Migration mechanism of heterogeneous memory pages using a two-way Hash chain list

Pei, Songwen; Ji, Yanfei; Shen, Tianma; Liu, Haikun

doi:10.1360/n112018-00246

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Learning for edge caching from non-stationary data stream is a problem of learning under concept drift. Edge caching nodes face quickly changing conditions that include unexpected (or even adversarial) traffic patterns [9], [24]. Besides, users may be remapped to other edge servers by network controller due to load balance or network congestion [10].…”

Section: B Concept Driftmentioning

confidence: 99%

An Incremental Learning Based Edge Caching System: From Modeling to Evaluation

Tang

Yuan

et al. 2020

IEEE Access

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Caches are widely applied to improve data delivery performance in distributed systems like edge networks and content delivery networks (CDNs). We consider caching mechanism in those networks that deliver contents to end users. The challenge comes from the dynamic content distribution problem. The distribution of data popularity is highly skewed and changing over time. Besides, the access pattern of the user requests also varies over time. Some learning algorithms for edge caching problems need to rebuild a new model periodically to adapt to system dynamics, where the knowledge learned from the past is discarded. Besides, each model updating needs a large amount of data, leading to outdated models for consecutive user requests. Inspired by the success of incremental learning approaches in processing massive data in real time, we propose an incremental learning based framework at an edge caching server. The incremental learning algorithm is used to preserve valuable knowledge and to adapt to dynamic workloads faster. We implement our incremental learning based cache system prototype and evaluate its performance under various real-world workloads. The experimental results show that our algorithm can boost cache hit ratio for dynamic workloads compared with the state-of-the-art caching algorithms. INDEX TERMS Edge caching, incremental learning, system dynamics.

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Section: B Concept Driftmentioning

confidence: 99%

An Incremental Learning Based Edge Caching System: From Modeling to Evaluation

Tang

Yuan

et al. 2020

IEEE Access

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“…As a result, execution of the DNNs poses significant challenges to the underlying computing platforms. The memristor crossbar based DNN accelerator has been proved to be a promising solution to improve the performance or energy efficiency in executing the DNNs [3]- [5]. In the crossbar, the memristor locates at the intersection of the word line and the bit line.…”

Section: Introductionmentioning

confidence: 99%

Aggressive Fault Tolerance for Memristor Crossbar-Based Neural Network Accelerators by Operational Unit Level Weight Mapping

Song

Wang

et al. 2021

IEEE Access

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The memristor crossbar has the characteristic of high parallelism in implementing the matrix vector multiplication which can speed up the computation of the neural network. However, faulty memristors resulted from the hardware defects significantly degrade the classification accuracy of neural networks deployed onto the crossbar. Weight mapping is a type of low cost fault tolerant scheme. Unfortunately, the existing schemes usually conduct fault aware weight mapping in the entire row granularity which constrains the optimization space for fault tolerance. To overcome above mentioned problem, in this paper, we propose the operational unit (OU) level weight mapping which further adjusts mapping of the weight blocks inside each OU after the row granularity weight mapping. Such strategy can implement fine-grained fault tolerance. Moreover, we unify the similar inputs for the different OUs in order to constrain the increase in the number of input vectors resulted from the OU level mapping adjustment. The simulation results demonstrate that with the defect rates of 5%, 10%, 15% and 20%, on average classification accuracies of the networks optimized by the proposed scheme are improved by 1.165%, 4.38%, 10.73% and 12.58% compared to the ones of the row level weight mapping. Moreover, on average numbers of the input vectors are reduced by 61.8%, 73.2%, 75.3% and 64.9%, compared with the OU level weight mapping without considering unification of the similar inputs.

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TransMigrator: A Transformer-Based Predictive Page Migration Mechanism for Heterogeneous Memory

Pei

Qian

et al. 2022

Lecture Notes in Computer Science

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Migration mechanism of heterogeneous memory pages using a two-way Hash chain list

Cited by 5 publications

References 15 publications

An Incremental Learning Based Edge Caching System: From Modeling to Evaluation

An Incremental Learning Based Edge Caching System: From Modeling to Evaluation

Aggressive Fault Tolerance for Memristor Crossbar-Based Neural Network Accelerators by Operational Unit Level Weight Mapping

TransMigrator: A Transformer-Based Predictive Page Migration Mechanism for Heterogeneous Memory

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