Learning chemistry with VirtualLabs@Uma: a customizable 3D platform for new experimental protocols

In general, students of laboratory courses such as chemistry or biology are not able to replicate at home an experiment of a previously studied class since they lack infrastructure and material. With the possibility of providing multimedia and virtual reality environments on the Web, different applications and virtual laboratories have been proposed. However, most of the existing tools are not flexible enough or are domain-oriented, not supporting the addition of new tailor-made experiments when needed. This paper introduces a new platform for providing a customizable Virtual Laboratory, VirtualLabs@UMa. This application was proposed at University of Madeira in order to provide students with a flexible 3D virtual laboratory and teachers with a platform that can be customized to new experimental protocols. Therefore, chemistry teachers are able to create their own experimental protocols and propose them to their students. Moreover, students can be followed accordingly having their learning needs and difficulties fulfilled. VirtualLabs@UMa is a solution for motivating students and proposing an added value for complementing laboratory courses.     

Scheduling streaming applications on a complex multicore platform

In this paper, we consider the problem of scheduling streaming applications described by complex task graphs on a heterogeneous multicore platform, the IBM QS 22 platform, embedding two STI Cell Broadband Engine processor. We first derive a complete computation and communication model of the platform on the basis of comprehensive benchmarks. Then we use this model to express the problem of maximizing the throughput of a streaming application on this platform. Although the problem is proven NP‐complete, we present an optimal solution based on mixed linear programming. We also propose simpler scheduling heuristics to compute mapping of the application task graph on the platform. We then come back to the platform and propose a scheduling software to deploy streaming applications on this platform. This allows us to thoroughly test our scheduling strategies on the real platform. We thus show that we are able to achieve a good speed‐up either with the mixed linear programming solution or using involved scheduling heuristics. Copyright © 2011 John Wiley & Sons, Ltd.     

Efficient consolidation-aware VCPU scheduling on multicore virtualization platform

Multicore processors are widely used in today’s computer systems. Multicore virtualization technology provides an elastic solution to more efficiently utilize the multicore system. However, the Lock Holder Preemption (LHP) problem in the virtualized multicore systems causes significant CPU cycles wastes, which hurt virtual machine (VM) performance and reduces response latency. The system consolidates more VMs, the LHP problem becomes worse. In this paper, we propose an efficient consolidation-aware vCPU (CVS) scheduling scheme on multicore virtualization platform. Based on vCPU over-commitment rate, the CVS scheduling scheme adaptively selects one algorithm among three vCPU scheduling algorithms: co-scheduling, yield-to-head, and yield-to-tail based on the vCPU over-commitment rate because the actions of vCPU scheduling are split into many single steps such as scheduling vCPUs simultaneously or inserting one vCPU into the run-queue from the head or tail. The CVS scheme can effectively improve VM performance in the low, middle, and high VM consolidation scenarios. Using real-life parallel benchmarks, our experimental results show that the proposed CVS scheme improves the overall system performance while the optimization overhead remains low.     

ST-Hadoop: a MapReduce framework for spatio-temporal data

This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data types and operations. In the indexing layer, ST-Hadoop spatiotemporally loads and divides data across computation nodes in Hadoop Distributed File System in a way that mimics spatio-temporal index structures, which result in achieving orders of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. In the operations layer, ST-Hadoop shipped with support for three fundamental spatio-temporal queries, namely, spatio-temporal range, top-k nearest neighbor, and join queries. Extensibility of ST-Hadoop allows others to extend features and operations easily using similar approaches described in the paper. Extensive experiments conducted on large-scale dataset of size 10 TB that contains over 1 Billion spatio-temporal records, to show that ST-Hadoop achieves orders of magnitude better performance than Hadoop and SpaitalHadoop when dealing with spatio-temporal data and operations. The key idea behind the performance gained in ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System.     

MapReduce scheduling algorithms: a review

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. Scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point.

     

元数据驱动的可定制短信自主服务中间件

为了解除短信服务与具体业务系统的强耦合关系,解决短信服务的可维护性、可复用性问题,实现服务逻辑的可定制目标,提出一种元数据驱动的可定制短信服务中间件设计方案.对短信服务建模,抽象出具有柔性机制的通用服务模型.定义和使用元数据,实现短信服务的可定制.基于元数据的异构数据实时集成,使短信服务能够适配异构业务数据源,实现中间件的通用化.中间件的部署使用,证明可定制短信服务中间件具有良好通用性、可维护性、可靠性,能够适应短信服务需求的不断变化.     

JackHare: a framework for SQL to NoSQL translation using MapReduce

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.     

SkyAlign: a portable,work-efficient skyline algorithm for multicore and GPU architectures

The skyline operator determines points in a multidimensional dataset that offer some optimal trade-off. State-of-the-art CPU skyline algorithms exploit quad-tree partitioning with complex branching to minimise the number of point-to-point comparisons. Branch-phobic GPU skyline algorithms rely on compute throughput rather than partitioning, but fail to match the performance of sequential algorithms. In this paper, we introduce a new skyline algorithm, SkyAlign, that is designed for the GPU, and a GPU-friendly, grid-based tree structure upon which the algorithm relies. The search tree allows us to dramatically reduce the amount of work done by the GPU algorithm by avoiding most point-to-point comparisons at the cost of some compute throughput. This trade-off allows SkyAlign to achieve orders of magnitude faster performance than its predecessors. Moreover, a NUMA-oblivious port of SkyAlign outperforms native multicore state of the art on challenging workloads by an increasing margin as more cores and sockets are utilised.     

NLR-OP: a high-performance optical router based on North-Last turning model for multicore processors

The Journal of Supercomputing - Regarding the increase in the number of cores in the electronic network-on-chip, they may not be an ideal choice in the response of needing latency, power, and...     

Joust: a platform for liquid software

The authors describe a Java-based platform for liquid software, called Joust, that is specifically designed to support low-level, communication-oriented systems and to avoid the limitations of general-purpose OSs. The authors contrast the platform requirements for communication-oriented liquid software with those of computation-oriented software, identify the limitations of current platforms, and outline the benefits of Joust. They also offer an overview of Scout (the underlying OS upon which Joust is built), its runtime system, and its just-in-time (JIT) compiler     

Simultaneous multithreading: a platform for next-generationprocessors

Simultaneous multithreading is a processor design which consumes both thread-level and instruction-level parallelism. In SMT processors, thread-level parallelism can come from either multithreaded, parallel programs or individual, independent programs in a multiprogramming workload. Instruction-level parallelism comes from each single program or thread. Because it successfully (and simultaneously) exploits both types of parallelism, SMT processors use resources more efficiently, and both instruction throughput and speedups are greater     

Energy-efficient multithreading for a hierarchical heterogeneous multicore through locality-cognizant thread generation

Energy costs have become increasingly problematic for high performance processors, but the rising number of cores on-chip offers promising opportunities for energy reduction. Further, emerging architectures such as heterogeneous multicores present new opportunities for improved energy efficiency. While previous work has presented novel memory architectures, multithreading techniques, and data mapping strategies for reducing energy, consideration to thread generation mechanisms that take into account data locality for this purpose has been limited. This study presents methodologies for the joint partitioning of data and threads to parallelize sequential codes across an innovative heterogeneous multicore processor called the Passive/Active Multicore (PAM) for reducing energy consumption from on-chip data transport and cache access components while also improving execution time. Experimental results show that the design with automatic thread partitioning offered reductions in energy-delay product (EDP) of up to 48%.     

SPECSA: a scalable, policy-driven, extensible, and customizable security architecture for wireless enterprise applications

This paper presents SPECSA, a new, optimized, policy-driven security architecture for wireless enterprise applications. SPECSA is scalable, extensible, flexible, and customizable. It supports end-to-end client authentication, data integrity and confidentiality between wireless clients and enterprise servers. The security services provided by SPECSA are customized and controlled by an easily configurable security policy that specifies several security-related attributes, classifies network data based on sensitivity and content, and provides an abstraction for the communication and messaging between the client and the server. In addition, SPECSA provides a standard Application Programming Interface (API) that conceals to a great extent the complexity of security operations and programming from the application developer who may not be experienced with enterprise security programming. SPECSA was designed in a platform-neutral manner and can be implemented on a wide range of wireless clients ranging from low-end platforms such as the Java 2 Mobile Edition/Connected Limited Device Configuration (J2ME/CLDC) on limited-memory mobile devices to Personal Java and the Net Compact Framework on PDAs. On the server side, SPECSA can be implemented on any of the available enterprise server platforms. A sample implementation of SPECSA was developed for J2ME on the client-side and for Java 2 Enterprise Edition (J2EE) on the server-side.     

Analytical Performance Models for MapReduce Workloads

MapReduce is a currently popular programming model to support parallel computations on large datasets. Among the several existing MapReduce implementations, Hadoop has attracted a lot of attention from both industry and research. In a Hadoop job, map and reduce tasks coordinate to produce a solution to the input problem, exhibiting precedence constraints and synchronization delays that are characteristic of a pipeline communication between maps (producers) and reduces (consumers). We here address the challenge of designing analytical models to estimate the performance of MapReduce workloads, notably Hadoop workloads, focusing particularly on the intra-job pipeline parallelism between map and reduce tasks belonging to the same job. We propose a hierarchical model that combines a precedence graph model and a queuing network model to capture the intra-job synchronization constraints. We first show how to build a precedence graph that represents the dependencies among multiple tasks of the same job. We then apply it jointly with an approximate Mean Value Analysis (aMVA) solution to predict mean job response time, throughput and resource utilization. We validate our solution against a queuing network simulator and a real setup in various scenarios, finding very close agreement in both cases. In particular, our model produces estimates of average job response time that deviate from measurements of a real setup by less than 15 %.     

GMR: graph-compatible MapReduce programming model

Multimedia Tools and Applications - The MapReduce programming model is widely used to parallelize data processing over the large scale of commodity computer clusters. However, on account of its...     

Hierarchical circuit-switched NoC for multicore video processing

Today’s prevailing video systems demand extreme performance that can be efficiently supported by parallel computing engines. This paper presents a novel hierarchical circuit-switched ring network on chip (called HrNoC) for the parallel engines, of which the cost, power, and latency have been extensively optimized from bottom up. First, a communication scheme “wave” is proposed for both intra-ring and inter-ring routing-paths built with rapid stream transactions. Then, a cost-effective bridge featuring deterministic packet traversal and deadlock avoidance is designed for flexible inter-ring connections. Finally, varied configurations of hierarchical rings are exploited by system specification and application mapping. In experiments, the proposed HrNoC on a 16-core multicore system performs about 50% latency reduction, 1/3 area cost, and 1/5 power consumption, compared with a packet-switched mesh NoC.