A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center

Leka, Habte Lejebo; Zhang, Fengli; Kenea, Ayantu Tesfaye; Tegene, Abebe Tamrat; Atandoh, Peter; Hundera, Negalign Wake

doi:10.1109/iccwamtip53232.2021.9674067

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…The software tool and the deep learning model developed in this work can be implemented in hardware, such as on a field programmable gate arrays (FPGA) device, in order to develop specialized and faster stand-alone devices for DNA profiling, and we hope that the community will investigate this. Moreover, in this work, we used MLP; however, there are several other deep learning networks, such as long short-term memory (LSTM) and generative adversarial networks (GANs), that are known to have provided better performance in other applications [45][46][47][48]. Future work will focus on using those deep learning networks.…”

Section: Discussionmentioning

confidence: 99%

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

et al. 2022

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DNA profiling involves the analysis of sequences of an individual or mixed DNA profiles to identify the persons that these profiles belong to. A critically important application of DNA profiling is in forensic science to identify criminals by finding a match between their blood samples and the DNA profile found on the crime scene. Other applications include paternity tests, disaster victim identification, missing person investigations, and mapping genetic diseases. A crucial task in DNA profiling is the determination of the number of contributors in a DNA mixture profile, which is challenging due to issues that include allele dropout, stutter, blobs, and noise in DNA profiles; these issues negatively affect the estimation accuracy and the computational complexity. Machine-learning-based methods have been applied for estimating the number of unknowns; however, there is limited work in this area and many more efforts are required to develop robust models and their training on large and diverse datasets. In this paper, we propose and develop a software tool called TAWSEEM that employs a multilayer perceptron (MLP) neural network deep learning model for estimating the number of unknown contributors in DNA mixture profiles using PROVEDIt, the largest publicly available dataset. We investigate the performance of our developed deep learning model using four performance metrics, namely accuracy, F1-score, recall, and precision. The novelty of our tool is evident in the fact that it provides the highest accuracy (97%) compared to any existing work on the most diverse dataset (in terms of the profiles, loci, multiplexes, etc.). We also provide a detailed background on the DNA profiling and literature review, and a detailed account of the deep learning tool development and the performance investigation of the deep learning method.

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Section: Discussionmentioning

confidence: 99%

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

et al. 2022

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“…It is being approached in the literature from many points of view, both in the workload specification and in the forecasting model itself. In the following, we will review some representative recent work to illustrate this variety of approaches [44][45][46][47][48].…”

Section: Forecasting Workload Behavior In Cloud Data Centers: a Seemi...mentioning

confidence: 99%

“…The first one consists of describing a synthetic workload, either in a static form [44], or from estimated resource life-cycle probabilities [47]. The second, more widespread, considers time series recorded in real data centers annotated with the relevant events [45,46,48].…”

Section: Forecasting Workload Behavior In Cloud Data Centers: a Seemi...mentioning

confidence: 99%

“…• Finally, we review a selection of recent works based on ML to predict workload behavior [46,48], including also its energy consumption [45]. However, an observation common to the previous referenced work, and to most of the literature on workload and energy forecasting, is the absence of cross-comparisons between complex and simple models [50].…”

Section: Forecasting Workload Behavior In Cloud Data Centers: a Seemi...mentioning

confidence: 99%

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L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime

et al. 2023

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Several emerging non-volatile (NV) memory technologies are rising as interesting alternatives to build the Last-Level Cache (LLC). Their advantages, compared to SRAM memory, are higher density and lower static power, but write operations wear out the bitcells to the point of eventually losing their storage capacity. In this context, this paper presents a novel LLC organization designed to extend the lifetime of the NV data array and a procedure to forecast in detail the capacity and performance of such an NV-LLC over its lifetime. From a methodological point of view, although different approaches are used in the literature to analyze the degradation of an NV-LLC, none of them allows to study in detail its temporal evolution. In this sense, this work proposes a forecasting procedure that combines detailed simulation and prediction, allowing an accurate analysis of the impact of different cache control policies and mechanisms (replacement, wear-leveling, compression, etc.) on the temporal evolution of the indices of interest, such as the effective capacity of the NV-LLC or the system IPC. We also introduce L2C2, a LLC design intended for implementation in NV memory technology that combines fault tolerance, compression, and internal write wear leveling for the first time. Compression is not used to store more blocks and increase the hit rate, but to reduce the write rate and increase the lifetime during which the cache supports near-peak performance. In addition, to support byte loss without performance drop, L2C2 inherently allows N redundant bytes to be added to each cache entry. Thus, L2C2+N, the endurance-scaled version of L2C2, allows balancing the cost of redundant capacity with the benefit of longer lifetime. For instance, as a use case, we have implemented the L2C2 cache with STT-RAM technology. It has affordable hardware overheads compared to that of a baseline NV-LLC without compression in terms of area, latency and energy consumption, and increases up to 6-37 times the time in which 50% of the effective capacity is degraded, depending on the variability in the manufacturing process. Compared to L2C2, L2C2+6 which adds 6 bytes of redundant capacity per entry, that means 9.1% of storage overhead, can increase up to 1.4-4.3 times the time in which the system gets its initial peak performance degraded.

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ADAPT: Attention-Driven Domain Adaptation for Inter-cluster Workload Forecasting in Cloud Data Centers

Mahbub,

Sinthia,

Jeon

et al. 2024

Lecture Notes in Computer Science

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A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center

Cited by 11 publications

References 13 publications

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime

ADAPT: Attention-Driven Domain Adaptation for Inter-cluster Workload Forecasting in Cloud Data Centers

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