Service Reliability in an HC: Considering From the Perspective of Scheduling With Load-Dependent Machine Reliability

Liu, Chubo; Li, Kenli; Liang, Jie

doi:10.1109/tr.2019.2908269

Cited by 19 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PhCs are designed in one, two, and three-dimensional (3D) structures, whose two-dimensional (2D) structure has been considered by researchers due to its greater applications and fabrication simplicity. Research groups designed a variety of structures using two-dimensional PhCs such as optical filters [25][26][27][28][29][30][31][32], Full adders [33][34][35], encoders [36], comparators [37,38], Flip-Flop [39], analog-to-digital converters [40,41], registers [42,43], memories [44][45][46], sensors [47,48], Biosensors [49,50]. Photon crystals are designed in one-dimensional (1D), 2D and 3D, which is a proposed structure in the present research according to the 2D PhCs.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Ultra-Sensitive Biosensor with Simultaneous Detection (of Cancer and Diabetes) and Analysis of Deformation Effects on Dielectric Rods in Optical Microstructure

et al. 2021

View full text Add to dashboard Cite

This study proposes a refractive index sensor for the simultaneous detection of cancer and diabetes based on photonic crystals (PhC). The proposed PhC composed of silicon rods in the air bed arranged in a hexagonal lattice forms the fundamental structure. Two tubes are used to place the cancerous or diabetic samples for measurement. The sensor’s transmission characteristics are simulated and analyzed by solving Maxwell’s electromagnetic equations using the finite-difference time-domain approach for samples being studied. Therefore, diabetes and cancer are detected according to the changes in the refractive index of the samples using the laser source centered at 1550 nm. Considering the findings, the sensor’s geometry changes to adjust the suggested sensitivity and quality factor of structure. According to the results, transmission power ranges between 91 and 100% based on the sample. Moreover, sensitivity ranges from 1294 to 3080 nm/RIU and the maximum Figure of Mertie is nearly FOM = 1550.11 ± 150.11 RIU−1 with the detection in range 31 × 10−6 RIU. In addition, the small area (61.56 μm2) of biosensor results in its appropriateness for different uses in compact photonic integrated circuits. Next, we changed the shape of the dielectric rods and investigated their effects on the sensitivity parameter. The sensitivity and figure of merit after changes in the shape of dielectric rods and nanocavities are at best S = 20,393 nm/RIU and FOM = 9104.017 ± 606.93 RIU−1, receptively. In addition, the resolution detection range is 203.93 × 10−6 RIU.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Ultra-Sensitive Biosensor with Simultaneous Detection (of Cancer and Diabetes) and Analysis of Deformation Effects on Dielectric Rods in Optical Microstructure

et al. 2021

View full text Add to dashboard Cite

show abstract

“…1) Cores Reliability Model: We take into account five main failure effects for cores based on previous works [10], [29], [30], as follows:…”

Section: E Reliability Modelling Of the Server Processormentioning

confidence: 99%

COCKTAIL: Multicore Co-Optimization Framework With Proactive Reliability Management

Huang

Pahlevan

Zapater

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power-hungry components of servers (e.g., cores and cache), hence avoiding server failure before its lifetime period. Traditional approaches focus on either using hybrid caches to reduce the leakage power of traditional static randomaccess memory (SRAM) cache, and thus increase the energy efficiency, or the trade-off between the lifetime and performance of multi-core processors. However, these approaches fall short in terms of flexibility and applicability for HPC tasks in terms of multi-parametric optimization including quality-of-service (QoS), lifetime reliability, and energy efficiency. As a result, in this paper we propose COCKTAIL, a holistic strategy framework to jointly optimize the energy efficiency of multi-core server processors and tasks performance in the HPC context, while guaranteeing the lifetime reliability. First, we analyze the best cache technology among traditional SRAM and resistive random access memory (RRAM), within the context of hybrid cache architectures, to improve the energy efficiency and manage cache endurance limits with respect to tasks requirements. Second, we introduce a novel efficient proactive queue optimization policy to reorder HPC tasks for execution considering their end time and possible reliability effects on the use of the hybrid caches. Third, we present a dynamic model predictive control (MPC)-based reliability management method to maximize task performance, by controlling the frequency, temperature, and target lifetime of the server processor. Our results demonstrate that, while consuming similar energy, COCKTAIL provides up to 60% QoS improvement when compared to latest state-of-the-art energy optimization and reliability management techniques in the HPC context. Moreover, our strategy guarantees a design lifetime longer than 5 years for the whole HPC system.

show abstract

“…1) Cores Reliability Model: We consider five main failure effects for cores based on previous works [31], [11], [51], [52], including the mean time to failure (MTTF) under the effects of EM (transport of material), SM (caused by mechanical stress), TDDB (caused by gate oxide breakdown), TC (caused by temperature cycling), and NBTI (increasing the transistors' threshold voltage).…”

Section: Reliability Modelling Of Server Processormentioning

confidence: 99%

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

Huang

Pahlevan

Costero

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of the hottest server components (i.e., cores and cache). Previous methods focused on either improving energy efficiency by adopting new hybrid-cache architectures including the resistive random-access memory (RRAM) and static randomaccess memory (SRAM) at the hardware level, or exploring trade-offs between lifetime limitation and performance of multicore processors under stable workloads conditions. Therefore, no work has so far proposed a co-optimization method with hybridcache-based server architectures for real-life dynamic scenarios taking into account scalability, performance, lifetime reliability, and energy efficiency at the same time. In this paper, we first formulate a reliability model for the hybrid-cache architecture to enable precise lifetime reliability management and energy efficiency optimization. We also include the performance and energy overheads of cache switching, and optimize the benefits of hybrid-cache usage for better energy efficiency and performance. Then, we propose a runtime Q-Learning-based reliability management and performance optimization approach for multi-core microprocessors with the hybrid-cache architecture, jointly incorporated with a dynamic preemptive priority queue management method to improve the overall tasks' performance by targeting to respect their end time limits. Experimental results show that our proposed method achieves up to 44% average performance (i.e., tasks execution time) improvement, while maintaining the whole system design lifetime longer than 5 years, when compared to the latest state-of-the-art energy efficiency optimization and reliability management methods for computing servers.

show abstract

Service Reliability in an HC: Considering From the Perspective of Scheduling With Load-Dependent Machine Reliability

Cited by 19 publications

References 44 publications

RETRACTED: Ultra-Sensitive Biosensor with Simultaneous Detection (of Cancer and Diabetes) and Analysis of Deformation Effects on Dielectric Rods in Optical Microstructure

RETRACTED: Ultra-Sensitive Biosensor with Simultaneous Detection (of Cancer and Diabetes) and Analysis of Deformation Effects on Dielectric Rods in Optical Microstructure

COCKTAIL: Multicore Co-Optimization Framework With Proactive Reliability Management

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

Contact Info

Product

Resources

About