Cloud failure prediction based totally on conventional gadget gaining expertise of and deep learning

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#49 (ツ) vikay
Last edited: 24.10.2022 by vikay, read: 123 times

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Cloud failure is one in all the essential problems given that it is far capin a position to value tens of heaps and heaps of greenbacks to cloud provider providers, further to the lack of productiveness suffered through business users.

Fault tolerance control is the critical thing method to cope with this issue, and failure prediction is one in all the strategies to store you the prevalence of a failure. One of the principle disturbing situations in acting failure prediction is to supply a rather correct predictive version.

Although a few paintings on failure prediction fashions has been proposed, there also can additionally be nonetheless a loss of a complete assessment of fashions based totally on exceptional patterns of gadget gaining expertise of algorithms.

Therefore, on this paper, we recommend a complete contrast and version assessment for predictive fashions for task and challenge failure. These fashions are constructed and skilled the utilization of 5 conventional gadget gaining expertise of algorithms and 3 editions of deep gaining expertise of algorithms.

We use a benchmark dataset, stated as Google Cloud Traces, for schooling and checking out the fashions. We evaluated the normal performance of fashions the utilization of a pair of metrics and decided their critical capabilities, similarly to measured their scalability.

Our evaluation resulted within side the following findings. Firstly, within side the case of task failure prediction, we observed that Extreme Gradient Boosting produces the first-class version in which the disk area request and CPU request are the maximum critical capabilities that have an effect on the prediction. Second, for challenge failure prediction, we observed that Decision Tree and Random Forest produce the first-class fashions in which the concern of the challenge is the maximum critical function for each fashions.

Our scalability evaluation has decided that the Logistic Regression version is the maximum scalable in comparison to others.

Cloud computing is at the vanguard of a worldwide virtual transformation

i. It permits a industrial enterprise to offer an additional layer of protection in phrases of facts protection and permits them to elevate the extent of performance of operation to a logo new level. According to Business Fortune Insight, the North American marketplace has spent about 78.28 billion greenbacks on cloud services. The cloud computing marketplace is anticipated to preserve to amplify from $219B in 2020 to $791.48B in 2028

ii. The implementation and deployment of the cloud machine opens as plenty as address exceptional patterns of cloud failure

iii. The implementation and deployment of the cloud machine opens as plenty as address exceptional patterns of cloud failure

iv. Failing to address those screw ups will deliver about degradation of pleasant of provider (QoS), availability, and reliability. It will within side the quit cause an monetary loss for each cloud clients and providers

v. This undertaking is typically addressed with fault tolerance control, which gives the cap potential to detect, identify, and cope with faults with out unfavorable the very last quit result of cloud computing

vi. There are numerous classes of fault tolerance strategies that encompass redundancy strategies, fault-conscious regulations (i.e., reactive and proactive regulations), and cargo balance

vii. In this paper, we attention on proactive regulations that also can additionally be applied with a failure prediction method skilled the utilization of gadget gaining expertise of algorithms. Failure prediction is huge in stopping the prevalence of failure and in minimizing the renovation expenses of fault tolerance control. As there are exceptional patterns of cloud failure, we pay unique interest to the prediction of task and challenge failure.

Both screw ups are interconnected (i.e., a task is composed of one or extra tasks) and want to be tackled simultaneously.Therefore, on this paper, our goal is to construct and examine a fixed of skilled fashions to expect the task and challenge termination status (i.e. failure or success). For this reason, we've got got got selected 5 conventional gadget gaining expertise of algorithms (TML) and 3 editions of deep gaining expertise of algorithms (DL). TML algorithms encompass logistic regression (LR), choice tree (DT), random forest (RF), gradient boost (GB), and excessive gradient boost (XGBoost). Meanwhile, the DL algorithms communicate over with single-layer long-short-time period memory (LSTM), two-layer (bi-layer) LSTM, and 3-layer (tri-layer) LSTM. We used the benchmark dataset, Google Cluster Traces (GCT), posted in 2011, to educate and take a glance at the fashions. We then convey out a sequence of critiques to locate the first-class fashions. Therefore, this paintings contributes threefold.

viii. First, an method to comprehensively produce and examine predictive failure fashions.

ix. Second, the effects and findings of 4 patterns of analyzes, particularly exploratory statistics evaluation, function evaluation, normal performance evaluation, and scalability evaluation.

x. Third, a evaluation of cloud failure prediction and gadget gaining expertise of techniques especially related with GCT, similarly to different datasets.

Resources:

i. IBM and Juniper Networks Solutions Brief. IBM Global Services. 2009.

ii. IBM and Juniper Networks: Delivering Solutions That Transform Your Networking Infrastructure URL: ftp://public.dhe.ibm.com/common/ssi/ecm/en/jns03002usen/JNS03002USEN.PDF

iii. Sittig DF, Singh H. Eight rights of safe electronic health record use. JAMA 2009 Sep 9;302(10):1111- 1113.

iv. Wang X, Tan Y. Application of cloud computing in the health information system. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM). New York, NY: IEEE; 2010 Presented at: International Conference on Computer Application and System Modeling; October 22-24, 2010; Taiyuan, China.

v. He C, Jin X, Zhao Z, Xiang T. A cloud computing solution for hospital information system. In: Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS). New York, NY: IEEE; 2010 Presented at: IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010); October 29-31, 2010; Xiamen, China.

vi. Botts N, Thoms B, Noamani A, Horan TA. Cloud computing architectures for the underserved: public health cyberinfrastructures through a network of healthATMs. In: Proceedings of the 2010 43rd Hawaii International Conference on System Sciences (HICSS). New York, NY: IEEE; 2010 Presented at: The 43rd Hawaii International Conference on System Sciences; January 5-8 , 2010; Hawaii, USA.

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Cloud failure prediction based totally on conventional gadget gaining expertise of and deep learning

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