Relevant bibliographies by topics / Load graph

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Load graph'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Load graph"

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Bok, Kyoungsoo, Junwon Kim, and Jaesoo Yoo. "Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores." Symmetry 11, no. 7 (2019): 926. http://dx.doi.org/10.3390/sym11070926.

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Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform graph partitioning. It creates a cluster by grouping RDF subgraphs with higher usage frequency while creating a subcluster with lower usage frequency. These clusters and subclusters conduct load balancing by using the mean frequency of queries for the distributed server and conduct graph data partitioning by considering the size of the data stored in each distributed server. It also minimizes the number of edge-cuts connected to clusters and subclusters to minimize communication costs between servers. This solves the problem of data concentration to specific servers due to ongoing data changes and additions and allows efficient load balancing among servers. The performance results show that the proposed method significantly outperforms the existing partitioning methods in terms of query performance time in a distributed server.
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Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online load balancing under graph constraints." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (2013): 363–64. http://dx.doi.org/10.1145/2494232.2465751.

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Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online Load Balancing Under Graph Constraints." IEEE/ACM Transactions on Networking 24, no. 3 (2016): 1690–703. http://dx.doi.org/10.1109/tnet.2015.2442597.

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COSNARD, M., and M. LOI. "AUTOMATIC TASK GRAPH GENERATION TECHNIQUES." Parallel Processing Letters 05, no. 04 (1995): 527–38. http://dx.doi.org/10.1142/s0129626495000473.

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We present a model of parallel computation, the parameterized task graph, which is a compact, problem size independent, representation of some frequently used directed acyclic task graphs. Techniques automating the construction of such a representation, starting from an annotated sequential program are proposed. We show that many important properties of the task graph such as the computational load of the nodes and the communication volume of the edges can be automatically deduced in a problem size independent way.
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Yang, Carl, Aydın Buluç, and John D. Owens. "GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU." ACM Transactions on Mathematical Software 48, no. 1 (2022): 1–51. http://dx.doi.org/10.1145/3466795.

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High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity , which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in “GraphBLAST”, the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.
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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 01 (2009): 199–220. http://dx.doi.org/10.1017/s0021900200005313.

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In this paper we give an analytic solution for graphs with n nodes and E = cn log n edges for which the probability of obtaining a given graph G is µn (G) = exp (- β ∑i=1 n d i 2), where d i is the degree of node i. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expansion and conductance, and (ii) to analyse the graph resilience to random failures.
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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 1 (2009): 199–220. http://dx.doi.org/10.1239/jap/1238592125.

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In this paper we give an analytic solution for graphs withnnodes andE=cnlognedges for which the probability of obtaining a given graphGisµn(G) = exp (-β∑i=1ndi2), wherediis the degree of nodei. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expansion and conductance, and (ii) to analyse the graph resilience to random failures.
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Sharma, Bhuvan, Van C. Willis, Claudia S. Huettner, et al. "Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs." JAMIA Open 3, no. 3 (2020): 332–37. http://dx.doi.org/10.1093/jamiaopen/ooaa028.

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Abstract Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.
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Alistarh, Dan, Giorgi Nadiradze, and Amirmojtaba Sabour. "Dynamic Averaging Load Balancing on Cycles." Algorithmica 84, no. 4 (2021): 1007–29. http://dx.doi.org/10.1007/s00453-021-00905-9.

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AbstractWe consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step $$t\ge 0$$ t ≥ 0 , a random edge is chosen, one unit of load is created, and placed at one of the endpoints. In the same step, assuming that loads are arbitrarily divisible, the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Peres et al. (Random Struct Algorithms 47(4):760–775, 2015) studied the variant of this process, where the unit of load is placed in the least loaded endpoint of the chosen edge, and the averaging is not performed. In the case of dynamic load balancing on the cycle of length n the only known upper bound on the expected gap is of order $$\mathcal {O}( n \log n )$$ O ( n log n ) , following from the majorization argument due to the same work. In this paper, we leverage the power of averaging and provide an improved upper bound of $$\mathcal {O} ( \sqrt{n} \log n )$$ O ( n log n ) . We introduce a new potential analysis technique, which enables us to bound the difference in load between k-hop neighbors on the cycle, for any $$k \le n/2$$ k ≤ n / 2 . We complement this with a “gap covering” argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We also show that our analysis can be extended to the specific instance of Harary graphs. On the other hand, we prove that the expected second moment of the gap is lower bounded by $$\Omega (n)$$ Ω ( n ) . Additionally, we provide experimental evidence that our upper bound on the gap is tight up to a logarithmic factor.
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Jeurissen, R., and W. Layton. "Load balancing by graph coloring, an algorithm." Computers & Mathematics with Applications 27, no. 3 (1994): 27–32. http://dx.doi.org/10.1016/0898-1221(94)90043-4.

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Dissertations / Theses on the topic "Load graph"

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Barat, Remi. "Load Balancing of Multi-physics Simulation by Multi-criteria Graph Partitioning." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0961/document.

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Les simulations dites multi-physiques couplent plusieurs phases de calcul. Lorsqu’elles sont exécutées en parallèle sur des architectures à mémoire distribuée, la minimisation du temps de restitution nécessite dans la plupart des cas d’équilibrer la charge entre les unités de traitement, pour chaque phase de calcul. En outre, la distribution des données doit minimiser les communications qu’elle induit. Ce problème peut être modélisé comme un problème de partitionnement de graphe multi-critères. On associe à chaque sommet du graphe un vecteur de poids, dont les composantes, appelées « critères », modélisent la charge de calcul porté par le sommet pour chaque phase de calcul. Les arêtes entre les sommets, indiquent des dépendances de données, et peuvent être munies d’un poids reflétant le volume de communication transitant entre les deux sommets. L’objectif est de trouver une partition des sommets équilibrant le poids de chaque partie pour chaque critère, tout en minimisant la somme des poids des arêtes coupées, appelée « coupe ». Le déséquilibre maximum toléré entre les parties est prescrit par l’utilisateur. On cherche alors une partition minimisant la coupe, parmi toutes celles dont le déséquilibre pour chaque critère est inférieur à cette tolérance. Ce problème étant NP-Dur dans le cas général, l’objet de cette thèse est de concevoir et d’implanter des heuristiques permettant de calculer efficacement de tels partitionnements. En effet, les outils actuels renvoient souvent des partitions dont le déséquilibre dépasse la tolérance prescrite. Notre étude de l’espace des solutions, c’est-à-dire l’ensemble des partitions respectant les contraintes d’équilibre, révèle qu’en pratique, cet espace est immense. En outre, nous prouvons dans le cas mono-critère qu’une borne sur les poids normalisés des sommets garantit que l’espace des solutions est non-vide et connexe. Nous fondant sur ces résultats théoriques, nous proposons des améliorations de la méthode multi-niveaux. Les outils existants mettent en oeuvre de nombreuses variations de cette méthode. Par l’étude de leurs codes sources, nous mettons en évidence ces variations et leurs conséquences à la lumière de notre analyse sur l’espace des solutions. Par ailleurs, nous définissons et implantons deux algorithmes de partitionnement initial, se focalisant sur l’obtention d’une solution à partir d’une partition potentiellement déséquilibrée, au moyen de déplacements successifs de sommets. Le premier algorithme effectue un mouvement dès que celui-ci améliore l’équilibre, alors que le second effectue le mouvement réduisant le plus le déséquilibre. Nous présentons une structure de données originale, permettant d’optimiser le choix des sommets à déplacer, et conduisant à des partitions de déséquilibre inférieur en moyenne aux méthodes existantes. Nous décrivons la plate-forme d’expérimentation, appelée Crack, que nous avons conçue afin de comparer les différents algorithmes étudiés. Ces comparaisons sont effectuées en partitionnant un ensembles d’instances comprenant un cas industriel et plusieurs cas fictifs. Nous proposons une méthode de génération de cas réalistes de simulations de type « transport de particules ». Nos résultats démontrent la nécessité de restreindre les poids des sommets lors de la phase de contraction de la méthode multi-niveaux. En outre, nous mettons en évidence l’influence de la stratégie d’ordonnancement des sommets, dépendante de la topologie du graphe, sur l’efficacité de l’algorithme d’appariement « Heavy-Edge Matching » dans cette même phase. Les différents algorithmes que nous étudions sont implantés dans un outil de partitionnement libre appelé Scotch. Au cours de nos expériences, Scotch et Crack renvoient une partition équilibrée à chaque exécution, là où MeTiS, l’outil le plus utilisé actuellement, échoue une grande partie du temps. Qui plus est, la coupe des solutions renvoyées par Scotch et Crack est équivalente ou meilleure que celle renvoyée par MeTiS<br>Multiphysics simulation couple several computation phases. When they are run in parallel on memory-distributed architectures, minimizing the simulation time requires in most cases to balance the workload across computation units, for each computation phase. Moreover, the data distribution must minimize the induced communication. This problem can be modeled as a multi-criteria graph partitioning problem. We associate with each vertex of the graph a vector of weights, whose components, called “criteria”, model the workload of the vertex for each computation phase. The edges between vertices indicate data dependencies, and can be given a weight representing the communication volume transferred between the two vertices. The goal is to find a partition of the vertices that both balances the weights of each part for each criterion, and minimizes the “edgecut”, that is, the sum of the weights of the edges cut by the partition. The maximum allowed imbalance is provided by the user, and we search for a partition that minimizes the edgecut, among all the partitions whose imbalance for each criterion is smaller than this threshold. This problem being NP-Hard in the general case, this thesis aims at devising and implementing heuristics that allow us to compute efficiently such partitions. Indeed, existing tools often return partitions whose imbalance is higher than the prescribed tolerance. Our study of the solution space, that is, the set of all the partitions respecting the balance constraints, reveals that, in practice, this space is extremely large. Moreover, we prove in the mono-criterion case that a bound on the normalized vertex weights guarantees the existence of a solution, and the connectivity of the solution space. Based on these theoretical results, we propose improvements of the multilevel algorithm. Existing tools implement many variations of this algorithm. By studying their source code, we emphasize these variations and their consequences, in light of our analysis of the solution space. Furthermore, we define and implement two initial partitioning algorithms, focusing on returning a solution. From a potentially imbalanced partition, they successively move vertices from one part to another. The first algorithm performs any move that reduces the imbalance, while the second performs at each step the move reducing the most the imbalance. We present an original data structure that allows us to optimize the choice of the vertex to move, and leads to partitions of imbalance smaller on average than existing methods. We describe the experimentation framework, named Crack, that we implemented in order to compare the various algorithms at stake. This comparison is performed by partitioning a set of instances including an industrial test case, and several fictitious cases. We define a method for generating realistic weight distributions corresponding to “Particles-in-Cells”-like simulations. Our results demonstrate the necessity to coerce the vertex weights during the coarsening phase of the multilevel algorithm. Moreover, we evidence the impact of the vertex ordering, which should depend on the graph topology, on the efficiency of the “Heavy-Edge” matching scheme. The various algorithms that we consider are implemented in an open- source graph partitioning software called Scotch. In our experiments, Scotch and Crack returned a balanced partition for each execution, whereas MeTiS, the current most used partitioning tool, fails regularly. Additionally, the edgecut of the solutions returned by Scotch and Crack is equivalent or better than the edgecut of the solutions returned by MeTiS
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Predari, Maria. "Load balancing for parallel coupled simulations." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0369/document.

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Dans le contexte du calcul scientique, l'équilibrage de la charge est un problème crucial qui conditionne la performance des simulations numériques parallèles. L'objectif est de répartir la charge de travail entre un nombre de processeurs donné, afin de minimiser le temps global d'exécution. Une stratégie populaire pour résoudre ce problème consiste à modéliser la simulation à l'aide d'un graphe et à appliquer des algorithmes de partitionnement. En outre, les simulations numériques tendent à se complexifier, notamment en mixant plusieurs codes représentant des physiques différentes ou des échelles différentes. On parle alors de couplage de codes multi-physiques ou multi-échelles. Dans ce contexte, le problème de l'équilibrage de charge devient également plus difficile, car il ne s'agit plus d'équilibrer chacun des codes séparément, mais l'ensemble de ces codes pris dans leur globalité. Dans ce travail, on propose de resoudre ce problème en utilisant le modèle de partitionnement à sommets fixes qui pourrait représenter efficacement les contraintes supplémentaires imposées par les codes couplés (co-partitionnement). Nous avons donc développé un algorithme direct de partitionnement de graphe qui gère des sommets fixes. L'algorithme a été implémenté dans le partitionneur Scotch et une série d'expériences ont été menées sur la collection des graphes DIMACS. Ensuite nous avons proposé trois algorithmes de co-partitionnement qui respectent les contraintes issues des codes couplés respectifs. Nous avons egalement validé nos algorithmes par une étude expérimentale en comparant nos méthodes aux strategies actuelles sur des cas artificiels ainsi que sur des codes réels couplés<br>Load balancing is an important step conditioning the performance of parallel applications. The goal is to distribute roughly equal amounts of computational load across a number of processors, while minimising interprocessor communication. A common approach to model the problem is based on graph structures and graph partitioning algorithms. Moreover, new challenges involve the simulation of more complex physical phenomena, where different parts of the computational domain exhibit different physical behavior. Such simulations follow the paradigm of multi-physics or multi-scale modeling approaches. Combining such different models in massively parallel computations is still a challenge to reach high performance. Additionally, traditional load balancing algorithms are often inadequate, and more sophisticated solutions should be explored. In this thesis, we propose new graph partitioning algorithms that balance the load of such simulations, refered to as co-partitioning. We formulate this problem with the use of graph partitioning with initially fixed vertices which we believe represents efficiently the additional constraints of coupled simulations. We have therefore developed a direct algorithm for graph partitioning that manages successfully problems with fixed vertices. The algorithm is implemented inside Scotch partitioner and a series of experiments were carried out on the DIMACS graph collection. Moreover we proposed three copartitioning algorithms that respect the constraints of the respective coupled codes. We finally validated our algorithms by an experimental study comparing our methods with current strategies on artificial cases and on real-life coupled simulations
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Sun, Jiawen. "The GraphGrind framework : fast graph analytics on large shared-memory systems." Thesis, Queen's University Belfast, 2018. https://pure.qub.ac.uk/portal/en/theses/the-graphgrind-framework-fast-graph-analytics-on-large-sharedmemory-systems(e1eb006f-3a68-4d05-91fe-961d04b42694).html.

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As shared memory systems support terabyte-sized main memory, they provide an opportunity to perform efficient graph analytics on a single machine. Graph analytics is characterised by frequent synchronisation, which is addressed in part by shared memory systems. However, performance is limited by load imbalance and poor memory locality, which originate in the irregular structure of small-world graphs. This dissertation demonstrates how graph partitioning can be used to optimise (i) load balance, (ii) Non-Uniform Memory Access (NUMA) locality and (iii) temporal locality of graph partitioning in shared memory systems. The developed techniques are implemented in GraphGrind, a new shared memory graph analytics framework. At first, this dissertation shows that heuristic edge-balanced partitioning results in an imbalance in the number of vertices per partition. Thus, load imbalance exists between partitions, either for loops iterating over vertices, or for loops iterating over edges. To address this issue, this dissertation introduces a classification of algorithms to distinguish whether they algorithmically benefit from edge-balanced or vertex-balanced partitioning. This classification supports the adaptation of partitions to the characteristics of graph algorithms. Evaluation in GraphGrind, shows that this outperforms state-of-the-art graph analytics frameworks for shared memory including Ligra by 1.46x on average, and Polymer by 1.16x on average, using a variety of graph algorithms and datasets. Secondly, this dissertation demonstrates that increasing the number of graph partitions is effective to improve temporal locality due to smaller working sets. However, the increasing number of partitions results in vertex replication in some graph data structures. This dissertation resorts to using a graph layout that is immune to vertex replication and an automatic graph traversal algorithm that extends the previously established graph traversal heuristics to a 3-way graph layout choice is designed. This new algorithm furthermore depends upon the classification of graph algorithms introduced in the first part of the work. These techniques achieve an average speedup of 1.79x over Ligra and 1.42x over Polymer. Finally, this dissertation presents a graph ordering algorithm to challenge the widely accepted heuristic to balance the number of edges per partition and minimise edge or vertex cut. This algorithm balances the number of edges per partition as well as the number of unique destinations of those edges. It balances edges and vertices for graphs with a power-law degree distribution. Moreover, this dissertation shows that the performance of graph ordering depends upon the characteristics of graph analytics frameworks, such as NUMA-awareness. This graph ordering algorithm achieves an average speedup of 1.87x over Ligra and 1.51x over Polymer.
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Deveci, Mehmet. "Load-Balancing and Task Mapping for Exascale Systems." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429199721.

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Botadra, Harnish. "iC2mpi a platform for parallel execution of graph-structured iterative computations /." unrestricted, 2006. http://etd.gsu.edu/theses/available/etd-07252006-165725/.

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Thesis (M.S.)--Georgia State University, 2006.<br>Title from title screen. Sushil Prasad, committee chair. Electronic text (106 p. : charts) : digital, PDF file. Description based on contents viewed June 11, 2007. Includes bibliographical references. Includes bibliographical references (p. 61-53).
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Yildiz, Ali. "Resource-aware Load Balancing System With Artificial Neural Networks." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607613/index.pdf.

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As the distributed systems becomes popular, efficient load balancing systems taking better decisions must be designed. The most important reasons that necessitate load balancing in a distributed system are the heterogeneous hosts having different com- puting powers, external loads and the tasks running on different hosts but communi- cating with each other. In this thesis, a load balancing approach, called RALBANN, developed using graph partitioning and artificial neural networks (ANNs) is de- scribed. The aim of RALBANN is to integrate the successful load balancing deci- sions of graph partitioning algorithms with the efficient decision making mechanism of ANNs. The results showed that using ANNs to make efficient load balancing can be very beneficial. If trained enough, ANNs may load the balance as good as graph partitioning algorithms more efficiently.
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Zheng, Chunfang. "GRAPHICAL MODELING AND SIMULATION OF A HYBRID HETEROGENEOUS AND DYNAMIC SINGLE-CHIP MULTIPROCESSOR ARCHITECTURE." UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/249.

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A single-chip, hybrid, heterogeneous, and dynamic shared memory multiprocessor architecture is being developed which may be used for real-time and non-real-time applications. This architecture can execute any application described by a dataflow (process flow) graph of any topology; it can also dynamically reconfigure its structure at the node and processor architecture levels and reallocate its resources to maximize performance and to increase reliability and fault tolerance. Dynamic change in the architecture is triggered by changes in parameters such as application input data rates, process execution times, and process request rates. The architecture is a Hybrid Data/Command Driven Architecture (HDCA). It operates as a dataflow architecture, but at the process level rather than the instruction level. This thesis focuses on the development, testing and evaluation of a new graphic software (hdca) developed to first do a static resource allocation for the architecture to meet timing requirements of an application and then hdca simulates the architecture executing the application using statically assigned resources and parameters. While simulating the architecture executing an application, the software graphically and dynamically displays parameters and mechanisms important to the architectures operation and performance. The new graphical software is able to show system and node level dynamic capability of the HDCA. The newly developed software can model a fixed or varying input data rate. The model also allows fault tolerance analysis of the architecture.
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Gillet, Noel. "Optimisation de requêtes sur des données massives dans un environnement distribué." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0553/document.

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Les systèmes de stockage distribués sont massivement utilisés dans le contexte actuel des grandes masses de données. En plus de gérer le stockage de ces données, ces systèmes doivent répondre à une quantité toujours plus importante de requêtes émises par des clients distants afin d’effectuer de la fouille de données ou encore de la visualisation. Une problématique majeure dans ce contexte consiste à répartir efficacement les requêtes entre les différents noeuds qui composent ces systèmes afin de minimiser le temps de traitement des requêtes ( temps maximum et en moyenne d’une requête, temps total de traitement pour toutes les requêtes...). Dans cette thèse nous nous intéressons au problème d’allocation de requêtes dans un environnement distribué. On considère que les données sont répliquées et que les requêtes sont traitées par les noeuds stockant une copie de la donnée concernée. Dans un premier temps, des solutions algorithmiques quasi-optimales sont proposées lorsque les communications entre les différents noeuds du système se font de manière asynchrone. Le cas où certains noeuds du système peuvent être en panne est également considéré. Dans un deuxième temps, nous nous intéressons à l’impact de la réplication des données sur le traitement des requêtes. En particulier, un algorithme qui adapte la réplication des données en fonction de la demande est proposé. Cet algorithme couplé à nos algorithmes d’allocation permet de garantir une répartition des requêtes proche de l’idéal pour toute distribution de requêtes. Enfin, nous nous intéressons à l’impact de la réplication quand les requêtes arrivent en flux sur le système. Nous procédons à une évaluation expérimentale sur la base de données distribuées Apache Cassandra. Les expériences réalisées confirment l’intérêt de la réplication et de nos algorithmes d’allocation vis-à-vis des solutions présentes par défaut dans ce système<br>Distributed data store are massively used in the actual context of Big Data. In addition to provide data management features, those systems have to deal with an increasing amount of queries sent by distant users in order to process data mining or data visualization operations. One of the main challenge is to evenly distribute the workload of queries between the nodes which compose these system in order to minimize the treatment time. In this thesis, we tackle the problem of query allocation in a distributed environment. We consider that data are replicated and a query can be handle only by a node storing the concerning data. First, near-optimal algorithmic proposals are given when communications between nodes are asynchronous. We also consider that some nodes can be faulty. Second, we study more deeply the impact of data replication on the query treatement. Particularly, we present an algorithm which manage the data replication based on the demand on these data. Combined with our allocation algorithm, we guaranty a near-optimal allocation. Finally, we focus on the impact of data replication when queries are received as a stream by the system. We make an experimental evaluation using the distributed database Apache Cassandra. The experiments confirm the interest of our algorithmic proposals to improve the query treatement compared to the native allocation scheme in Cassandra
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Tbaileh, Ahmad Anan. "Robust Non-Matrix Based Power Flow Algorithm for Solving Integrated Transmission and Distribution Systems." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/89362.

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This work presents an alternative approach to power system computations, Graph Trace Analysis (GTA), and applies GTA to the power flow problem. A novel power flow algorithm is presented, where GTA traces are used to implement a modified Gauss-Seidel algorithm coupled with a continuation method. GTA is derived from the Generic Programming Paradigm of computer science. It uses topology iterators to move through components in a model and perform calculations. Two advantages that GTA brings are the separation of system equations from component equations and the ability to distribute calculations across processors. The implementation of KVL and KCL in GTA is described. The GTA based power flow algorithm is shown to solve IEEE standard transmission models, IEEE standard distribution models, and integrated transmission and distribution models (hybrid models) constructed from modifying IEEE standard models. The GTA power flow is shown to solve a set of robustness testing circuits, and solutions are compared with other power flow algorithms. This comparison illustrates convergence characteristics of different power flow algorithms in the presence of voltage stability concerns. It is also demonstrated that the GTA power flow solves integrated transmission and distribution system models. Advantages that GTA power flow bring are the ability to solve realistic, complex circuit models that pose problems to many traditional algorithms; the ability to solve circuits that are operating far from nominal conditions; and the ability to solve transmission and distribution networks together in the same model.<br>PHD
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Cheng, Danling. "Integrated System Model Reliability Evaluation and Prediction for Electrical Power Systems: Graph Trace Analysis Based Solutions." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28944.

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A new approach to the evaluation of the reliability of electrical systems is presented. In this approach a Graph Trace Analysis based approach is applied to integrated system models and reliability analysis. The analysis zones are extended from the traditional power system functional zones. The systems are modeled using containers with iterators, where the iterators manage graph edges and are used to process through the topology of the graph. The analysis provides a means of computationally handling dependent outages and cascading failures. The effects of adverse weather, time-varying loads, equipment age, installation environment, operation conditions are considered. Sequential Monte Carlo simulation is used to evaluate the reliability changes for different system configurations, including distributed generation and transmission lines. Historical weather records and loading are used to update the component failure rates on-the-fly. Simulation results are compared against historical reliability field measurements. Given a large and complex plant to operate, a real-time understanding of the networks and their situational reliability is important to operational decision support. This dissertation also introduces using an Integrated System Model in helping operators to minimize real-time problems. A real-time simulation architecture is described, which predicts where problems may occur, how serious they may be, and what is the possible root cause.<br>Ph. D.
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Books on the topic "Load graph"

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Graph out loud: A graphical guide to popular culture. Gotham Books, 2009.

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Graph out loud: A graphical guide to popular culture. Gotham Books, 2009.

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Graph out loud: A graphical guide to popular culture. Gotham Books, 2009.

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Graph out loud: A graphical guide to popular culture. Gotham Books, 2009.

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Krake, LR, N. Steele Scott, MA Rezaian, and RH Taylor. Graft-transmitted Diseases of Grapevines. CSIRO Publishing, 1999. http://dx.doi.org/10.1071/9780643101067.

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For almost 40 years, Australian researchers have been part of an international group of scientists who have studied graft-transmitted disorders of the grapevine. The Australian wine and grape industries are undergoing significant expansion as is the case in some other countries. Preventing the spread of pathogens, by producing clean propagation material, and minimising the disease load on new vines, is essential for the continuing success of the industry.&#x0D; This book covers the characteristics of each class of graft-transmitted pathogen, their effect on vines, how they spread and strategies for their control. Eleven of the most important diseases are illustrated and described comprehensively, including information about occurrence, symptoms, detection, transmission and effect on yield and quality. Finally there is a discussion of quarantine issues and disease management.&#x0D; This book will be an invaluable teaching tool and is intended for vineyard managers, grape growers, consultants, extension offers and students. While it provides a basic understanding of the nature of pathogens, it will aid in field assessment and identification of the often confusing disease symptoms.
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Turley, Glynda. Lily Pond Medium Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.

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Turley, Glynda. Lily Pond Large Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.

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Turley, Glynda. Lily Pond Extra Large Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.

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Book chapters on the topic "Load graph"

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Dey, Lakshmi Kanta, Debashis Ghosh, and Satya Bagchi. "Efficient Load Balancing Algorithm Using Complete Graph." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23223-7_83.

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Boillat, Jacques E. "Fast load balancing in Cayley graphs and in circuits." In Graph-Theoretic Concepts in Computer Science. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57899-4_62.

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Kiefer, Tim, Dirk Habich, and Wolfgang Lehner. "Penalized Graph Partitioning for Static and Dynamic Load Balancing." In Euro-Par 2016: Parallel Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43659-3_11.

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Aliev, Araz R., and Nigar T. Ismayilova. "Graph-Based Load Balancing Model for Exascale Computing Systems." In 11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92127-9_33.

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Wanka, Rolf. "Any Load-Balancing Regimen for Evolving Tree Computations on Circulant Graphs Is Asymptotically Optimal." In Graph-Theoretic Concepts in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36379-3_36.

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Busato, Federico, and Nicola Bombieri. "Efficient Load Balancing Techniques for Graph Traversal Applications on GPUs." In Euro-Par 2018: Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96983-1_45.

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Munasinghe, Kalyani, and Richard Wait. "Load Balancing by Changing the Graph Connectivity on Heterogeneous Clusters." In Advances in Grid Computing - EGC 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11508380_106.

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Márquez, Claudio, Eduardo César, and Joan Sorribes. "Graph-Based Automatic Dynamic Load Balancing for HPC Agent-Based Simulations." In Euro-Par 2015: Parallel Processing Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27308-2_33.

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Deng, Yiran, Yingjie Zhou, and Zhiyong Zhang. "Short-Long Correlation Based Graph Neural Networks for Residential Load Forecasting." In Neural Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92270-2_37.

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Zheng, Hang, Xu Ding, Yang Wang, and Chong Zhao. "Attention Based Spatial-Temporal Graph Convolutional Networks for RSU Communication Load Forecasting." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92635-9_7.

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Conference papers on the topic "Load graph"

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Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online load balancing under graph constraints." In the ACM SIGMETRICS/international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2465529.2465751.

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Sun, He, and Luca Zanetti. "Distributed Graph Clustering by Load Balancing." In SPAA '17: 29th ACM Symposium on Parallelism in Algorithms and Architectures. ACM, 2017. http://dx.doi.org/10.1145/3087556.3087569.

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Talukder, Nilothpal, and Mohammed J. Zaki. "Parallel graph mining with dynamic load balancing." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840995.

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Pandat, Ami, Nidhi Gupta, and Minal Bhise. "Load Balanced Semantic Aware Distributed RDF Graph." In IDEAS 2021: 25th International Database Engineering & Applications Symposium. ACM, 2021. http://dx.doi.org/10.1145/3472163.3472167.

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Kumar, Kriti, Rahul Sinha, M. Girish Chandra, and Naveen Kumar Thokala. "Data-driven electrical load disaggregation using graph signal processing." In 2016 IEEE Annual India Conference (INDICON). IEEE, 2016. http://dx.doi.org/10.1109/indicon.2016.7839109.

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Batreddy, Subbareddy, Kriti Kumar, and M. Girish Chandra. "On Using Graph Signal Processing for Electrical Load Disaggregation." In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2019. http://dx.doi.org/10.1109/hipcw.2019.00008.

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Belghaouti, Fethi, Amel Bouzeghoub, Zakia Kazi Aoul, and Raja Chiky. "Graph-oriented load-shedding for semantic Data Stream processing." In 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). IEEE, 2015. http://dx.doi.org/10.1109/iwcim.2015.7347064.

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Kumar, Amit, and Hemant Kumar Meena. "Non-intrusive load monitoring based on graph signal processing." In 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE). IEEE, 2017. http://dx.doi.org/10.1109/rdcape.2017.8358232.

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Kuo, Ming-Chia, Pangfeng Liu, and Jan-Jan Wu. "An Efficient Dynamic Load-Balancing Large Scale Graph-Processing System." In the 2018 VII International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3301326.3301343.

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Zhao, Bochao, Lina Stankovic, and Vladimir Stankovic. "Blind non-intrusive appliance load monitoring using graph-based signal processing." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418158.

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Reports on the topic "Load graph"

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Bhatele, Abhinav, Sebastien Fourestier, Harshitha Menon, Laxmikant V. Kale, and Francois Pellegrini. Applying graph partitioning methods in measurement-based dynamic load balancing. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1114706.

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Bhatele, A., S. Fourestier, H. Menon, L. Kale, and F. Pellegrini. Applying graph partitioning methods in measurement-based dynamic load balancing. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1093410.

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Financial Stability Report - Second Semester of 2020. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2020.

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The Colombian financial system has not suffered major structural disruptions during these months of deep economic contraction and has continued to carry out its basic functions as usual, thus facilitating the economy's response to extreme conditions. This is the result of the soundness of financial institutions at the beginning of the crisis, which was reflected in high liquidity and capital adequacy indicators as well as in the timely response of various authorities. Banco de la República lowered its policy interest rates 250 points to 1.75%, the lowest level since the creation of the new independent bank in 1991, and provided ample temporary and permanent liquidity in both pesos and foreign currency. The Office of the Financial Superintendent of Colombia, in turn, adopted prudential measures to facilitate changes in the conditions for loans in effect and temporary rules for rating and loan-loss provisions. Finally, the national government expanded the transfers as well as the guaranteed credit programs for the economy. The supply of real credit (i.e. discounting inflation) in the economy is 4% higher today than it was 12 months ago with especially marked growth in the housing (5.6%) and commercial (4.7%) loan portfolios (2.3% in consumer and -0.1% in microloans), but there have been significant changes over time. During the first few months of the quarantine, firms increased their demands for liquidity sharply while consumers reduced theirs. Since then, the growth of credit to firms has tended to slow down, while consumer and housing credit has grown. The financial system has responded satisfactorily to the changes in the respective demands of each group or sector and loans may grow at high rates in 2021 if GDP grows at rates close to 4.6% as the technical staff at the Bank expects; but the forecasts are highly uncertain. After the strict quarantine implemented by authorities in Colombia, the turmoil seen in March and early April, which was evident in the sudden reddening of macroeconomic variables on the risk heatmap in Graph A,[1] and the drop in crude oil and coal prices (note the high volatility registered in market risk for the region on Graph A) the local financial markets stabilized relatively quickly. Banco de la República’s credible and sustained policy response played a decisive role in this stabilization in terms of liquidity provision through a sharp expansion of repo operations (and changes in amounts, terms, counterparties, and eligible instruments), the purchases of public and private debt, and the reduction in bank reserve requirements. In this respect, there is now abundant aggregate liquidity and significant improvements in the liquidity position of investment funds. In this context, the main vulnerability factor for financial stability in the short term is still the high degree of uncertainty surrounding loan quality. First, the future trajectory of the number of people infected and deceased by the virus and the possible need for additional health measures is uncertain. For that reason, there is also uncertainty about the path for economic recovery in the short and medium term. Second, the degree to which the current shock will be reflected in loan quality once the risk materializes in banks’ financial statements is uncertain. For the time being, the credit risk heatmap (Graph B) indicates that non-performing and risky loans have not shown major deterioration, but past experience indicates that periods of sharp economic slowdown eventually tend to coincide with rises in non-performing loans: the calculations included in this report suggest that the impact of the recession on credit quality could be significant in the short term. This is particularly worrying since the profitability of credit establishments has been declining in recent months, and this could affect their ability to provide credit to the real sector of the economy. In order to adopt a forward-looking approach to this vulnerability, this Report presents several stress tests that evaluate the resilience of the liquidity and capital adequacy of credit institutions and investment funds in the event of a hypothetical scenario that seeks to simulate an extreme version of current macroeconomic conditions. The results suggest that even though there could be strong impacts on the credit institutions’ volume of credit and profitability under such scenarios, aggregate indicators of total and core capital adequacy will probably remain at levels that are above the regulatory limits over the horizon of a year. At the same time, the exercises highlight the high capacity of the system's liquidity to face adverse scenarios. In compliance with its constitutional objectives and in coordination with the financial system's security network, Banco de la República will continue to closely monitor the outlook for financial stability at this juncture and will make the decisions that are necessary to ensure the proper functioning of the economy, facilitate the flow of sufficient credit and liquidity resources, and further the smooth operation of the payment systems. Juan José Echavarría Governor
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