dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on the data migration approach, Ursa implement power management approach in which they use a threshold strategy to maintain the amount of nodes to save energy. Similarly machine virtualization-based technology that consolidating VMs dynamically and turning off idle servers has been proved effective also within its domain. Han et al. [19]  proposed a remaining utilization-aware (RUA) algorithm for virtual machine placement, and a power-aware algorithm to find proper hosts to shut down for energy saving. Savinov and Daudjee et al. [20]  proposed virtualizationdriven database provisioning system, Dolly. Dolly took advantage of a new database replica spawning technique that leverages virtual machine cloning in which Dolly makes more replicas when the system is overloaded, and reduces the amount of replicas otherwise.
Chen et al. [21]  proposed scheduling approach for real time tasks within virtualization environment based on interval number theory, in which they also proposed scale functions to switch nodes on and off according to the workload. Above works achieved energy efficiency in their domains. However, such approaches naturally require to migrate a large number of data from one node to another in order to switch off underutilized nodes or switch on more nodes. In WattDB  [8] , Schall and Härder took advantage of SDD to reduce the migration cost. In Usra  [7] , Gae-Won You et al. took advantage of replica strategy to shutdown nodes which do not contains any primary replicas. However, in cloud database systems, this technique might not suitable since with different implement of system, they have different consistency strategy.
For example, Cassandra provides 5 consistency levels, and HBase provides strong consistency only. In Guangjie Han et al. 's work  [19] , their virtual machine consolidation policy aims to improve resource utilization and reduce the number of active physical servers, therefore they did not consider the migration cost. In Dolly  [20] , Emmanuel Cecchet et al. only considered the down time of the database but not the migration cost. Meanwhile, they only applied the technique for a web application's database layer in which 12 GB amount of data was used in the extreme case of the experiments. In Huangke Chen et al. 's work  [21] , they scheduled real-time tasks to the virtual machines. When there are overloaded nodes, they scale up the computing resource, namely turn up more virtual machines, and otherwise they reduce the amount of virtual machines.
B. SPECIALIZED MODEL FOR ENERGY AWARE CLOUD DATABASE In this section, the generic model is specialized for cloud database systems. The workload w i , the capacity measurement function z(c i , f i ), the migration function m, the power consumption estimation function p(s) and the migration cost estimation function mc( s) must therefore be identified. In a cloud database system, the dataset D consists of h data blocks, in which the size of block b g is denoted as b g , and the blocks are distributed within the cluster. In order to meet data integrity and fault-tolerance requirements, cloud databases use a replication factor to control the number of replicas of the data blocks. The dataset with a replication factor r is denoted as D r = {b 11 , b 12. , b 1r. b h1 , b h2. b hr }, in which b gk ∈ D r , k ≤ r is the k th replica of b g.
The workload w i of a cloud database system is defined as data query throughput. The probability of b gk being accessed is denoted as ϕ gk , and the total throughput of the system is denoted as l. For all the blocks h g r k ϕ gk = 1. ϕ gk and l are prediction values, which can be given by data mining techniques and machine learning techniques, such as time series data mining and linear regression. For the corresponding techniques, we refer readers to the literature  [24]. Let the block set assigned to c i be D r i = {b gk | b gk ∈ D r and b gk is assigned to c i }. The workload w i is defined by: w i = b gk ∈D r i l × ϕ gk. (3) The capacity measurement function z(c i , f i ) is a discrete function. Using benchmarks, the maximum throughput of a cloud database under each frequency can be obtained (see Section V-A1).
The frequency option set is denoted as η. A frequency f is one of the available frequency options. Let the idle power consumption and the maximum power consumption of a node under a frequency f be c idle f and c max f respectively. If ∀f p , f q ∈ η and f p > f q , c idle f p > c idle f q and c max f p > c max f q. With a higher frequency, the system provides more resources to support workloads, but consumes more energy. If a fraction ψ of CPU is used under the frequency f , the power consumption estimation function is defined by Equation (  4 ). In cloud database systems, ψ i is defined by Equation (  5 ), in which w * i is the workload after the migration, hence ψ i 1. p(s * ) = n i c idle f i + ψ i × c max f i -c idle f i , (4) ψ i = w * i z(c i , f i ). (5) The migration process refers to the migration of data blocks.
Let M In and M Out denote the block sets migrated within a rack and between racks, respectively. Let e In and e Out denote the energy costs per mega byte of migration within a rack and between racks respectively, which are obtained through the benchmark experiment (see Section V-A2). The migration cost estimation function mc( s) is defined by: mc( s) = e In × b gk ∈M In b gk + e Out × b gk ∈M Out b gk. (6) minimized according to Equation  (2). In could database systems, energy consumption is estimated according to Equation (1) in which the power consumption and the migration cost are given by Equation (  4 ) and Equation (  6 ) respectively. Thus, energy consumption of each time window comes from two parts: energy for running the workload and energy for performing migration. According to the previous sections, the model of frequency selection approach for cloud database systems is shown as follow: minimize n i=1 c idle f i + w * i z(c i , f i ) × c max f i -c idle f i + e In × b gk ∈M In b gk +e Out × b gk ∈M Out b gk.
A. MODEL SIMPLIFICATION The power consumption and the migration cost can be estimated by Equation (  4 ) and  (6). However, both values are obtained after the migration process. Using the greedy algorithm and MTHM algorithm, a migration plan can be obtained within polynomial time. However, when the total amount of possible frequency vectors increases, the evaluation time becomes unacceptable. The model simplification obtains upper bounds of the power consumption and the migration By means of redefining the key conceptions and key functions of the generic model, the model is specialized for cloud database systems to cope with the resource provisioning problem. This subsection is an example to extend the generic model to fit to a cloud system.
Using the model simplification, the performance for evaluating frequency vectors is improved. To verify the improvement, a frequency evaluation process with model simplification and a frequency evaluation process with migration process are applied to evaluate 1000 frequency vectors for dataset d10, d20 and d30 (see Section V-B) respectively and the execution time for each case is shown in Table  (1). The execution time of evaluating frequency vectors for d10, d20 and d30 are reduced 97. 7%, 98. 4% and 99. 1% respectively. In order to avoid the impact of this simplification on the frequency selection algorithms, the corresponding algorithms are executed multiple times to obtain several frequency vector candidates, and the one with the minimum energy consumption is chosen as the final solution.
V. EXPERIMENT In this section, a series test cases are designed and executed to verify the model and the corresponding algorithms. There are 5 objectives for the experiment: 1) Verify the exist of maximum throughout in cloud database system and obtain the corresponding static parameters; 2) Analyze the accuracy and performance of the algorithms; 3) Analyze the scalability of the algorithms; 4) Analyze the optimization bound of the approach; 5) Compare our approach with the exist approach. Therefore the experiments consist of 5 parts: setup bench-.
1) CAPACITY BENCHMARK This benchmark is executed on Grid5000  [29]  testbed. Grid5000 is designed to provide a scientific tool for computer scientists similar to the large-scale instruments used by physicists, astronomers and biologists. In the benchmark, a database system Cassandra  [4]  with 10 nodes belonging to the Nancy site graphene cluster is deployed. The core properties used in YCSB workload profile are shown in Table  (2). To be noticed that, the cache related parameters are set to 0 to avoid the influence of cache mechanism to the experiment results. The nodes are equipped with a 4 cores Intel Xeon X3440 and 16 GB of RAM. The energy consumption values are collected by Power Distribution Units (PDU). There are 8 available frequency options: 2. 53GHz, 2. 40GHz, 2. 13GHz, 2. 00GHz, 1.
B. PARAMETER INFLUENCE In this section, the influence of parameters on the algorithms is examined. The datasets are denoted as d{NodeAmount}. For example, d10 represents a dataset consisting of 10 nodes. As the number of blocks on each node does not impact the performance of the frequency selection algorithm, the number of blocks is set to 64 for each node, and the access probabilities are generated by Zipf's law (distribution factor is set to 2. 5). A test case is a combination of a dataset(d), a workload(l) and an algorithm(a). For example (d10, 5000, GA) indicates a test case, in which GA is applied to dataset d10 and the workload is set to 5000 opt/sec for each node. The value of throughput per node is a standard to simulate the total workload for the cases, and the throughput for each node is decided by ϕ gk.
C. SCALABILITY ANALYSIS In this section, there are 12 datasets (d10 to d120) involved at first. Only one candidate is required in each case. For GA, generation size is set to 150 and population size is set to 100. The result is shown in Figure  (7). Generally, T (d, GA) > T (d, MCTS). When the dataset is smaller, the difference is more dramatic. For example, T (d10, GA) is nearly 19 times T (d10, MCTS). However, the growth rate of the execution time of GA is lower than MCTS. For example, T (d120, GA) is 6 times T (d10, GA) while T (d120, MCTS) is 103 times T (d10, MCTS). In GA, the increasing amount of nodes leads to the longer length of chromosome. When generation size and population size are constant, the length of chromosome only influences the performance in each evaluation. It leads to linear increment.
However, in MCTS, when the amount of node is increased by 1, the height of the tree is increased by 1 which leads to exponential growth of the leaf nodes. The search of solutions is an exponential function, which makes the execution time grows exponentially. In order to examine the scalability of our algorithms, an extreme scalability analysis is done. In this expeimrent, 10 huge datasets, (d100 to d1000) is used. Only one candidate is required in each case, and the configuration of the algorithms keeps same. The result is shown in Figure  (8). At beginning, GA solve d100 around 7. 05s, while MCTS find a solution around 5. 15s. With the increment of the problem, both algorithms needs more time for finding a solution. more evolutions, which improves A(d, GA). However afterwards the search process is close enough to an optimal point and the iterations keep the solution around the optimal point.
2) THE INFLUENCE OF POPULATION SIZE ON GA The result is shown in Figure  (5). In each case, generation size is set to 150 and the amount of candidates is set to 10. In Figure  (5) , with the same population size, ∀i > j, T (di, GA) > T (dj, GA), because more chromosomes are evaluated in one iteration. Increasing population size improves A(d, GA) at the beginning. However, at some points (80 for d10, 160 for d20 and 320 for d30), the increment of population size does not improve the accuracy any more. For d1000, GA consumes 73. 97s, while MCTS consumes 600. 90s. The execution time of GA is acceptable considering the length of time window. As the conclusion from Figure  (7)  and Figure  (8) , GA is more suitable with huge datasets, since the execution time increment of GA is liner relationship with the amount of nodes, while it is exponential for MCTS.
D. OPTIMIZATION BOUNDARY ANALYSIS In this section, optimization ratio is introduced to evaluate how much energy can be saved using the frequency selection approach. The optimization ratio is defined by Equation  (18)  in which Performance refers to the approach that all nodes are set to the performance mode (i. e. , the maximum frequency). The optimization ratio indicates the ratio between the saved energy by frequency selection approach and the energy consumption under performance mode. In this section, each case is solved by GA. In GA, generation size is set to 150, population size is set to 100, and the amount of candidates is set to 10. There are 12 datasets involves (d10 to d120), and the cases are divided into 4 categories based on their throughputs per each node. The throughputs for each node are 3500 opt/sec, 4000 opt/sec, 3500 opt/sec and 5000 opt/sec.
According to equation  (18) , the maximum optimization ratio is calculated as equation  (19). 26. 43% is the optimization bound of the model theoretically. The higher optimization ratio bound could be obtained by means of decreasing workload. However when the workload is too low, the value is meaningless, because the cluster is fully under-utilized. In the real cases, the optimization ratio might be lower than  26  Chihoub et al. [9]  proposed a reconfiguration approach called Hot-N-Cold for Cassandra System to demonstrate the impact on energy consumption with strong and eventual consistency, in which half of the nodes are set to highest frequency and another half of nodes are set to lowest frequency. The comparison between Hot-N-Cold, GA and MCTS is made. The dataset used in this section is For the cases, the throughput per node is set to 3500 opt/sec, 4000 opt/sec, 4500 opt/sec and 5000 opt/sec respectively.
VI. ALGORITHM ROBUSTNESS ANALYSIS In previous sections, the frequency selection model and corresponding algorithms are based on the predictions of the workload. Because the prediction errors are inevitable, the robustness of the algorithms are analyzed in this section. To analyze the robustness of algorithms, datasets d10, d20 and d30 (Section V-B) are used in this section. In the specialized model, the block access possibility ϕ gk is the key to define the workload for cloud database systems. At first the prediction errors extracted from the normalize distribution N (µ, σ 2 ). In each case, µ is set to 0 to make sure half of errors are negative and other half of errors are positive. The range of values extracted from the normalize distribution is within [µ + 3σ, µ -3σ ]. To make sure the ranges of errors are the same with corresponding case, and the σ is set to ϕφ in which φ is the value from [0, 0.
Meanwhile, we only considered using DVFS to solve the resource provisioning for energy aware cloud database systems. In the future work, the other resources can be considered, for example I/O resource can be introduced to further improve the energy efficiency of the system In general, the model's static parameters which are related to the node's architecture should be obtained according to the different architectures. Workload prediction errors could cause SLA violation and energy wasting for both algorithms. In order to reduce impact of prediction errors, the capability tolerance factor is introduced to tune the node capability. According to the experiments, the maximum error ratio can be reduced by means of a negative capability tolerance factor for both algorithms, and the maximum waste ratio can be reduced by means of a positive capability tolerance factor.
CHAOPENG GUO received the B. S. degree in software engineering from Northeastern University, China, in 2013. He is currently pursuing the Ph. D. degree with University of Toulouse. His current research focuses on energy efficiency and energy aware cloud database. JEAN-MARC PIERSON received the Ph. D. degree in computer science from the LIP Laboratory, École Normale Supérieure de Lyon. He has been a Full Professor with University of Toulouse, since 2006. His research interesting includes distributed systems and high-performance computing, distributed data management, security grid, cloud computing, and energy-aware distributed computing. HUI LIU received the Ph. D. degree from Northeastern University, Shenyang, China, in 2010. She is currently a Lecture with the School of Metallurgy, Northeastern University.
In the current case, we have opted for the R-Igraph and Python-Networkx packages. In Fig. 9 , we see the degree distribution, P (k), of the typical networks emerging from different gEQW tend to be very unequal and left-skewed for small q and more uniformly distributed for high q. This pattern is well captured by both the Shannon entropy and the skewness of P (k) where the first increases as the memory range parameter q increases and the second become less negative over time (see Fig. 10 ). We have seen the mean degree and standard deviation of P (k) increases with q, because of small q act as an effective barrier for long-range hopping events. In Fig. 11 , we show how the main structural properties of the QW network evolve over time for increasing q. Both the number of links n L and vertices n V increases, with different concavity though.
Di�fering from big-data research programs, the emphasis in the lab approach is not in the magnitude of the data analysed, but in the critical a�fordances deployed by their acquisition protocol. The methods try to derive significant findings from relatively small, ad-hoc designed, data-sets (Marres & Weltevrede, 2013). Being a challenge of data selection and curation, they provide, through the redaction of their protocols, an evident and traceable inspection of the qualitative decisions taken to compose datasets and corpora. 12 The notion of 'technique' diverts from any 'magical properties' (Gad & Jensen, 2014) of reproducibility and self-explanation that the notion of 'methods' risks carrying. Take as example the growing number of boiled down, easyfied instructional materials meant to di�fuse and reproduce design methods.
11 Di�fering from big-data research programs, the emphasis in the lab approach is not in the magnitude of the data analysed, but in the critical a�fordances deployed by their acquisition protocol. The methods try to derive significant findings from relatively small, ad-hoc designed, data-sets (Marres & Weltevrede, 2013). Being a challenge of data selection and curation, they provide, through the redaction of their protocols, an evident and traceable inspection of the qualitative decisions taken to compose datasets and corpora. 12 The notion of 'technique' diverts from any 'magical properties' (Gad & Jensen, 2014) of reproducibility and self-explanation that the notion of 'methods' risks carrying. Take as example the growing number of boiled down, easyfied instructional materials meant to di�fuse and reproduce design methods.
16 For a more detailed de- scription of the project and its design implication, see: Ricci, de Mourat, Leclercq, & Latour, 2015. 17 Through a custom and open-source so�tware, available at https://github. com/medialab/catwalk, every tweet was read by the research team and evaluated in terms of its pertinence. The close reading of the tweets en- abled it to have a constant overview of the state of the discussion, gaining a deep understanding of the dynamics of the issue. For a detailed account of the project methodological challenges and biases, see: Ricci, Colombo, Meunier, & Brilli, 2017. 16 textuales y visuales. Para una descripción más detallada del proyecto y la Podemos encontrar un ter- implicación del diseño, ver: cer ejemplo en una instancia del Ricci, de Mourat, Leclercq, & Latour, 2015.
proyecto ALGOGLITCH que se 17 A través de un so�tware centra en la algoritmización de la personalizado y de código sociedad y que aborda la siguien- abierto, disponible en https://github. com/ te pregunta: «¿Cómo queremos medialab/catwalk, el equipo de investigación leyó cada ser calculados?» En el marco de uno de los tweets y evaluó una colaboración con el Programa su pertinencia. Esta lectura detallada de los tweets per- Experimental en Arte Político de mitió al equipo supervisar constantemente el estado del SciencesPo (SPEAP), un grupo de debate y obtener una pro- estudiantes diseñó un protoco- funda comprensión de las dinámicas del asunto. Para lo de investigación destinado a una descripción detallada de los desafíos metodológicos recopilar un corpus de objetos y de este proyecto, ver: Ricci, narraciones relacionadas con los Colombo, Meunier, & Brilli, 2017.
In this simple description, the knowledge base comprises a very large number of entities and documents with numerous direct document-entity relationships, and much more derived entityentity co-occurrence relationships e. g. person-person or personlocation relationships. In Information Visualization terms, this is a case of large and dense graph with heterogeneous nodes and various types of links, as well as attributes associated with the nodes, e. g. a person's dates of birth and death, which may be described as a multilayer multivariate time-dependent graph often referred to as multilayer graphs  [11]. While exploring and navigating through large information spaces is the main raison d'être of information visualization, large and dense network datasets are known to defeat well-established approaches such as the visual information seeking mantra  [13]  ("overview first, zoom and filter, then details on demand").
This is partly due to the fact that popular graph layouts (e. g. node-link) result in unwieldy overviews due to edge clutter, which precludes any further exploration  [6, 7]. More importantly, the top-down approach assumed by this mantra contradicts the analytical methodology adopted in some fields (e. g. by historians), where the scope of investigation starts from a detail and expands gradually to include more and more elements. The Visual Analytics mantra  [10]  ("Analyze first, show the important, zoom, filter and analyze further, details on demand") introduces an automated analysis step at the onset of the analytical pipeline in order to qualify what is important to the user. This is useful when providing an exhaustive overview becomes infeasible due to the sheer size of the dataset (computing power) or to cognitive overload.
Code blocks farther away are then summarized as a single line or function header. Furnas' work consists in specifying a good notion of "distance" to decide what to display, in detail or in summary form. This notion of distance can be expressed as a score for estimating node interest in different domains such as ontologies  [9]  or trees  [2]. Van Ham and van Wijk used this approach for navigation purposes in a tree describing a hierarchical ascending partitioning of the nodes of a graph (clustering)  [8]. Van Ham and Perer proposed a generalization of the score to any graph, adding a semantic dimension  [14]. Interest is determined from structural information of the graph (degree, centrality, etc. ) and information relative to node attributes (keywords, tags, etc. ). M-QuBE 3 generalizes and aggregates previous work by leveraging multilayer graphs, more faithfully capturing the complexity of the data than traditional single-layer graphs.