Towards improvements on the quality of service for multi-tenant RDBMS in the cloud

Moreira, Leonardo O.; Farias, Victor A. E. de; Sousa, Flávio R. C.; Santos, Gustavo A. C.; Maia, José Gilvan Rodrigues; Machado, Javam C.

doi:10.1109/icdew.2014.6818322

Cited by 9 publications

(27 citation statements)

References 18 publications

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“…• adapt the technique used in the work of Moreira et al 4 for the allocation of replicated databases in the cloud since no strategy was adopted to avoid the provision of computational resources. The aforementioned work 4 presented an allocating strategy of databases in virtual machines in order to take advantage of existing virtual machines while avoiding SLA violation.…”

Section: Discussionmentioning

confidence: 99%

“…For each tenant type, there is a multi‐tenant model associated. However, the shared DBMS multi‐tenant model has been the most widely adopted in the database management platforms . In this model, a DBMS manages multiple tenants where a tenant is a database.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic provisioning techniques are designed to handle irregular workloads, so that SLA violations and their contractual penalties associated are avoided or limited, reducing costs from the cloud provider perspective . These techniques usually take action based on workload observation and can be classified as either reactive or proactive.…”

Section: Introductionmentioning

confidence: 99%

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Predictive elastic replication for multi‐tenant databases in the cloud

Sousa

Moreira

Filho

et al. 2018

Concurrency and Computation

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Cloud computing is a trend of technology aimed at providing on-demand services with payment based on usage. In order to improve the use of resources, cloud providers adopt multi-tenant approaches, reducing the operation cost of services. Moreover, tenants have irregular workload patterns, which impacts in guarantees of quality of service, mainly due to interference among tenants. This paper proposes PredRep, a predictive approach to characterize the cloud database system workload and automatically provide or reduce resources by replication techniques. In order to evaluate PredRep, some experiments that measure the quality of service and elasticity are presented. Our experiment results confirm that PredRep reduces cost and SLA violations. INTRODUCTIONCloud computing is now a well-established paradigm for the use of computational resources, according to which hardware infrastructure, software, and platforms for the development of new applications are offered as remotely available services on a global scale. 1 Cloud users give up their own computing infrastructure to dispose it through services, based on the pay-as-you-go model, offered by third parties (cloud providers), delegating responsibilities and assuming costs proportionally to the amount of resources used. Cloud providers, in turn, must meet the expected infrastructure needs of their users.The cloud computing adoption makes it an attractive paradigm that is potentially able to meet the most strict quality-of-service (QoS) levels stipulated in a service-level agreement (SLA). However, it is still a challenging issue for providers to efficiently tackle both gradual load variations and load peaks from the workloads, which are highly dynamic and, as such, very unpredictable, 2 in order not to violate SLA requirements to maximize their revenues.Cloud providers adopt the sharing of their computing resources among users, thus optimizing resource usage, reducing costs, and maximizing profits. There are several levels of resource sharing that define the granule shared for the different users of the cloud. For this, there is the multi-tenancy, which is a technique used to consolidate multiple tenants in a computational resource. 3 In the cloud database context, a tenant can be a tuple, table, database, DBMS, OS, or VM. For each tenant type, there is a multi-tenant model associated. However, the shared DBMS multi-tenant model has been the most widely adopted in the database management platforms. 4 In this model, a DBMS manages multiple tenants where a tenant is a database.Dynamic provisioning techniques are designed to handle irregular workloads, so that SLA violations and their contractual penalties associated are avoided or limited, reducing costs from the cloud provider perspective. 4 These techniques usually take action based on workload observation and can be classified as either reactive or proactive. Proactive solutions apply sophisticated system models for prediction 2 and use forecast results to triggering allocations of expected need in advance. In contrast,...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive elastic replication for multi‐tenant databases in the cloud

Sousa

Moreira

Filho

et al. 2018

Concurrency and Computation

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“…Thus, we seek to extend the network lifespan by choosing the best semantic collector for the next status of the network. ARIMA was chosen because it performs better with small prediction windows [Santos et al 2013] [Moreira et al 2014]. This model can be used to predict certain events like time series showing trends, correla-tions, and seasonal variations.…”

Section: Collects Datamentioning

confidence: 99%

“…ARIMA is considered a general class of models for forecasting a time series. Besides, it has performed well on short-term prediction windows [Moreira et al 2014, Santos et al 2013, which can be suitable to avoid temporal disruptions in WSN and other short-term communication failures.…”

Section: Introductionmentioning

confidence: 99%

FLECHA: a Forecasting eLEction meCHAnism for semantic collectors sensor nodes

Neto¹,

Moreira²,

Gomes³

2016

Anais Do XV Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance 2016)

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Due to the resource constraints of the sensor nodes, energy provisioning in wireless sensor networks remains a challenging task, particularly in outdoor scenarios. Among the literature proposals to mitigate this problem, we highlight semantic clustering as a recent energy-efficient technique for prolonging the network lifetime. In semantic clustering, each cluster has a semantic leader (collector) which is periodically elected according to an energy-related criterion. However, since collectors’ energy depletion is faster than the others members of their cluster, suitable election mechanisms are required to avoid the energy hole problem. Here we propose FLECHA, a mechanism based on the ARIMA model to predict semantic collectors elections with leader-node alternation. Our hypothesis is that by anticipating the best candidates to semantic leaders, we can improve the energy-saving at the node-level, and hence allow the network lifetime to be further extended.

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A predictive replication for multi‐tenant databases using deep learning

Raouf

Abo-Alian

Badr

2021

Concurrency and Computation

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The service level agreement (SLA) is an agreement between clients and the service provider, which defines the minimum performance and availability requirements for software. The service provider should have effective strategies for placement, migration, and replication of the tenants to reduce their operational costs, maximize the utilization of their hardware and software resources and accordingly meet the SLA requirements with slight SLA violations. In this research, a clustered‐based multi‐tenant database management system (CB‐MT DBMS) is proposed. Additionally, a dynamic proactive provisioning technique is built using three different prediction models: The Recursive Window Forecasting Autoregressive Integrated Moving Average (ARIMA) model, Exponential Moving Average (EMA) model, and the proposed Recurrent Neural Network (RNN) with Long Short‐Term Memory (LSTM) cells model. Various experimental scenarios with different datasets have been conducted to prove that the RNN model with LSTM cells is a promising solution in multi‐tenant environments, where the tenants have irregular workload patterns. Experimental results firstly show that the RNN model accuracy is superior to their counterparts (i.e., ARIMA and EMA models) when applied to multi‐tenant database workloads generated using TPC benchmarks, as it reduces the prediction error value which is computed using the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) metrics. Secondly, Experimental results prove that the RNN prediction model accuracy is superior to their counterparts for detecting SLA violation values and windows using different SLA values.

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Towards improvements on the quality of service for multi-tenant RDBMS in the cloud

Cited by 9 publications

References 18 publications

Predictive elastic replication for multi‐tenant databases in the cloud

Predictive elastic replication for multi‐tenant databases in the cloud

FLECHA: a Forecasting eLEction meCHAnism for semantic collectors sensor nodes

A predictive replication for multi‐tenant databases using deep learning

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