Software Monitoring in Data Centers

Wu, Chengdong; Guo, Jun

doi:10.1007/978-1-4939-2092-1_42

Cited by 3 publications

(7 citation statements)

References 39 publications

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“…The data center service performance prediction procedure [1,2] usually includes following stages ( Fig. 1):…”

Section: Service Indexes Prediction and Calculation Proceduresmentioning

confidence: 99%

“…It's obvious that for increasing number of lags m it will lead motion in the lagged space to become more predictable, and for ∞ → m system will tend to become deterministic and equivalent to original state space. Takens' theorem [1] demonstrates that lagged vectors become deterministic at a finite dimension of 1 2 + ≥ d m…”

Section: Sti Time Series Prediction Algorithmmentioning

confidence: 99%

“…Physical resources are distributed on different servers and VMs so estimation of SROI value is a nontrivial task. The most efficient method of SROI analysis is development of radar char, a graphical method of displaying multivariate data more than two quantitative variables [1,5].…”

Section: Sroi and Spi Value Prediction Algorithmmentioning

confidence: 99%

“…In order to identify the main aspects of the problem, an analysis of recent studies and publi-cations was done. It was analyzed that aspects of data center service performance that were mentioned to be key ones [1,2], specifically service transactions index, service resource occupancy index and service performance index. To solve the problem of prediction of those, works were studied devoted to chaotic analysis [2][3][4], radar chart method [1,5] and weighted average method [6].…”

mentioning

confidence: 99%

“…It was analyzed that aspects of data center service performance that were mentioned to be key ones [1,2], specifically service transactions index, service resource occupancy index and service performance index. To solve the problem of prediction of those, works were studied devoted to chaotic analysis [2][3][4], radar chart method [1,5] and weighted average method [6]. Also, within the bounds of this study, the fundamental mathematical materials were analyzed [7][8][9] related to fuzzy logic in order to use it at cloud platform data center services performance analysis and prediction.…”

mentioning

confidence: 99%

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Development of the performance prediction algorithms for cloud services

Zuev¹,

Kropachev

Usov³

et al. 2018

SRIT

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Main stages of data center service performance prediction were discussed, specifically data monitoring and gathering, calculation and prediction of key indexes and performance index prediction. It was proposed to build data center service performance prediction algorithm based on an analysis of the service transactions index, service resource occupancy index and service performance index. Prediction of the indexes is based on chaotic time series analysis that was used to estimate service transactions index time series trend, the radar chart method to calculate the service resource occupancy index value and weighted average method to calculate service performance index. For performance prediction, it is proposed to use a fuzzy judgment matrix with the service transactions index and service resource occupancy index as input values. It was taken into consideration that service transactions index is usually represented by nonlinear time series and thus the index time series parameters had to be predicted by chaos theory and for the calculation of this index, the estimation procedure of Lyapunov exponent value can be used. The radar chart demonstrates service resource occupancy index estimation of shared storage, mobile storage, memory, computational capability and network bandwidth. The prediction technique was based on the fuzzy nearness category that use input values of transactions index and dynamic changes of the service resource occupancy index.

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“…The data center service performance prediction procedure [1,2] usually includes following stages ( Fig. 1):…”

Section: Service Indexes Prediction and Calculation Proceduresmentioning

confidence: 99%

Section: Sti Time Series Prediction Algorithmmentioning

confidence: 99%

Section: Sroi and Spi Value Prediction Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Development of the performance prediction algorithms for cloud services

Zuev¹,

Kropachev

Usov³

et al. 2018

SRIT

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DCAV: A software system to evaluate next‐generation cloud data center availability through a friendly graphical interface

et al. 2019

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To assess the availability of different data center configurations, understand the main root causes of data center failures and represent its low-level details, such as subsystem's behavior and their interconnections, we have proposed, in previous works, a set of stochastic models to represent different data center architectures (considering three subsystems: power, cooling, and IT) based on the TIA-942 standard. In this paper, we propose the Data Center Availability (DCAV), a web-based software system to allow data center operators to evaluate the availability of their data center infrastructure through a friendly interface, without need of understanding the technical details of the stochastic models.DCAV offers an easy step-by-step interface to create and configure a data center model. The main goal of the DCAV system is to abstract low-level details and modeling complexities, becoming the data center availability analysis a simple and less time-consuming task.Next-generation cloud data center infrastructures are being built on a hyperscale, also known as composable data centers, focused on promoting flexibility, automation, optimization, and scalability. For that, the next-generation data center refactors traditional ones into pools of disaggregated resource units (processors, memory, storage, and networking, plus power and cooling sources). According to Li et al 5 this design approach "offers the potential advantage of enabling continuous peak workload performance while minimizing resource fragmentation for fast evolving heterogeneous workloads."Several industry initiatives have emerged to turn this paradigm a reality. The Intel Rack Scale Design (Intel RSD) is an architecture for disaggregated composable data center infrastructure based on Redfish a Distributed Management Task Force (DMTF) industry standard. Many partners have developed solutions based on Intel RSD and Redfish; for instance, Ericsson has proposed the Hyperscale Datacenter System 6 that allows data center operators to configure and control a composable data center by using virtual performance-optimized resources (named virtual Performance-Optimized Data Center (vPOD)). To compose vPOD systems, only the required hardware resources are allocated and may be reallocated easily on demand reducing management complexities, idle resources, energy consumption, and maintenance time, which reflects in higher availability levels.Despite this, data center managers or planners must know in detail the data center architecture low-level details; how the power, cooling and IT subsystems work; and how they are interconnected to plan the data center with minimum downtime. Furthermore, the management of challenges, such as guaranteeing service level agreements with established availability levels, requires data center's planners to assess the availability of a data center, resulting in a complex task as it requires gathering information of a large-scale infrastructure.

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Data center maintenance: applications and future research directions

Abadi

Haghighat

2020

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Purpose One of the most critical infrastructures is a data center (DC) because of it having many servers, computers and other equipment. DCs provide online services for various companies in the information technology (IT) industry. DC facilities should provide reliable online services while addressing the required quality and performance level considering maximum reliability and availability. The purpose of this study is to represent and classify the main findings in this area and to identify the main research gaps and shortcomings from the perspective of research. Design/methodology/approach This paper provides an organized and systematic literature review focusing on topics regarding the operation and maintenance (O&M) management of DCs. Findings Although there are several studies on O&M management systems for industrial systems and facilities, a limited number of studies with few methods and models have focused on DCs so far and these facilities require more attention. This paper identifies the issues and challenges for DC buildings and facilities and provides a conclusion of the findings to highlight the main research limitations for discovering new potential methods as future research opportunities. Research limitations/implications The paper has highlighted the main practical issues of DCs in terms of maintenance management. Several research works have been discussed specifically for DC’s maintenance, which makes this paper a credible source for researchers, maintenance managers and companies involved in the area of DC. Because several of the reviewed literature were based on real case studies, decision-makers in the DC maintenance sector can take advantage of new research on maintenance scheduling to reduce the costs of maintenance. Originality/value The paper has presented a comprehensive list of frequent keywords in recent publications related to O&M management for DCs. It has provided a categorized list of publications based on by their topic, methodology and case study. Because this paper has discussed research works specifically for DC’s maintenance, it is a credible source for researchers, maintenance managers and companies involved in the area of DCs.

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Software Monitoring in Data Centers

Cited by 3 publications

References 39 publications

Development of the performance prediction algorithms for cloud services

Development of the performance prediction algorithms for cloud services

DCAV: A software system to evaluate next‐generation cloud data center availability through a friendly graphical interface

Data center maintenance: applications and future research directions

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