Scheduling for data centers with multi-level data locality

Daghighi, Amirali; Kavousi, Mohammadamir

doi:10.1109/iraniancee.2017.7985172

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…This is especially the case for colocation datacenters, where the business model is such that tenants have the full control of their workloads and tasks, as the datacenter operator just leases the infrastructure and space for computing. Furthermore, the model is also employed to profit from data locality 30 and to provide a proper load balancing 31 and network awareness. 32 The two-level planning approach proposed in this article is described next.…”

Section: Planning Approachmentioning

confidence: 99%

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Muraña

Nesmachnow

2021

SIMULATION

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This article presents the evaluation of multicriteria planning heuristics for demand response in datacenters and supercomputing facilities. This is a relevant problem for science nowadays, when the growing application of cutting-edge technologies (numerical methods, big data processing, artificial intelligence, smart systems, etc.) has raised the energy demands in datacenters. The proposed approach involves a negotiation mechanism for colocation datacenters, where the datacenter operator agrees prices and quality of service with a group of tenants. Twelve different multicriteria heuristics are proposed for planning using both local and global information at tenants and datacenter operator levels. The proposed approach is evaluated applying simulations over realistic scenarios considering different tenant sizes and heterogeneity levels that model different business models for datacenters. Several metrics are computed and Pareto analysis is provided. The main results indicate that accurate trade-off values between the problem objectives are obtained, offering different options for decision making. The proposed approach provides a useful and applicable method for demand response planning in modern datacenters.

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Section: Planning Approachmentioning

confidence: 99%

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Muraña

Nesmachnow

2021

SIMULATION

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“…Using the insights of this paper, for data parallel processing of big data, more sophisticated algorithms based on MapReduce can be used for speeding up the processing time, e.g. look at [36,39,5,40,19].…”

Section: Related Workmentioning

confidence: 99%

Estimating the Rating of the Reviews Based on the Text

Kavousi

Saadatmand

2018

Data Analytics and Learning

Self Cite

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User-generated texts such as reviews and social media are valuable sources of information. Online reviews are important assets for users to buy a product, see a movie, or make a decision. Therefore, rating of a review is one of the reliable factors for all users to read and trust the reviews. This paper analyzes the texts of the reviews to evaluate and predict the ratings. Moreover, we study the effect of lexical features generated from text as well as sentimental words on the accuracy of rating prediction. Our analysis show that words with high information gain score are more efficient compared to words with high TF-IDF value. In addition, we explore the best number of features for predicting the ratings of the reviews.

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“…However, in the proposed method of generating the deterministic matrix, before data compression, matrix Φ m * n is determined for a pair of sender and receiver and there is no need to send or store matrix Φ m * n . Note that the generation of the deterministic matrix Φ m * n can be made fast by utilizing parallel processing of data and using sophisticated MapReduce algorithms, e.g [66,67,68,69,70,71,72,73].…”

Section: Production Of Stochastic Matrix φ M * Nmentioning

confidence: 99%

Fast Fourier-Based Generation of the Compression Matrix for Deterministic Compressed Sensing

Jajimi

2018

Preprint

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The primary goal of this work is to review the importance of data compression and present a fast Fourier-based method for generating the deterministic compression matrix in the area of deterministic compressed sensing. The principle concepts of data compression such as general process of data compression, sparse signals, coherence matrix and Restricted Isometry Property (RIP) have been defined. We have introduced two methods of sparse data compression. The first method is formed by utilizing a stochastic matrix which is a common approach, and the second method is created by utilizing a deterministic matrix which is proposed more recently. The main goal of this work is to improve the execution time of the deterministic matrix generation. The execution time is related to the generation method of the deterministic matrix. Furthermore, we have implemented a software which makes it possible to compare different methods of reconstructing data compression. To make this comparison, it is necessary to draw and compare certain graphs, e.g. phase transition, the ratio of output signal to noise and input signal to noise, signal to noise output and also the ratio of percentage of accurate reconstructing and order of sparse signals for various reconstructing methods. To facilitate this process, the user would be able to draw his/her favorite graphs in GUI environment.

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Scheduling for data centers with multi-level data locality

Cited by 6 publications

References 14 publications

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Estimating the Rating of the Reviews Based on the Text

Fast Fourier-Based Generation of the Compression Matrix for Deterministic Compressed Sensing

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