A Maximum Cache Value Policy in Hybrid Memory-Based Edge Computing for Mobile Devices

Jia, Gangyong; Han, Guangjie; Du, Jian Hua; Chan, Sammy

doi:10.1109/jiot.2018.2878872

Cited by 23 publications

(10 citation statements)

References 27 publications

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“…Over the past few years, a large number of research works has investigated cloud computing [18][19][20], fog&edge computing [21][22][23][24][25][26][27][28][29], and content caching [30][31][32]. However, most of these works involved these three technologies separately.…”

Section: Related Workmentioning

confidence: 99%

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Fan

Zheng

Jiang

et al. 2020

Sensors

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This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

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Section: Related Workmentioning

confidence: 99%

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Fan

Zheng

Jiang

et al. 2020

Sensors

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“…Testbed results verified the energy efficiency and adaptability of this approach However, the storage, processing and network features have not been considered in the current work. There are some related work can be used as references [124]- [134], which are not described in our body text.…”

Section: Energy Savings Strategies Utilizing Data Replication Techmentioning

confidence: 99%

A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

You

Zhen

et al. 2020

IEEE Access

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During the past ten years, the energy consumption problem in cloud-related environments has attracted substantial attention in research and industrial communities. Researchers have conducted many surveys on energy efficiency issues from different perspectives. All of the surveys can be classified into five categories: surveys on the energy efficiency of the whole cloud related system, surveys on the energy efficiency of a certain level or component of the cloud, surveys on all of the energy efficient strategies, surveys on a certain energy efficiency techniques, and other energy efficiency related surveys. However, to the best of our knowledge, surveys on energy-aware data management strategies in cloud-related environment are absent. In this paper, we conduct a comprehensive survey on energy saving-aware data management strategies in cloud-related environments, such as data classification, data placement and data replication strategies. Compared to current existing reviews on energy efficiency in cloud-related environments, we firstly conduct the survey on the energy consumption problem from the data management perspective. Furthermore, we classify the energy-aware data management strategies from different perspectives. This survey and the taxonomy of the energy-aware data management strategies demonstrate the potential for reducing the energy consumption at the data management level of a cloud storage system, which will compress more space for energy reduction and finally achieve energy proportionality. Moreover, this survey and taxonomy on the energy efficiency issue from the data management perspective is an important supplement to current existing surveys on energy efficiency in cloud-related environments.

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“…The operating system level includes applications for different scenarios, file systems for file management, and block layers for blockdevice abstraction. At the flash memory level, a flash translation layer (FTL) acts as an interface layer between the operating system and the flash memory chip to overcome the inherent defects of NAND flash memory and becomes compatible with the legacy or heterogeneous upper-layer operating system [21]- [23]. Buffer management is integrated in FTL, along with other flash memory management strategies, such as address mapping, garbage collection, and wear leveling [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

DPW-LRU: An Efficient Buffer Management Policy Based on Dynamic Page Weight for Flash Memory in Cyber-Physical Systems

et al. 2019

Self Cite

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Owing to its high performance, small size, and low energy consumption, NAND flash memory has been extensively adopted in cyber-physical systems. However, the inherent characteristics of flash memory, including not-in-place update and asymmetric I/O latencies, present difficulties in the design of buffer management policies. In this paper, we propose an enhanced buffer management policy for the flash memory referred to as dynamic page weight least recently used (DPW-LRU), which considers temporal locality and simultaneously provides effective utilization of limited buffer resources. Page migration is further enhanced by identifying the page access mode and frequency while separating the buffer into two different regions. A novel eviction algorithm is also designed to reduce the write operations and maintain a high hit ratio of the buffer regions, combining dynamic temporal locality, real-time eviction cost, and recency of pages. The experimental results show that DPW-LRU improves the hit ratio by up to 8.3%, decreases the write operation by up to 22.6%, and reduces the overall latency by up to 18.8% relative to those of other state-of-the-art buffers management policies. INDEX TERMS NAND flash memory, buffer management, flash translation layer, temporal locality.

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A Maximum Cache Value Policy in Hybrid Memory-Based Edge Computing for Mobile Devices

Cited by 23 publications

References 27 publications

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

DPW-LRU: An Efficient Buffer Management Policy Based on Dynamic Page Weight for Flash Memory in Cyber-Physical Systems

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