Live Data Migration for Reducing SLA Violations in Multi-tiered Storage Systems

Tai, Jianzhe; Sheng, Bo; Yao, Yi; Mi, Ningfang

doi:10.1109/ic2e.2014.8

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…In any case, one can perform a selective replication under constraints to avoid the transfer of high volumes of data in the network [41]. Outliers detection and data replication should be carefully designed when data hosts are characterized by limited resources (e.g., energy) [5].…”

Section: Related Workmentioning

confidence: 99%

Landmark based Outliers Detection in Pervasive Applications

Kolomvatsos

Anagnostopoulos

2021

2021 12th International Conference on Information and Communication Systems (ICICS)

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The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a 'soft' approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.

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Section: Related Workmentioning

confidence: 99%

Landmark based Outliers Detection in Pervasive Applications

Kolomvatsos

Anagnostopoulos

2021

2021 12th International Conference on Information and Communication Systems (ICICS)

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“…We can perform a selective replication under constraints avoiding to circulate all the collected data in the network. Such a selective approach can be realized online [37], however, under the goal of choosing the appropriate hosts to have data close to the appropriate users. In networks, where nodes exhibit limited energy resources, we have to balance the number of replicas to the energy consumption [5].…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Approach for Data Management in Pervasive Computing Applications

Kolomvatsos¹

2020

Preprint

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Current advances in Pervasive Computing (PC) involve the adoption of the huge infrastructures of the Internet of Things (IoT) and the Edge Computing (EC). Both, IoT and EC, can support innovative applications around end users to facilitate their activities. Such applications are built upon the collected data and the appropriate processing demanded in the form of requests. To limit the latency, instead of relying on Cloud for data storage and processing, the research community provides a number of models for data management at the EC. Requests, usually defined in the form of tasks or queries, demand the processing of specific data. A model for pre-processing the data preparing them and detecting their statistics before requests arrive is necessary. In this paper, we propose a promising and easy to implement scheme for selecting the appropriate host of the incoming data based on a probabilistic approach. Our aim is to store similar data in the same distributed datasets to have, beforehand, knowledge on their statistics while keeping their solidity at high levels. As solidity, we consider the limited statistical deviation of data, thus, we can support the storage of highly correlated data in the same dataset. Additionally, we propose an aggregation mechanism for outliers detection applied just after the arrival of data. Outliers are transferred to Cloud for further processing. When data are accepted to be locally stored, we propose a model for selecting the appropriate datasets where they will be replicated for building a fault tolerant system. We analytically describe our model and evaluate it through extensive simulations presenting its pros and cons.

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“…We classified them in two categories. The first category is related to studies on centralized Clouds such as [12], [13], [16], [15], [11], [31]. They investigated several issues related to I/O efficiency in case of heterogeneous storage systems, see Table Ia.…”

Section: Introductionmentioning

confidence: 99%

A Cost Model for Hybrid Storage Systems in a Cloud Federations

Chikhaoui

Boukhalfa

Boukhobza

2018

Annals of Computer Science and Information Systems

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A cloud federation gives to cloud service providers (CSP) the opportunity to collaborate in order to offer a better QoS to customers at a lower cost. To do so, CSPs make some spare resources available to others at a reduced cost. One of the most critical resources is the storage system as it represents the main system bottleneck. From this point of view, how to efficiently place data in a federation of Clouds with heterogeneous storage systems is a real challenge. To address this issue, one needs to accurately estimate the data placement cost. In this paper, we propose a cost model for hybrid storage systems in a cloud federation for a Database as a Service (DBaaS) application. It takes into account the storage system characteristics, customers I/O workloads and SLA. The proposed cost model considers both 1) Internal customers data placement cost including local placement, outsourcing, back-migration and penalty costs, and 2) External customers data placement cost including insourcing and geo-migration costs. It can be used to help in the decision-making process which aims to enhance customers QoS and reduce CSPs costs in a federation. Simulation results showed the relevance of the considered costs. We have shown that mis-considering some sub-costs may lead to a 95% cost error for external customers data placement and 80% for outsourcing customers. This may cause significant financial loss.

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Live Data Migration for Reducing SLA Violations in Multi-tiered Storage Systems

Cited by 16 publications

References 7 publications

Landmark based Outliers Detection in Pervasive Applications

Landmark based Outliers Detection in Pervasive Applications

A Probabilistic Approach for Data Management in Pervasive Computing Applications

A Cost Model for Hybrid Storage Systems in a Cloud Federations

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