Predictive elastic replication for multi‐tenant databases in the cloud

Abstract

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,...

A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

“…In order to improve their profits, providers implement a resource sharing among multiple tenants by consolidating various tenant's applications on a single system. Thus, multiple tenants are run on a same physical server (Long et al, 2014), e.g., a tenant can share a DBMS with another in the context of databases (Sousa et al, 2018). In return, each tenant pays the rent of resources to the provider according to the 'pay as you go' model, i.e., a tenant only pays what it consumes (Armbrust et al, 2010).…”

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

“…There are other, same examples in Refs. [52,101,113,114], found in the performance and availability category, and also Reference [112] is included in scalability, elasticity, and cost category because all these strategies are addressing primary and secondary target objectives in a single replication strategy, with each target objective having its priority. In other words, these strategies address both categories of target objectives simultaneously.…”

“…The resource provisioning issue is one of the biggest issues in distributed computing configurations [157], especially when we talk about the dynamic workload and dynamic environments. The available solutions include proactive and reactive approaches [112]. During high workloads, the data storage must be able to expand with increasing load hike and also adjust to shrink during low load by releasing the unutilized cloud resources [128].…”

“…We can stress adaptively using more virtual machines. Another way to increase the performance is by using the SLA protocols, along with elasticity using cost-effective approaches [112]. In future, researchers can plan to extend the elasticity with queuing theory-based model, "where the server is treated as a queuing framework and its theoretic results are used to derive a relationship between the request rate, service times of requests, and the response time SLA", for the estimation of capacity regarding provisioning on the cloud [158].…”

A predictive replication for multi‐tenant databases using deep learning

“…Dynamic provisioning techniques 6 are designed to handle irregular workloads and accomplish the aforementioned tasks. Dynamic provisioning techniques usually act based on workload observation and can be classified as either reactive or proactive.…”