Search

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%. [ABSTRACT FROM AUTHOR]

Caused by massive data, large-scale data centers suffer from insufficient storage capacity. This increases the risk of component damage and disk failure. To enhance storage capacity of data centers, scaling has turned out to be one of the most popular choices. RAID-6 is deployed extensively that has superior reliability, availability, and scalability compared to other RAID levels. However, higher reliability also means expensive parity data update cost. To address this issue, this paper proposes an efficient RAID-6 scaling scheme, HS6, for H-Code. The basic idea of HS6 is reducing the total parity data update cost and saving the total scaling time by optimizing data migration and parity data update. The properties of HS6 can be summarized as follows: (1) it utilizes horizontal data migration to eliminate the horizontal parity data update, and (2) it makes full use of original parity data to cut down the total parity data update cost. Numerical results and experimental data analysis indicate that: (1) HS6 decreases the data migration rate by 76.92% ∼ 95.31%, 55.56% ∼ 84.46%, and 14.29% ∼ 16.67% compared to Round-Robin, Semi-RR, and HCS, (2) HS6 saves the total scaling time by 62.05% ∼ 76.75%, 53.24% ∼ 73.94%, and 3.68% ∼ 6.79% against Round-Robin, Semi-RR, and HCS, respectively, under offline and outperforms Round-Robin by 59.99% ∼ 79.02%, Semi-RR by 53.04% ∼ 76.41%, and HCS by 0.56% ∼ 23.78% in total scaling time under four workloads, and (3) HS6 maintains almost the same user average response time with Round-Robin, Semi-RR, and HCS during scaling and after scaling. [ABSTRACT FROM AUTHOR]

Thanks to excellent reliability, availability, flexibility and scalability, r edundant a rrays of i ndependent (or i nexpensive) d isks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces |$\tau $| to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling. [ABSTRACT FROM AUTHOR]

Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy‐aware data clustering through a machine learning algorithm (K‐ear) is proposed. K‐ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three tidal categories according to its tidal access characteristics. The 15 classified categories are stored in different storage zones with different energy and performance modes. Simulation experiments using CloudSimDisk with the constructed mathematic models demonstrate that the proposed K‐ear algorithm is more energy‐efficient than the default data clustering algorithms in Hadoop and the classical data clustering storage strategy according to the data access frequency (Striping‐Based Energy‐Aware Strategy). [ABSTRACT FROM AUTHOR]