The CMS monitoring infrastructure and applications

The globally distributed computing infrastructure required to cope with the multi-petabytes datasets produced by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN comprises several subsystems, such as workload management, data management, data transfers, and submission of users' and centrally managed production requests. The performance and status of all subsystems must be constantly monitored to guarantee the efficient operation of the whole infrastructure. Moreover, key metrics need to be tracked to evaluate and study the system performance over time. The CMS monitoring architecture allows both real-time and historical monitoring of a variety of data sources and is based on scalable and open source solutions tailored to satisfy the experiment's monitoring needs. We present the monitoring data flow and software architecture for the CMS distributed computing applications. We discuss the challenges, components, current achievements, and future developments of the CMS monitoring infrastructure.Comment: 14 pages, 5 figures, submitted to Computing and Software for Big Science, see https://www.springer.com/journal/4178

Container-Managed ETL Applications for Integrating Data in Near Real-Time

As the analytical capabilities and applications of e-business systems expand, providing real-time access to critical business performance indicators to improve the speed and effectiveness of business operations has become crucial. The monitoring of business activities requires focused, yet incremental enterprise application integration (EAI) efforts and balancing information requirements in real-time with historical perspectives. The decision-making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in a timely manner. In this paper, we present an architecture for a container-based ETL (extraction, transformation, loading) environment, which supports a continual near real-time data integration with the aim of decreasing the time it takes to make business decisions and to attain minimized latency between the cause and effect of a business decision. Instead of using vendor proprietary ETL solutions, we use an ETL container for managing ETLets (pronounced “et-lets”) for the ETL processing tasks. The architecture takes full advantage of existing J2EE (Java 2 Platform, Enterprise Edition) technology and enables the implementation of a distributed, scalable, near real-time ETL environment. We have fully implemented the proposed architecture. Furthermore, we compare the ETL container to alternative continuous data integration approaches

Decision making on operational data: a remote approach to distributed data monitoring

Information gathering and assimilation is normally performed by data mining tools and Online analytic processing (OLAP) operating on historic data stored in a data warehouse. Data mining and OLAP queries are very complex, access a significant fraction of a database and require significant time and resources to be executed. Therefore, it has been impossible to draw the data analysis benefits in operational data environments. When it comes to analysis of operational (dynamic) data, running complex queries on frequently changing data is next to impossible. The complexity of active data integration increases dramatically in distributed applications which are very common in automated or e-commerce applications. We suggest a remote data analysis approach to find hidden patterns and relationships in distributed operational data, which does not adversely affect routine transaction processing. Distributed data integration on frequently updated data has been performed by analysing SQL commands coming to the distributed databases and aggregating data centrally to produce a real-time view of fast changing data. This approach has been successfully evaluated on data sources for over 30 data sources for hotel properties. This paper presents the performance results of the method, and its comparative study of the state-of-the art data integration techniques. The remote approach to data integration and analysis has been built into a scalable data monitoring system. It demonstrates the ease of application and performance results of operational data integration

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper