Many 19thcentury Romanian texts are written in the Transitional Script (RTS) combining Cyrillic and Latin characters in variable proportions depending on specific factors (period, region, publishing house, literary trends, and personal beliefs). Thousands of heritage documents in numerous libraries across Romania have yet to be digitized. Reading texts written in RTS is challenging for modern scholars, today’s students and OCR engines which due to specific particularities have difficulties in processing them. Transfer learning can be used to train state-of-the-art tools such as Tesseract or Transkribus, but their efficiency varies according to the underlying deep neural network, the training parameters, and the particularities of the scanned images and used scripts. In this paper, we compare the performance of two architectures on 180 OCR models for 5 scenarios (best CER <4.6% for Tesseract) and discuss OCR challenges on RTS texts.

Nowcasting is vital for PV farms in order to match supply and demand on energy markets. The accuracy of existing forecasting methods relies on short term modelling of cloud movement and dynamics by combining satellite images with ground based allsky observations. Cloud movement is modelled based on complex nonlinear numerical models which on the short term are outperformed by image analysis techniques. In this research we propose a simplified nature inspired model for nowcasting which scales with the data. The model is based on forecasting wind direction extracted from motion vector fields by modelling wind using a modified Boids Flocking algorithm. We show that the model is accurate in predicting the cloud coverage up to several hours ahead and that it scales with the number of particles on shared memory systems suited for commodity machines available to PV farm operators.

Cloud computing is used by numerous applications tailored for on-demand execution on elastic resources. While most cloud based applications rely mostly on virtualization, an emerging technology based on lightweight containers is starting to gain traction. While most research on job scheduling on clouds has focused on dedicated machines, the emergence and applicability of containers on a wider range of platforms including IoT, reopens the issue of scheduling on non-dedicated machines with high priority background load. In this paper we address this problem by proposing a model and several heuristics for scheduling non-preemptive data stream jobs on containers running on machines with background load. We also address the issue of estimating the container parameters. The heuristics are tested and analyzed based on real-life traces.

This paper investigates the scalability of WRF (Weather Research and Forecast) model on three different platforms: BlueGene/P, Intel Xeon Cluster and Microsoft Azure cloud at different resolutions and domain sizes. Contrary to prior work we benchmark the model on a cloud platform, analyze the behavior of various individual configurations, and test the scalability of our previously proposed parallel genetic algorithm for physical parametrization of WRF. While we obtain good results on all platforms, the speedup is particularly interesting on the cloud platform, peaking at 10x using OpenMP library with up to 20 processors. This is similar to that achieved on Bluegene/P on 1,024 cores. On the Bluegene/P and Xeon cluster we used the MPI library which provided speedups ranging between 2--10x. Overall, running on Azure or the Xeon cluster is more effective than running experiments on Bluegene/P especially at low resolutions and with few processors. Finally, we tested the scalability of a parallel GA implementation with the purpose of finding optimal physical parametrization settings for WRF and achieved speedups up to 6x which proved superior than those from a similar experiment done in a prior paper.

Summary A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright © 2016 John Wiley & Sons, Ltd.

The envisioned intercloud bridging numerous cloud providers offering clients the ability to run their applications on specific configurations unavailable to single clouds poses challenges with respect to selecting the appropriate resources for deploying VMs. Reasons include the large distributed scale and VM performance fluctuations. Reusing previously "successful" resource coalitions may be an alternative to a brute force search employed by many existing scheduling algorithms. The reason for reusing resources is motivated by an implicit trust in previous successful executions that have not experienced VM performance fluctuations described in many research papers on cloud performance. Furthermore, the data deluge coming from services monitoring the load and availability of resources forces a shift in traditional centralized and decentralized resource management by emphasizing the need for edge computing. In this way only meta data is sent to the resource management system for resource matchmaking. In this paper we propose a bottom-up monitoring architecture and a proof-of-concept platform for scheduling applications based on resource coalition reuse. We consider static coalitions and neglect any interference from other coalitions by considering only the historical behavior of a particular coalition and not the overall state of the system in the past and now. We test our prototype on real traces by comparing with a random approach and discuss the results by outlying its benefits as well as some future work on run time coalition adaptation and global influences.

Astrophysical applications are known to be data and computationally intensive with large amounts of images being generated by telescopes on a daily basis. To analyze these images data mining, statistical, and image processing techniques are applied on the raw data. Big data platforms such as MapReduce are ideal candidates for processing and storing astrophysical data due to their ability to process loosely coupled parallel tasks. These platforms are usually deployed in clouds, however, most astrophysical applications are legacy applications that are not optimized for cloud computing. While some work towards exploiting the benefits of Hadoop to store astrophysical data and to process the large datasets exists, not much research has been done to assess the scalability of cloud enabled astrophysical applications. In this work we analyze the data and resource scalability of MapReduce applications for astrophysical problems related to cluster detection and inter cluster spatial pattern search. The maximum level of parallelism is bounded by the number of clusters and the number of (cluster, subcluster) pairs in the pattern search. We perform scale-up tests on Google Compute Engine and Amazon EC2. We show that while data scalability is achieved, resource scalability (scale up) is bounded and moreover seems to depend on the underlying cloud platform. For future work we also plan to investigate the scale out on tens of instances with large input files of several GB.

This paper describes a semantic modeling of the sensor data streams. In order to fulfill the requirements of the Wireless Sensor Networks and Wireless Body Area Networks data streams, for real-time vital signs monitoring, we designed a flexible architecture based on Multi-agent system. The system is able to collect and build sensor data streams according to the proposed semantic model. Different data attributes are presented in order to describe timestamp, vital signs, values, etc., for each reading. The system can handle the large amount of sensed physiological data from different sensor nodes and to transmit only average values for a specific time series reducing unnecessary transmissions and energy consumption.

Summary In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright © 2015 John Wiley & Sons, Ltd.

The purpose of this special issue is to collect the best papers presented at the Distributed Computing (DC) track of the 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) held on September 22-25, 2014 in Timisoara, Romania. The multidisciplinary nature of the conference brings together people from various computer science areas, including symbolic computing, numerical analysis, multi-agent systems, natureinspired processing, and distributed/parallel computing. These topics provide the attendees the chance to uncover interdisciplinary research ideas and concrete-use cases for their work.

Prosumers or proactive consumers are steadily on the rise in emerging Smart Grid systems. These consumers, apart from their traditonal role of using energy from the grid, also are actively involved in individually transferring stored energy from renewable sources such as wind and solar, to the grid. The large-scale integration of renewable generation in the emerging grid will re-define ways of meeting consumer energy demands, and more importantly drive greener and cost-effective utility operations. In this paper, we investigate the problem of matching consumer demand with the grid supply in real-time, and in the presence of renewables. We formulate this problem as a stochastic optimization problem and propose MATCH, a fast distributed real-time algorithm that accounts for the uncertainties in (i) renewable generation, (ii) the latter's transmission through the grid network, (iii) loads, and (iv) energy prices, and balances power in the Smart Grid at all times. MATCH is based on the Lyapunov stochastic optimization framework and scales to localities with a large number of networked renewable generation sources. We validate the efficacy of MATCH through experiments conducted using data modelled on proprietary data obtained from two public utilities. As part of the main results of this work, we show that (a) MATCH outputs unique approximate-optimal grid parameter configuration vectors in real-time that ensure perennial supplydemand balance in the grid at a minimum cost, and (b) mesh transmission network topologies lead to better MATCH outputs when compared to other existing transmission network topologies.

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in aprovably optimalreal-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online)randomized approximation algorithmfor grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.

Existing Big Data streams coming from social and other connected sensor networks exhibit intrinsic inter-dependency enabling unique challenges to scalable graph analytics. Data from these graphs is usually collected in different geographically located data servers making it suitable for distributed processing on clouds. While numerous solutions for large scale static graph analysis have been proposed, addressing in real-time the dynamics of social interactions requires novel approaches that leverage incremental stream processing and graph analytics on elastic clouds. We propose a scalable solution based on our stream processing engine, Floe, on top of which we perform real-time data processing and graph updates to enable low latency graph analytics on large evolving social networks. We demonstrate the platform on a large Twitter data set by performing several fast graph and non-graph analytics to extract in real-time the top k influential nodes, with different metrics, during key events such as the US NFL playoffs. This information allows advertisers to maximize their exposure to the public by always targeting the continuously changing set of most influential nodes. Its applicability spans multiple domains including surveillance, counter-terrorism, or disease spread monitoring. The evaluation will be performed on a combination our local cluster of 16 eight-core nodes running Eucalyptus fabric and 100s of virtual machines on the Amazon AWS public cloud. We will showcase the low latency in detecting changes in the graph under variable data streams, and also the efficiency of the platform to utilize resources and to elastically scale to meet demand.

The MapReduce programming model, due to its simplicity and scalability, has become an essential tool for processing large data volumes in distributed environments. Recent Stream Processing Systems (SPS) this model to provide low-latency analysis of high-velocity continuous data streams. However, integrating MapReduce with streaming poses challenges: first, the runtime variations in data characteristics such as data-rates and key-distribution cause resource overload, that in-turn leads to fluctuations in the Quality of the Service (QoS), and second, the stateful reducers, whose state depends on the complete tuple history, necessitates efficient fault-recovery mechanisms to maintain the desired QoS in the presence of resource failures. We propose an integrated streaming MapReduce architecture leveraging the concept of consistent hashing to support runtime elasticity along with locality-aware data and state replication to provide efficient load-balancing with low-overhead fault-tolerance and parallel fault-recovery from multiple simultaneous failures. Our evaluation on a private cloud shows up to 2.8× improvement in peak throughput compared to Apache Storm SPS, and a low recovery latency of 700 - 1500 ms from multiple failures.

Abstract—In a smart grid, data and information are trans-ported, transmitted, stored, and processed with various stake-holders having to cooperate effectively. Furthermore, personal data is the key to many smart grid applications and therefore privacy impacts have to be taken into account. For an effective smart grid, well integrated solutions are crucial and for achieving a high degree of customer acceptance, privacy should already be considered at design time of the system. To assist system engineers in early design phase, frameworks for the automated privacy evaluation of use cases are important. For evaluation, use cases for services and software architectures need to be formally captured in a standardized and commonly understood manner. In order to ensure this common understanding for all kinds of stakeholders, reference models have recently been developed. In this paper we present a model-driven approach for the automated assessment of such services and software architectures in the smart grid that builds on the standardized reference models. The focus of qualitative and quantitative evaluation is on privacy. For evaluation, the framework draws on use cases from the University of Southern California microgrid. I.

Graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over time, and on which novel algorithms both over the network structure and across the time-variant attribute values is necessary. We formalize the notion of time-series graphs and propose a Temporally Iterative BSP programming abstraction to develop algorithms on such datasets using several design patterns. Our abstractions leverage a sub-graph centric programming model and extend it to the temporal dimension. We present three time-series graph algorithms based on these design patterns and abstractions, and analyze their performance using the Offish distributed platform on Amazon AWS Cloud. Our results demonstrate the efficacy of the abstractions to develop practical time-series graph algorithms, and scale them on commodity hardware.

The need for low latency analysis over high-velocity data streams motivates the need for distributed continuous dataflow systems. Contemporary stream processing systems use simple techniques to scale on elastic cloud resources to handle variable data rates. However, application QoS is also impacted by variability in resource performance exhibited by clouds and hence necessitates autonomic methods of provisioning elastic resources to support such applications on cloud infrastructure. We develop the concept of “dynamic dataflows” which utilize alternate tasks as additional control over the dataflow's cost and QoS. Further, we formalize an optimization problem to represent deployment and runtime resource provisioning that allows us to balance the application's QoS, value, and the resource cost. We propose two greedy heuristics, centralized and sharded, based on the variable-sized bin packing algorithm and compare against a Genetic Algorithm (GA) based heuristic that gives a near-optimal solution. A large-scale simulation study, using the linear road benchmark and VM performance traces from the AWS public cloud, shows that while GA-based heuristic provides a better quality schedule, the greedy heuristics are more practical, and can intelligently utilize cloud elasticity to mitigate the effect of variability, both in input data rates and cloud resource performance, to meet the QoS of fast data applications.

Smart grids are becoming popular with the advent of sophisticated smart meters. They allow utilities to optimize energy consumption during peak hours by applying various demand response techniques including voluntary curtailment, direct control and price incentives. To sustain the curtailment over long periods of time of up to several hours utilities need to make fast and accurate consumption predictions on a large set of customers based on a continuous flow of real time data and huge historical data sets. Given the numerous consumption patterns customers exhibit, different prediction methods need to be used to reduce the prediction error. The straightforward approach of testing each customer against every method is unfeasible in this large volume and high velocity environment. To this aim, we propose a neural network based approach for automatically selecting the best prediction method per customer by relying only on a small subset of customers. We also introduce two historical averaging methods for consumption prediction that take advantage of the variability of the data and continuously update the results based on a sliding window technique. We show that once trained, the proposed neural network does not require frequent retraining, ensuring its applicability in online scenarios such as the sustainable demand response.

This paper presents Schlouder, a broker of IaaS cloud resources which helps users to take advantage of IaaS elasticity. The main advantages of Schlouder are its simplicity, its extensibility, and its capability to provide the user with a prediction of the make span and cost, given the chosen provisioning strategy and before any actual execution. This paper illustrates how Schlouder enables, with a very limited engineering effort, the port of a bag-of-tasks scientific application which was originally developed for the European Grid Infrastructure. Experiments in real environment support assessments on Schlouder efficiency and comparison between grids and clouds for scientific computations. We conclude that porting grid applications to the cloud represents a shift in the associated problematics: from tailoring the application to the platform, to tailoring the platform to the application.

Distributed systems require effective mechanisms to manage the reliable provisioning of computational resources from different and distributed providers. Moreover, the dynamic environment that affects the behaviour of such systems and the complexity of these dynamics demand autonomous capabilities to ensure the behaviour of distributed scheduling platforms and to achieve business and user objectives. In this paper we propose a self-adaptive distributed scheduling platform composed of multiple agents implemented as intelligent feedback control loops to support policy-based scheduling and expose self-healing capabilities. Our platform leverages distributed scheduling processes by (i) allowing each provider to maintain its own internal scheduling process, and (ii) implementing self-healing capabilities based on agent module recovery. Simulated tests are performed to determine the optimal number of agents to be used in the negotiation phase without affecting the scheduling cost function. Test results on a real-life platform are presented to evaluate recovery times and optimize platform parameters.

Orchestrating composite applications inside distributed systems requires complex coordination. In this frame workflow orchestration engines provide a viable solution. Contrary to their centralized counterparts, decentralized workflow engines allow better scalability, autonomy and increased fault-tolerance. However, most of these systems lack a self-healing mechanism in order to cope with engine failures. This paper presents a distributed workflow engine enhanced with self-healing capabilities and message based communication. The engine is validated against two real case scenarios. Some results regarding transfer time between components and recovery times are also given.

As Distributed Systems begin to rely more and more on Service Oriented Architectures there is an increasingly need to store information remotely and to access to it by means of services. In this frame scheduling heuristics play an important role as they help reduce task execution costs. We propose a model that follows a nature inspired paradigm to represent the scheduling heuristics itself. Services are used to access remotely available data required by the algorithm. Furthermore a model to share the schedule data among multiple distributed scheduling algorithms that run in parallel is devised.

One of the benefits of the current service-oriented architectures is the easy static composition of geographically scattered services into complex applications. Dynamic composition is more difficult to achieve with the current technologies. We propose a practical solution for dynamic composition of the facilities provided by computer algebra systems, based on Grid services and the WS-BPEL standard Web service orchestration language. Moreover, we introduce a methodology for migrating from Web services to Grid services using databases for persistence.

Modern grid and Web technologies provide straightforward access to applications and services running on remote resources. The user (which may be a software developer or even an application) should be able to discover such a service and dynamically load an interface to interact with it. While some software systems support either fully or partially automatic creation of client software to access Web services, fewer support grid services in the same way. This paper introduces a new system that automatically generates client software to allow access to both grid and Web services. Although designed as a stand-alone tool for accessing any Web or grid service, we demonstrate its usefulness in the context of the SymGrid framework for grid-based symbolic computations.