K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems

You, Xindong; Sun, Tianxiang; Sun, Dawei; Liu, Xunyun; Lv, Xueqiang; Buyya, Rajkumar

doi:10.1002/cpe.6096

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…SEA [11] Frequency -Statistic GreenHDFS [19] Frequency -Statistic Lighting [20] Frequency -Statistic Liao et al [21] Frequency -Statistic Zhang et al [22] Frequency -Statistic K-ear [23] Frequency √ K-means CSEA [4] Frequency √ K-means Our work…”

Section: Criterion Seasonal Feature Data Classification Algorithmmentioning

confidence: 96%

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Text Semantics-Driven Data Classification Storage Optimization

Yuan,

Lv,

Gong

et al. 2024

Applied Sciences

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Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt a dual-layer strategy based on semantic similarity to extract seasonal features. Second, we put forward a cost-effective data classification storage framework based on text seasonal features. We compare our work with the data classification approach AS-H, which runs at full high performance. In addition, we also compare it with K-ear, which adopts K-means as the classification algorithm. The experimental results show that compared with AS-H and K-ear, our method reduces energy consumption by 9.51–13.35% and operating costs by 13.20–22.17%.

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Section: Criterion Seasonal Feature Data Classification Algorithmmentioning

confidence: 96%

“…While the approach shows energy savings under specific conditions, its effectiveness may diminish in dynamic environments. K-ear [23] categorizes data into multiple groups by extracting seasonal period characteristics using the K-means clustering algorithm. These categories are then stored in various regions within a cloud storage system.…”

Section: Related Workmentioning

confidence: 99%

Text Semantics-Driven Data Classification Storage Optimization

Yuan,

Lv,

Gong

et al. 2024

Applied Sciences

Self Cite

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“…Clustering was the first phase of their processing. The k-means-based algorithm, k-ear, was developed to analyze the energy needs related to the seasonal access characteristics for data management systems in [33]. The supporting role of k-means in the process of data preparation for the artificial neural network was used in [34] to predict traffic flow patterns.…”

Section: Introductionmentioning

confidence: 99%

Using K-Means Clustering in Python with Periodic Boundary Conditions

2022

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Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

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