Agglutinative languages such as Turkish, Finnish andHungarian require morphological disambiguation beforefurther processing due to the complex morphologyof words. A morphological disambiguator is usedto select the correct morphological analysis of a word.Morphological disambiguation is important because itgenerally is one of the first steps of natural languageprocessing and its performance affects subsequent analyses.In this paper, we propose a system that uses deeplearning techniques for morphological disambiguation.Many of the state-of-the-art results in computer vision,speech recognition and natural language processinghave been obtained through deep learning models.However, applying deep learning techniques to morphologicallyrich languages is not well studied. In this work,while we focus on Turkish morphological disambiguationwe also present results for French and German inorder to show that the proposed architecture achieveshigh accuracy with no language-specific feature engineeringor additional resource. In the experiments, weachieve 84.12 , 88.35 and 93.78 morphological disambiguationaccuracy among the ambiguous words forTurkish, German and French respectively.

Scientists increasingly rely on the execution of workflows in grids to obtain results from complex mixtures of applications. However, the inherently dynamic nature of grid workflow scheduling, stemming from the unavailability of scheduling information and from resource contention among the (multiple) workflows and the non-workflow system load, may lead to poor or unpredictable performance. In this paper we present a comprehensive and realistic investigation of the performance of a wide range of dynamic workflow scheduling policies in multicluster grids. We first introduce a taxonomy of grid workflow scheduling policies that is based on the amount of dynamic information used in the scheduling process, and map to this taxonomy seven such policies across the full spectrum of information use. Then, we analyze the performance of these scheduling policies through simulations and experiments in a real multicluster grid. We find that there is no single grid workflow scheduling policy with good performance across all the investigated scenarios. We also find from our real system experiments that with demanding workloads, the limitations of the head-nodes of the grid clusters may lead to performance loss not expected from the simulation results. We show that task throttling, that is, limiting the per-workflow number of tasks dispatched to the system, prevents the head-nodes from becoming overloaded while largely preserving performance, at least for communication-intensive workflows. 1.

In multicluster grid systems, parallel applications may benefit from processor coallocation, that is, the simultaneous allocation of processors in multiple clusters. Although coallocation allows the allocation of more processors than available in a single cluster, it may severely increase the execution time of applications due to the relatively slow wide-area communication. The aim of this paper is to investigate the benefit of coallocation in multicluster grid systems, despite this drawback. To this end, we have conducted experiments in a real multicluster grid environment, as well as in a simulated environment, and we evaluate the performance of coallocation for various applications that range from computation-intensive to communication-intensive and for various system load settings. In addition, we compare the performance of scheduling policies that are specifically designed for coallocation. We demonstrate that considering latency in the resource selection phase improves the performance of coallocation, especially for communication-intensive parallel applications.

The use of today's multicluster grids exhibits periods of submission bursts with periods of normal use and even of idleness. To avoid resource contention, many users employ observational scheduling, that is, they postpone the submission of relatively low-priority jobs until a cluster becomes (largely) idle. However, observational scheduling leads to resource contention when several such users crowd the same idle cluster. Moreover, this job execution model either delays the execution of more important jobs, or requires extensive administrative support for job and user priorities. Instead, in this work we investigate the use of cycle scavenging to run jobs on grid resources politely yet efficiently, and with an acceptable administrative cost. We design a two-level cycle scavenging scheduling architecture that runs unobtrusively alongside regular grid scheduling. We equip this scheduler with two novel cycle scavenging scheduling policies that enforce fair resource sharing among competing cycle scavenging users. We show through experiments with real and synthetic applications in a real multicluster grid that the proposed architecture can execute jobs politely yet efficiently.

Large-scale distributed computing systems such as grids are serving a growing number of scientists. These environments bring about not only the advantages of an economy of scale, but also the challenges of resource and workload heterogeneity. A consequence of these two forms of heterogeneity is that job runtimes and queue wait times are highly variable, which generally reduces system performance and makes grids difficult to use by the common scientist. Predicting job runtimes and queue wait times have been widely studied for parallel environments. However, there is no detailed investigation on how the proposed prediction methods perform in grids, whose resource structure and workload characteristics are very different from those in parallel systems. In this paper, we assess the performance and benefit of predicting job runtimes and queue wait times in grids based on traces gathered from various research and production grid environments. First, we evaluate the performance of simple yet widely used time series prediction methods and the effect of applying them to different types of job classes (e.g., all jobs submitted by single users or to single sites). Then, we investigate the performance of two kinds of queue wait time prediction methods for grids. Last, we investigate whether prediction-based grid-level scheduling policies can have better performance than policies that do not use predictions.

Ever more scientists are employing large-scale distributed systems such as grids for their computational work, instead of tightly coupled high-performance computing systems. However, while these distributed systems are more cost-effective, their heterogeneity in terms of hardware, software, and systems administration, and the lack of accurate resource information leads to inefficient scheduling. In addition, and in contrast to the workloads of tightly coupled high-performance computing systems, a large part of the workloads submitted to these distributed systems consists of large sets (bags) of sequential tasks. Therefore, a realistic performance analysis of scheduling bags-of-tasks in large-scale distributed systems is important. Towards this end, we introduce in this paper a realistic workload model for bags-of-tasks, and we explore through trace-based simulations the design space of scheduling bags-of-tasks. Finally, we identify three new scheduling policies that use only inaccurate information when scheduling, and we compare them against known classes of proposed scheduling policies.

In large-scale distributed execution environments such as multicluster systems and grids, resource availability may vary due to resource failures and because resources may be added to or withdrawn from such environments at any time. In addition, single sites in such systems may have to deal with workloads originating from both local users and from many other sources. As a result, application malleability, that is, the property of applications to deal with a varying amount of resources during their execution, may be very beneficial for performance. In this paper we present the design of the support of and scheduling policies for malleability in our Koala multicluster scheduler with the help of our Dynaco framework for application malleability. In addition, we show the results of experiments with scheduling malleable workloads with Koala in our DAS multicluster testbed.

In multicluster systems, and more generally, in grids, parallel applications may require co-allocation, i.e., the simultaneous allocation of resources such as processors in multiple clusters. Although co-allocation enables the allocation of more processors than available on a single cluster, depending on the applications' communication characteristics, it has the potential disadvantage of increased execution times due to relatively slow wide-area communication. In this paper, we present two job placement policies, the Cluster Minimization and the Flexible Cluster Minimization policies which take into account the wide-area communication overhead when co-allocating applications across the clusters. We have implemented these policies in our grid scheduler called KOALA in order to serve different job request types. To assess the performance of the policies, we perform experiments in a real multicluster testbed using communication-intensive parallel applications.