Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning

In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introduce \system, a learned scheduler that leverages overlapping data reads among incoming queries and learns a scheduling strategy that improves cache hits. \system relies on deep reinforcement learning to produce workload-specific scheduling strategies that focus on long-term performance benefits while being adaptive to previously-unseen data access patterns. We present results from a proof-of-concept prototype, demonstrating that learned schedulers can offer significant performance improvements over hand-crafted scheduling heuristics. Ultimately, we make the case that this is a promising research direction in the intersection of machine learning and databases

DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts

Проектирование и разработка имитационной модели мультиклиентского кластера баз данных

One of the main trends of recent years in software design is a shift to a Software as a Service (SaaS) paradigm which brings a number of advantages for both software developers and end users. However, along with these benefits this transition brings new architectural challenges. One of such challenges is the implementation of a data storage that would meet the needs of a service-provider, at the same time providing a fairly simple application programming interface for software developers. In order to develop effective solutions in this area, the architectural features of cloud-based applications should be taken into account. Among others, such key features are the need for scalability and quick adaptation to changing conditions. This paper provides a brief analysis of the problems in the field of cloud data storage systems based on the relational model and it proposes the concept of database cluster designed for applications with a multi-tenant architecture. Besides, the article describes a simulation model of such a cluster, as well as the main stages of its development and the main principles forming its foundation.Одной из главных тенденций последних лет в проектировании программного обеспечения стал переход к парадигме Software as a Service (SaaS), которая несет ряд неоспоримых преимуществ как для компаний-разработчиков ПО, так и для конечных пользователей. Однако вместе с этими преимуществами данный переход несет и новые архитектурные вызовы, одним из которых является организация хранилища данных, которое могло бы удовлетворить нужды компании-провайдера услуг, обеспечив достаточно простой прикладной интерфейс для разработчиков. Для разработки эффективного решения в данной области следует принимать во внимание особенности архитектуры облачных приложений, ключевыми из которых являются потребность в простом масштабировании и быстрой адаптации к меняющимся условиям. В данной работе проводится краткий анализ существующих проблем в области организации облачных систем хранения данных, основанных на реляционной модели, а также предлагается концепция кластера РСУБД, предназначенного для обслуживания приложений с мультиклиентской архитектурой. Кроме того, в статье дается описание имитационной модели подобного кластера, а также основных этапов ее разработки и принципов, заложенных в ее основу

A Review Of Multi-Tenant Database And Factors That Influence Its Adoption.

A Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). This is gaining momentum with significant increase in the number of organizations ready to take advantage of the technology. A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organizations (tenants). This is a database which provides database support to a number of separate and distinct groups of users or tenants. This concept spreads the cost of hardware, software and other services to a large number of tenants, therefore significantly reducing per tenant cost. Three different approaches of implementing multi-tenant database have been identified. These methods have been shown to be increasingly better at pooling resources and also processing administrative operations in bulk. This paper reports the requirement of multi-tenant databases, challenges of implementing MTD, database migration for elasticity in MTD and factors influencing the choice of models in MTD. An insightful discussion is presented in this paper by grouping these factors into four categories. This shows that the degree of tenancy is an influence to the approach to be adopted and the capital and operational expenditure are greatly reduced in comparison with an on-premises solutio

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. 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Sharing Buffer Pool Memory in Multi-Tenant Relational Database-as-a-Service

ABSTRACT Relational database-as-a-service (DaaS) providers need to rely on multi-tenancy and resource sharing among tenants, since statically reserving resources for a tenant is not cost effective. A major consequence of resource sharing is that the performance of one tenant can be adversely affected by resource demands of other colocated tenants. One such resource that is essential for good performance of a tenant's workload is buffer pool memory. In this paper, we study the problem of how to effectively share buffer pool memory in multi-tenant relational DaaS. We first develop an SLA framework that defines and enforces accountability of the service provider to the tenant even when buffer pool memory is not statically reserved on behalf of the tenant. Next, we present a novel buffer pool page replacement algorithm (MT-LRU) that builds upon theoretical concepts from weighted online caching, and is designed for multi-tenant scenarios involving SLAs and overbooking. MT-LRU generalizes the LRU-K algorithm which is commonly used in relational database systems. We have prototyped our techniques inside a commercial DaaS engine and extensive experiments demonstrate the effectiveness of our solution

Fault Tolerant Multitenant Database Server Consolidation

Server consolidation is important in situations where a sequence of database tenants need to be allocated (hosted) dynamically on a minimum number of cloud server machines. Given a tenant’s load defined by the amount of resources that the tenant requires and a service-level- agreement (SLA) between the tenant customer and the cloud service provider, resource cost savings can be achieved by consolidating multiple database tenants on server machines. Ad- ditionally, in realistic settings, server machines might fail causing their tenants to become un- available. To address this, service providers place multiple replicas of each tenant on different servers and reserve extra capacity to ensure that tenant failover will not result in overload on any remaining server. The focus of this thesis is on providing effective strategies for placing tenants on server machines so that the SLA requirements are met in the presence of failure of one or more servers. We propose the Cube-Fit (CUBEFIT ) algorithm for multitenant database server consolidation that saves resource costs by utilizing fewer servers than existing approaches for analytical workloads. Additionally, unlike existing consolidation algorithms, CUBEFIT can tolerate multiple server failures while ensuring that no server becomes overloaded. We provide extensive theoretical analysis and experimental evaluation of CUBEFIT. We show that compared to existing algorithms, the average case and worst case behavior of CUBEFIT is superior and that CUBEFIT produces near-optimal tenant allocation when the number of tenants is large. Through evaluation and deployment on a cluster of up to 73 machines as well as through simulation stud- ies, we experimentally demonstrate the efficacy of CUBEFIT in practical settings

Performance Isolation in Multi-Tenant Applications

The thesis presents methods to isolate different tenants, sharing one application instance, with regards to he performance they observe. Therefore, a request based admission control is introduced. Furthermore, the publication presents methods and novel metrics to evaluate the degree of isolation a system achieves. These insights are used to evaluate the developed isolation methods, resulting in recommendations of methods for various scenarios