency problem. In this paper we propose a new replication method, called the Branch Replication Scheme (BRS), that provides three main advantages over traditional approaches: optimizing storage usage, by creating subreplicas; increasing data access performance, by applying parallel I/O techniques; and providing the possibility to modify the replicas, by maintaining consistency among updates in an efficient way. An analytical model of the replication scheme, naming system, and replica updating scheme are formally described in the paper. Using this model, operations such as reading, writing, or updating a replica are analyzed. Simulation results demonstrate the feasibility of BRS, as they show that the new replication algorithm increases data access performance, compared with popular replication schemes such as hierarchical and server-directed replication, which are commonly used in current data grids.

Predictive Data Grouping and Placement for Cloud-Based Elastic Server Infrastructures

Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload.

E-mc2: A formal framework for energy modelling in cloud computing

Abstract Due to energy crisis of the last years, energy waste and sustainability have been brought both into public attention, and under industry and scientific scrutiny. Thus, obtaining high-performance at a reduced cost in cloud environments as reached a turning point where computing power is no longer the most important concern. However, the emphasis is shifting to manage energy efficiently, whereas providing techniques for measuring energy requirements in cloud systems becomes of capital importance. Currently there are different methods for measuring energy consumption in computer systems. The first consists in using power meter devices, which measure the aggregated power use of a machine. Another method involves directly instrumenting the motherboard with multimeters in order to obtain each power connector’s voltage and current, thus obtaining real-time power consumption. These techniques provide a very accurate results, but they are not suitable for large-scale environments. On the contrary, simulation techniques provide good scalability for performing experiments of energy consumption in cloud environments. In this paper we propose E-mc 2 , a formal framework integrated into the iCanCloud simulation platform for modelling the energy requirements in cloud computing systems.

The Design of the Expand Parallel File System

This article describes an implementation of MPI-IO using a new parallel file system, called Expand (Expandable Parallel File System), which is based on NFS servers. Expand combines multiple NFS servers to create a distributed partition where files are striped. Expand requires no changes to the NFS server and uses RPC operations to provide parallel access to the same file. Expand is also independent of the clients, because all operations are implemented using RPC and NFS protocols. Using this system, we can join heterogeneous servers (Linux, Solaris, Windows 2000, etc.) to provide a parallel and distributed partition. The article describes the design, implementation and evaluation of Expand with MPI-IO. This evaluation has been made in Linux clusters and compares Expand and PVFS.

View-Based Collective I/O for MPI-IO

This paper presents the design and implementation of a new file system independent collective I/O optimization based on file views: view-based collective I/O. View-based collective I/O has been implemented and evaluated inside ROMIO implementation of MPI-IO standard. The evaluation section shows that view-based I/O outperforms the original two-phase collective I/O from ROMIO in most of the cases for three well-known parallel I/O benchmarks. This is especially due to a smaller cost of scatter/gather operations, a reduction of the metadata overhead, and a smaller number of collective communication and synchronization primitives used in the implementation.

Affinity P2P: A self-organizing content-based locality-aware collaborative peer-to-peer network

The last years have brought a dramatic increase in the popularity of collaborative Web 2.0 sites. According to recent evaluations, this phenomenon accounts for a large share of Internet traffic and significantly augments the load on the end-servers of Web 2.0 sites. In this paper, we show how collaborative classifications extracted from Web 2.0-like sites can be leveraged in the design of a self-organizing peer-to-peer network in order to distribute data in a scalable manner while preserving a high-content locality. We propose Affinity P2P (AP2P), a novel cluster-based locality-aware self-organizing peer-to-peer network. AP2P self-organizes in order to improve content locality using a novel affinity-based metric for estimating the distance between clusters of nodes sharing similar content. Searches in AP2P are directed to the cluster of interests, where a logarithmic-time parallel flooding algorithm provides high recall, low latency, and low communication overhead. The order of clusters is periodically changed using a greedy cluster placement algorithm, which reorganizes clusters based on affinity in order to increase the locality of related content. The experimental and analytical results demonstrate that the locality-aware cluster-based organization of content offers substantial benefits, achieving an average latency improvement of 45%, and up to 12% increase in search recall.

An ontology-driven decision support system for high-performance and cost-optimized design of complex railway portal frames

Highlights? We model the design process of complex railway electrification structures. ? We provide ontology and rules to model railway engineers knowledge. ? Increasing automation will increase infrastructure quality and will reduce design and construction costs. ? Our tool reduces design time from days to minutes, getting optimized structures compliant with railway normative. Electrification structures design for railway systems is a crucial and complex process, since it compounds plenty of infrastructure elements, design decisions, and calculation conditions. In this paper, an ontology-driven decision support system for designing complex railway portal frames is presented and developed. A knowledge-rules database has been also developed relying on experts knowledge and complying with railway standards. Our system outperforms the current portal frames design methods by decreasing construction time and costs. As a result, an intelligent computer-aided design tool is provided, thus facilitating the task of seeking for the optimal portal frame, which is geometrically and structurally feasible, and cost-effective.

Design and Evaluation of Multiple-Level Data Staging for Blue Gene Systems

Parallel applications currently suffer from a significant imbalance between computational power and available I/O bandwidth. Additionally, the hierarchical organization of current Petascale systems contributes to an increase of the I/O subsystem latency. In these hierarchies, file access involves pipelining data through several networks with incremental latencies and higher probability of congestion. Future Exascale systems are likely to share this trait. This paper presents a scalable parallel I/O software system designed to transparently hide the latency of file system accesses to applications on these platforms. Our solution takes advantage of the hierarchy of networks involved in file accesses, to maximize the degree of overlap between computation, file I/O-related communication, and file system access. We describe and evaluate a two-level hierarchy for Blue Gene systems consisting of client-side and I/O node-side caching. Our file cache management modules coordinate the data staging between application and storage through the Blue Gene networks. The experimental results demonstrate that our architecture achieves significant performance improvements through a high degree of overlap between computation, communication, and file I/O.