Exploring cosmology applications on distributed environments

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

Community detection in networks is a hard to solve combinatorial optimization problem with large-scale applications such as those based on flight networks. To approach this problem, a number of heuristics can be found in the literature, among them we highlight multilevel algorithms which are very efficient in detecting communities in larger networks. In this paper, we propose an improvement of a multilevel community detection algorithm that aims at finding communities so as to maximize the well-known modularity measure. The proposed improvement is the inclusion of a refinement phase to enhance the modularity value in flight networks we compiled. We presented a study case on the communities found for real flight networks. The vertices of the constructed networks correspond to airports in Europe, South America, United States of America and Canada and the arcs represent the flights whose weights indicate the number of flights between pairs of destinations. Experiments with these networks pointed to an improvement in the modularity of the communities found by the proposed algorithm when contrasted with its original version and the Lovain method. We visually identified consistent communities in the flight networks. Besides, in comparison to other reference algorithms on benchmark networks, ours was very competitive.

Optimal multimedia transport on the Internet

A major obstacle facing developers of large-scale distributed applications is deployment of the software under real-world conditions. While the product may hold up under lab conditions, it can fail horribly when faced with actual users using the service over the Internet. Part of the problem lies in the fact that there is very little support for software developers to examine the true behavioral nature of their software at scale. In order to determine this nature, it is important to be able reproduce a large number of different, realistic environmental conditions repeatedly and consistently. Without this ability, it becomes very difficult to argue that the observed behavior is truly representative of the system. Put in a different perspective, while characteristics like average throughput, response delay, and time between failures can be computed based on the lab results, it is unclear whether these measurements do indeed capture the true statistical properties of the system, as opposed to a momentary, rare blip. This work presents a prototype implementation of a framework that allows automated testing, including repeatable experiments (as required by the tenets of the scientific method), sweeping through experiment parameters, and supporting the ability to engage in closed-loop testing. The intention of this prototype effort is to build on top of existing testbed capabilities, such as those provided by Emulab. The results of using this prototype framework to test an industry-held distributed system, and to research collaborative worm propagation mitigation strategies, are shown.

In this paper, we present a portable simulation environment GridMate for large-scale adaptive scientific applications in multi-site Grid environments. GridMate is a discrete-event based simulator, consisting of abstractions of trace-based applications, computing resources, partitioners and schedulers, a 3D visualization tool, and user interfaces. It supports the analysis of runtime management strategies that address spatial and temporal heterogeneity in both adaptive scientific applications and geographically distributed resources in Grid computing environments. The targeted applications are a class of emerging large-scale dynamic Grid applications that require large amount of computational resources typically spanning multiple sites and exhibit long execution times. The underlying partitioning and scheduling algorithms are based on our previous work on the hybrid space-time runtime management strategy (HRMS). HRMS defines a set of flexible mechanisms and policies to adapt to state transitions of both applications and resources. The major components of GridMate are developed in Java, making GridMate highly portable and extensible. The design of GridMate and simulation results using GridMate are presented.

In order to study the performance of scheduling algorithms, simulators of parallel and distributed applications need accurate models of the application's behavior during execution. For this purpose, traces of low-level events collected during the actual execution of real applications are needed. Collecting such traces is a difficult task due to the timing, to the interference of instrumentation code, and to the storage and transfer of the collected data. To address this problem we propose a comprehensive software architecture, which instruments the application's executables, gather hierarchically the traces, and post-process them in order to feed simulation models. We designed it to be scalable, modular and extensible.

This article investigates geopolitical interactions between Europe and the United States (US) in large-scale outer space application programmes, which I term transatlantic astropolitics. Europe and the US have transcended tangible geographical constraints on terrestrial territories and extended geopolitics to outer space with advanced technologies, thereby making outer space a new ‘space’ for transatlantic interaction. Transatlantic astropolitics reveals an instrumental rationality that both sides pursue interests with respective cost-effective calculation rather than shared norms, common culture, or collective identities. This phenomenon indicates an inconsistency between the discourse of transatlantic security community and practices of European and US individual geopolitical interest satisfaction. This article provides a coupling between theories of international relations (IR) and political geography in outer space issue area, and thereby identifies the role geopolitical thoughts play in transatlantic astropolitics. This article concludes with the validity of neo-classical astropolitics in explaining outer space activities, and cost-efficient calculation as the cause of strategic variation in transatlantic astropolitics.

The interconnection of geographically distributed supercomputers via highspeed networks makes available the needed compute power for large-scale scientific applications, such as finite element applications. In this paper we propose a two-level data decomposition method for efficient execution of finite element applications on a network of supercomputers. Our method exploits the following features that may be different for each supercomputer in the system: processor speed, number of processors used from each supercomputer, local network performance, wide area network performance and wide area topology. Preliminary experiments involving a nonlinear, finite element application executed on a network of two supercomputers, one located at Argonne National Laboratory and the other one at the Cornell Theory Center, demonstrate a 20% reduction in execution time when the proposed decomposition is used as compared with naively applying conventional decompositions that are applicable to single supercomputers.

Many current international scientific projects are based on large scale applications that are both computationally complex and require the management of large amounts of distributed data. Grid computing is fast emerging as the solution to the problems posed by these applications. To evaluate the impact of resource optimisation algorithms, simulation of the Grid environment can be used to achieve important performance results before any algorithms are deployed on the Grid. In this paper, we study the effects of various job scheduling and data replication strategies and compare them in a variety of Grid scenarios using several performance metrics. We use the Grid simulator \textsf{OptorSim} , and base our simulations on a world-wide Grid testbed for data intensive high energy physics experiments. Our results show that scheduling algorithms which take into account both the file access cost of jobs and the workload of computing resources are the most effective at optimising computing and storage resources as well as improving the job throughput. The results also show that, in most cases, the economy-based replication strategies which we have developed improve the Grid performance under changing network loads.

Legionella and Mycobacterium can proliferate within free-living amoebae (FLA) where they are protected from disinfectants at concentrations that can kill bacteria but not protozoa. Despite effective treatment of drinking water, microbes can enter water utility distribution systems (DS) and hence the plumbing within building premises. Additionally, biofilm formation may account for the persistence of microbes in the DS. In the present study a domestic water tap in north-central United States (USA) was sampled in March and September 2007 and analysed for FLA, Legionella and Mycobacterium. Identification of organisms was determined by growth on specific culture media, light and electron microscopy, and amplification of DNA probes specific for each organism. In both the spring and fall samples, amoebae, Legionella and Mycobacterium were detected. However, Acanthamoeba was prominent in the spring sample whereas Vahlkampfia and Naegleria were the amoebae detected in the autumn. Bacterial proliferation in laboratory cultures was noticeably enhanced in the presence of amoebae and biofilms rapidly formed in mixed amoebae and bacteria cultures. It is hypothesized that temperature affected the dynamics of FLA species population structure within the DS and that pathogenic bacteria that proliferate within FLA, which are themselves opportunistic pathogens, pose dual public health risks.

High entropy materials offer almost unlimited catalytic possibilities due to their variable composition, unique structure, and excellent electrocatalytic performance. However, due to the strong tendency of nanoparticles to coarsen and agglomerate, it is still a challenge to synthesize nanoparticles using simple methods to precisely control the morphology and size of the nanoparticles in large quantities, and their large-scale application is limited by high costs and low yields. Herein, a series of high-entropy oxides (HEOs) nanoparticles with high-density and ultrasmall size (<5nm) loaded on carbon nanosheets with large quantities are prepared by Joule-heating treatment of gel precursors in a short period of time (≈60s). Among them, the prepared (FeCoNiRuMn)3O4-x catalyst shows the best electrocatalytic activity for oxygen evolution reaction, with low overpotentials (230mV @10mAcm-2, 270mV @100mAcm-2), small Tafel slope (39.4mVdec-1), and excellent stability without significant decay at 100mAcm-2 after 100h. The excellent performance of (FeCoNiRuMn)3O4-x can be attributed to the synergistic effect of multiple elements and the inherent structural stability of high entropy systems. This study provides a more comprehensive design idea for the preparation of efficient and stable high entropy catalysts.

Large-scale MV efficient frontier computation via a procedure of parametric quadratic programming

A wide range of scientific computing applications still use algorithms provided by large old code or libraries, that rarely make profit from multiple cores architectures and hardly ever are distributed. In this paper we propose a flexible strategy for execution of those legacy codes, identifying main modules involved in the process. Key technologies involved and a tentative implementation are provided allowing to understand challenges and limitations that surround this problem. Finally a case study is presented for a large-scale, single threaded, stochastic geostatistical simulation, in the context of mining and geological modeling applications. A successful execution, running time and speedup results are shown using a workstation cluster up to eleven nodes.

On the impact of process replication on executions of large-scale parallel applications with coordinated checkpointing