CMS Monte Carlo production in the WLCG computing Grid

Monte Carlo production in CMS has received a major boost in performance and scale since the past CHEP06 conference. The production system has been re-engineered in order to incorporate the experience gained in running the previous system and to integrate production with the new CMS event data model, data management system and data processing framework. The system is interfaced to the two major computing Grids used by CMS, the LHC Computing Grid (LCG) and the Open Science Grid (OSG). Operational experience and integration aspects of the new CMS Monte Carlo production system is presented together with an analysis of production statistics. The new system automatically handles job submission, resource monitoring, job queuing, job distribution according to the available resources, data merging, registration of data into the data bookkeeping, data location, data transfer and placement systems. Compared to the previous production system automation, reliability and performance have been considerably improved. A more efficient use of computing resources and a better handling of the inherent Grid unreliability have resulted in an increase of production scale by about an order of magnitude, capable of running in parallel at the order of ten thousand jobs and yielding more than two million events per day

HPS-HDS:High Performance Scheduling for Heterogeneous Distributed Systems

Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, data-aware service allocation, Big Data management and processing, performance analysis and optimization

Development of a Smart Grid Course in an Electrical Engineering Technology Program

Electric power systems courses have been traditionally offered by electrical engineering technology programs for a long time, with the main objective to introduce students to the fundamental concepts in the field of electric power systems and electrical to mechanical energy conversion. A typical electric power systems course covers a variety of topics, such as general aspects of electric power system design, electric generators, components of transmission and distribution systems, power flow analysis, system operation, and performance measures. In the last decades, electric power systems have significantly modernized alongside with requirement of improvement in system efficiency, reliability, cybersecurity, and environmental sustainability. The current modernized grid is called “Smart Grid,” which integrates advanced sensing technologies, control methods using machine learning approaches, and integrated communications into current electric power systems. Consequently, offered electric power systems courses are required to update in electrical engineering technology as well, to meet the industry needs of a workforce prepared to integrate smart grid technologies, such as advanced sensing, control, monitoring, communication, renewable energy, storage, computing, cybersecurity, etc. However, such updates of the course content are not always easy to implement due to the complexity of smart grid technologies and the limited number of instructors having knowledge of those technologies. In addition, smart grid courses should include a hands-on component aligned with the theoretical upgrades introduced in the course in the form of term projects. Such projects can be on a variety of topics, such as smart home/building, smart meter, smart distribution system, microgrid, communication infrastructure, Distributed energy resources (DERs) (e.g., rooftop solar photovoltaics (PV), wind), electric vehicle (EV), customer engagement, energy generation forecasting, load forecasting, and others. This paper will discuss the details of introducing a new course on smart grids in an electrical engineering technology program, including detailed examples of project selection

Introducing risk management into the grid

Service Level Agreements (SLAs) are explicit statements about all expectations and obligations in the business partnership between customers and providers. They have been introduced in Grid computing to overcome the best effort approach, making the Grid more interesting for commercial applications. However, decisions on negotiation and system management still rely on static approaches, not reflecting the risk linked with decisions. The EC-funded project "AssessGrid" aims at introducing risk assessment and management as a novel decision paradigm into Grid computing. This paper gives a general motivation for risk management and presents the envisaged architecture of a "risk-aware" Grid middleware and Grid fabric, highlighting its functionality by means of three showcase scenarios

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment