scholarly journals Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 329
Author(s):  
Zhigang Hu ◽  
Yong Tao ◽  
Meiguang Zheng ◽  
Chenglong Chang
Keyword(s):  
Clustering Algorithm ◽  
Task Characteristics ◽  
Processing Unit ◽  
Central Processing ◽  
Scheduling Policy ◽  
Load Imbalance ◽  
Usage Cost ◽  
Task Types ◽  
Load Size ◽  
Task Sequences

To address the issue of cloud mixed workloads scheduling which might lead to system load imbalance and efficiency degradation in cloud computing, a novel cloud task staggering peak scheduling policy based on the task types and the resource load status is proposed. First, based on different task characteristics, the task sequences submitted by the user are divided into queues of different types by the fuzzy clustering algorithm. Second, the Performance Counters (PMC) mechanism is introduced to dynamically monitor the load status of resource nodes and respectively sort the resources by the metrics of Central Processing Unit (CPU), memory, and input/output (I/O) load size, so as to reduce the candidate resources. Finally, the task sequences of specific type are scheduled for the corresponding light loaded resources, and the resources usage peak is staggered to achieve load balancing. The experimental results show that the proposed policy can balance loads and improve the system efficiency effectively and reduce the resource usage cost when the system is in the presence of mixed workloads.

Hybrid Partitioning-Density Algorithm for K-Means Clustering of Distributed Data Utilizing OPTICS

2019 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Mikołaj Markiewicz ◽  
Jakub Koperwas
Keyword(s):  
Clustering Algorithm ◽  
Additional Data ◽  
Processing Unit ◽  
Distributed Data ◽  
Local Models ◽  
Excellent Outcome ◽  
Central Processing ◽  
Clustering Model ◽  
Local Clustering ◽  
Fragmented Data

The authors present the first clustering algorithm for use with distributed data that is fast, reliable, and does not make any presumptions in terms of data distribution. The authors' algorithm constructs a global clustering model using small local models received from local clustering statistics. This approach outperforms the classical non-distributed approaches since it does not require downloading all of the data to the central processing unit. The authors' solution is a hybrid algorithm that uses the best partitioning and density-based approach. The proposed algorithm handles uneven data dispersion without a transfer overload of additional data. Experiments were carried out with large datasets and these showed that the proposed solution introduces no loss of quality compared to non-distributed approaches and can achieve even better results, approaching reference clustering. This is an excellent outcome, considering that the algorithm can only build a model from fragmented data where the communication cost between nodes is negligible.

Multilevel Flow-Based Markov Clustering for Design Structure Matrices

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
T. Wilschut ◽  
L. F. P. Etman ◽  
J. E. Rooda ◽  
I. J. B. F. Adan
Keyword(s):  
Clustering Algorithm ◽  
Central Processing Unit ◽  
System Structure ◽  
Underlying Structure ◽  
Design Structure Matrix ◽  
Processing Unit ◽  
Central Processing ◽  
Cpu Time ◽  
Design Structure ◽  
Markov Clustering

For decomposition and integration of systems, one needs extensive knowledge of system structure. A design structure matrix (DSM) model provides a simple, compact, and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering, that is able to cope with the presence of “bus” elements, returns multilevel clusters, is capable of clustering weighted, directed, and undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters. Comparison with two algorithms from the literature shows that the proposed algorithm provides clusterings of similar quality at the expense of less central processing unit (CPU) time.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

PERANGKAT KERAS ( HARDWARE ) PADA KOMPUTER

2020 ◽  
Author(s):  
Siti Kumala Dewi
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device ◽  
System P ◽  
System 2

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Berdasarkan fungsinya, perangkat keras terbagi menjadi :1.Sistem Perangkat Keras Masukan (Input Device System )2.Sistem Pemrosesan ( Central Processing System/ Central Processing Unit(CPU)3.Sistem Perangkat Keras Keluaran ( Output Device System )4.Sistem Perangkat Keras Tambahan (Peripheral/Accessories Device System)

Assessing pragmatic competence in oral proficiency interviews at the C1 level with the new CEFR descriptors

2020 ◽  
Vol 16 (1) ◽  
pp. 87-121
Author(s):  
Bárbara Eizaga-Rebollar ◽  
Cristina Heras-Ramírez
Keyword(s):  
Rating Scales ◽  
Rating Scale ◽  
Oral Proficiency ◽  
Pragmatic Competence ◽  
Task Characteristics ◽  
The Common ◽  
Task Descriptions ◽  
Oral Proficiency Interviews ◽  
Task Types ◽  
And Task

AbstractThe study of pragmatic competence has gained increasing importance within second language assessment over the last three decades. However, its study in L2 language testing is still scarce. The aim of this paper is to research the extent to which pragmatic competence as defined by the Common European Framework of Reference for Languages (CEFR) has been accommodated in the task descriptions and rating scales of two of the most popular Oral Proficiency Interviews (OPIs) at a C1 level: Cambridge’s Certificate in Advanced English (CAE) and Trinity’s Integrated Skills in English (ISE) III. To carry out this research, OPI tests are first defined, highlighting their differences from L2 pragmatic tests. After pragmatic competence in the CEFR is examined, focusing on the updates in the new descriptors, CAE and ISE III formats, structure and task characteristics are compared, showing that, while the formats and some characteristics are found to differ, the structures and task types are comparable. Finally, we systematically analyse CEFR pragmatic competence in the task skills and rating scale descriptors of both OPIs. The findings show that the task descriptions incorporate mostly aspects of discourse and design competence. Additionally, we find that each OPI is seen to prioritise different aspects of pragmatic competence within their rating scale, with CAE focusing mostly on discourse competence and fluency, and ISE III on functional competence. Our study shows that the tests fail to fully accommodate all aspects of pragmatic competence in the task skills and rating scales, although the aspects they do incorporate follow the CEFR descriptors on pragmatic competence. It also reveals a mismatch between the task competences being tested and the rating scale. To conclude, some research lines are proposed.

scholarly journals Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

2020 ◽  
Author(s):  
Wisoot Sanhan ◽  
Kambiz Vafai ◽  
Niti Kammuang-Lue ◽  
Pradit Terdtoon ◽  
Phrut Sakulchangsatjatai
Keyword(s):  
Experimental Data ◽  
Heat Pipe ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Sources ◽  
Final Thickness ◽  
Laptop Computers ◽  
Central Processing ◽  
Graphics Processing ◽  
Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

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