In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while maintaining response time guarantees.

Today, SLAs for SaaS business applications usually lack stringent service level objectives and significant penalties. Moreover, Operational Business Intelligence features of modern business applications, like analytic dashboards, result in mixed workloads which make it even more difficult to predict execution times accurately due to resource contention. In contrast to the traditional three-tier architecture, an architecture for SaaS business applications should combine application and database layer to allow for processing business transactions and queries according to a queuing approach which enables strict SLAs with stringent response time and throughput guarantees. With stricter SLAs it would be easier to compare different cloud offerings with on-premise solutions and thus cloud computing could become more attractive for potential customers.

Software-as-a-Service applications commonly consolidate multiple businesses into the same database to reduce costs. This practice makes it harder to implement several essential features of enterprise applications. The first is support for master data, which should be shared rather than replicated for each tenant. The second is application modification and extension, which applies both to the database schema and master data it contains. The third is evolution of the schema and master data, which occurs as the application and its extensions are upgraded. These features cannot be easily implemented in a traditional DBMS and, to the extent that they are currently offered at all, they are generally implemented within the application layer. This approach reduces the DBMS to a `dumb data repository' that only stores data rather than managing it. In addition, it complicates development of the application since many DBMS features have to be re-implemented. Instead, a next-generation multi-tenant DBMS should provide explicit support for Extensibility, Data Sharing and Evolution. As these three features are strongly related, they cannot be implemented independently from each other. Therefore, we propose FLEXSCHEME which captures all three aspects in one integrated model. In this paper, we focus on efficient storage mechanisms for this model and present a novel versioning mechanism, called XOR Delta, which is based on XOR encoding and is optimized for main-memory DBMSs.

In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the cluster to ensure that response time goals are met. In so doing, the number of servers in the cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant clustering framework for SAP's in-memory column database TREX.

This paper discusses about what cloud computing means for data management from an industrial perspective, illustrate challenges, opportunities, technologies and impact. We will also ask where cloud computing stands on the hype curve, if we are reinventing the wheel with some technologies, and outline important research questions the data management community should address to move cloud computing to the next level.

A multi-tenant database system for Software as a Service (SaaS) should offer schemas that are flexible in that they can be extended different versions of the application and dynamically modified while the system is on-line. This paper presents an experimental comparison of five techniques for implementing flexible schemas for SaaS. In three of these techniques, the database "owns" the schema in that its structure is explicitly defined in DDL. Included here is the commonly-used mapping where each tenant is given their own private tables, which we take as the baseline, and a mapping that employs Sparse Columns in Microsoft SQL Server. These techniques perform well, however they offer only limited support for schema evolution in the presence of existing data. Moreover they do not scale beyond a certain level. In the other two techniques, the application "owns" the schema in that it is mapped into generic structures in the database. Included here are XML in DB2 and Pivot Tables in HBase. These techniques give the application complete control over schema evolution, however they can produce a significant decrease in performance. We conclude that the ideal database for SaaS has not yet been developed and offer some suggestions as to how it should be designed.

Summary form only given. This paper describes basic architectures and best practices for implementing software as a service application. In this context, achieving good margins requires making careful engineering trade-offs between adding features and lowering total cost of ownership. Achieving good system utilization requires that businesses share resources using either virtual machines (OS-level virtualization) or multi-tenancy (application-level virtualization). While multi-tenancy achieves greater levels of consolidation, it limits the kinds of features that can be provided. Thus the ideal level for virtualization depends on the characteristics of the application and its users.

In the implementation of hosted business services, multiple tenants are often consolidated into the same database to reduce total cost of ownership. Common practice is to map multiple single-tenant logical schemas in the application to one multi-tenant physical schema in the database. Such mappings are challenging to create because enterprise applications allow tenants to extend the base schema, e.g., for vertical industries or geographic regions. Assuming the workload stays within bounds, the fundamental limitation on scalability for this approach is the number of tables the database can handle. To get good consolidation, certain tables must be shared among tenants and certain tables must be mapped into fixed generic structures such as Universal and Pivot Tables, which can degrade performance.

There is a class of data, including messages and business workflow state, for which conventional monolithic databases are less than ideal. Performance and scalability of Application Server systems can be dramatically increased by distributing such data across transactional filestores, each of which is bound to a server instance in a cluster. This paper describes a high-performance, transactional filestore that has been developed for the BEA WebLogic Application ServerTM and benchmarks it against a database. The filestore uses a novel, platform-independent disk scheduling algorithm to minimize the latency of small, synchronous writes to disk.

Traditional database systems provide a user with the ability to query and manipulate one database state, namely the current database state. However, in several emerging applications, the ability to analyze “what-if” scenarios in order to reason about the impact of an update (before committing that update) is of paramount importance. Example applications include hypothetical database access, active database management systems, and version management, to name a few. The central thesis of the Heraclitus paradigm is to provide flexible support for applications such as these by elevating deltas , which represent updates proposed against the current database state, to be first-class citizens. Heraclitus[Alg,C] is a database programming language that extends C to incorporate the relational algebra and deltas. Operators are provided that enable the programmer to explicitly construct, combine, and access deltas. Most interesting is the when operator, that supports hypothetical access to a delta: the expression E when σ yields the value that side effect free expression E would have if the value of delta expression σ were applied to the current database state. This article presents a broad overview of the philosophy underlying the Heraclitus paradigm, and describes the design and prototype implementation of Heraclitus[Alg, C]. A model-independent formalism for the Heraclitus paradigm is also presented. To illustrate the utility of Heraclitus, the article presents an in-depth discussion of how Heraclitus[Alg, C] can be used to specify, and thereby implement, a wide range of execution models for rule application in active databases; this includes both prominent execution models presented in the literature, and more recent “customized” execution models with novel features.

Article Free Access Share on Safety and translation of calculus queries with scalar functions Authors: Martha Escobar-Molano View Profile , Richard Hull View Profile , Dean Jacobs View Profile Authors Info & Claims PODS '93: Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsAugust 1993Pages 253–264https://doi.org/10.1145/153850.153909Published:01 August 1993Publication History 19citation245DownloadsMetricsTotal Citations19Total Downloads245Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper presents a type system for logic programs that supports parametric polymorphism and subtypes. This system follows most knowledge representation and object-oriented schemes in that subtyping is name-based, i.e., τ1 is considered to be a subtype of τ2 iff it is declared as such. We take this as a fundamental principle in the sense that type declarations have the form of subtype constraints. Types are assigned meaning by viewing such constraints as Horn clauses that, together with a few basic axioms, define a subtype predicate. This technique provides a (least) model for types and, at the same time, a sound and complete proof system for deriving subtypes. Using this proof system, we define well-typedness conditions which ensure that a logic program/query respects a set of predicate types. We prove that these conditions are consistent in the sense that every atom of every resolvent produced during the execution of a well-typed program is consistent with its type.