Scale-Space Filtering for Workload Analysis and Forecast

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

doi:10.1109/cloud.2013.119

Cited by 12 publications

(17 citation statements)

References 18 publications

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“…It adds or removes replicas according to information obtained in the monitoring service and SLA. In [12] is shown a solution for forecast workloads in dynamic environments. The paper uses forecast methods in time series.…”

Section: Methodsmentioning

confidence: 99%

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How to deploy a virtual learning environment in the cloud?

Coutinho¹,

Moreira²,

Paillard³

et al. 2014

Proceedings of the 7th Euro American Conference on Telematics and Information Systems

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Cloud computing is a trend of technology aimed at providing on-demand services with payment based on usage. Virtual Learning Environments (VLEs) are applications that require a highly scalable architecture that provides for its users an acceptable level of Quality of Service (QoS). This work aims to show the steps needed to install a VLE in a cloud computing infrastructure. The VLE's migration to this new type of execution environment allows the increase of its use but also brings some performance issues that must be considered. The case study will consider the Moodle VLE which was chosen for its widespread use.

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Section: Methodsmentioning

confidence: 99%

“…For this, we use the implementations reported in [13] and [12]. The replication service is based on RepliC [13].…”

Section: Methodsmentioning

confidence: 99%

How to deploy a virtual learning environment in the cloud?

Coutinho¹,

Moreira²,

Paillard³

et al. 2014

Proceedings of the 7th Euro American Conference on Telematics and Information Systems

Self Cite

View full text Add to dashboard Cite

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“…We combine this technique with two widely used forecasting methods, SVM (Support Vector Machines for Regression) and ARIMA (Autoregressive Integrated Moving Average) to obtain an approach robust to noise and capable to handle both reactive and proactive solutions [7]. SVM and ARIMA are representative, respectively, from machine learning and from statistical time series analysis.…”

Section: B Forecast Approachmentioning

confidence: 99%

“…This theory allows to represent time series in a mathematically sound way [18] since it provides a qualitative signal representation as a multi-scale measurement [7], thus enabling either fine and coarse interpretations of the data.…”

Section: B Forecast Approachmentioning

confidence: 99%

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

Moreira

Farias

Sousa

et al. 2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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Cloud computing is a trend of technology aimed at providing on-demand services with payment based on usage. To improve the use of resources, cloud providers adopt multi-tenant approaches, reducing the operation cost of services. Moreover, tenants have irregular workload patterns, impacting in the guarantees of quality of service, mainly due to interference between tenants. This paper proposes an approach to improve quality of service for multi-tenant RDBMS. This approach employs migration techniques of tenants, system monitoring, allocation strategy, forecast approach, and benefits of cloud infrastructure to improve performance and reduce provider cost. We carried out experiments on performance and resource usage in order to evaluate it.

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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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Scale-Space Filtering for Workload Analysis and Forecast

Cited by 12 publications

References 18 publications

How to deploy a virtual learning environment in the cloud?

How to deploy a virtual learning environment in the cloud?

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

Predictive elastic replication for multi‐tenant databases in the cloud

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