Verena Kantere

Optimal Service Pricing for a Cloud Cache

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution.

An Economic Model for Self-Tuned Cloud Caching

Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-economic query services while maintaining a profitable cloud. In this work we propose an economic model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data.

Top- k dominant web services under multi-criteria matching

As we move from a Web of data to a Web of services, enhancing the capabilities of the current Web search engines with effective and efficient techniques for Web services retrieval and selection becomes an important issue. Traditionally, the relevance of a Web service advertisement to a service request is determined by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from using Information Retrieval similarity metrics up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Since there is no consensus on how to weight these scores, existing methods are typically pessimistic, adopting a worst-case scenario. As a consequence, several services, e.g., those having a single unrelated parameter, can be excluded from the result set, even though they are potentially good alternatives. In this work, we present a methodology that overcomes both deficiencies. Given a request, we introduce an objective measure that assigns a dominance score to each advertised Web service. This score takes into consideration all the available criteria for each parameter in the request. We investigate three distinct definitions of dominance score, and we devise efficient algorithms that retrieve the top-k most dominant Web services in each case. Extensive experimental evaluation on real requests and relevance sets, as well as on synthetically generated scenarios, demonstrates both the effectiveness of the proposed technique and the efficiency of the algorithms.

GrouPeer: Dynamic clustering of P2P databases

Sharing structured data in a P2P network is a challenging problem, especially in the absence of a mediated schema. The standard practice of answering a consecutively rewritten query along the propagation path often results in significant loss of information. On the opposite, the use of mediated schemas requires human interaction and global agreement, both during creation and maintenance. In this paper we present GrouPeer, an adaptive, automated approach to both issues in the context of unstructured P2P database overlays. By allowing peers to individually choose which rewritten version of a query to answer and evaluate the received answers, information-rich sources left hidden otherwise are discovered. Gradually, the overlay is restructured as semantically similar peers are clustered together. Experimental results show that our technique produces very accurate answers and builds clusters that are very close to the optimal ones by contacting a very small number of nodes in the overlay.

Efficient Semantic Web Service Discovery in Centralized and P2P Environments

Efficient and scalable discovery mechanisms are critical for enabling service-oriented architectures on the Semantic Web. The majority of currently existing approaches focuses on centralized architectures, and deals with efficiency typically by pre-computing and storing the results of the semantic matcher for all possible query concepts. Such approaches, however, fail to scale with respect to the number of service advertisements and the size of the ontologies involved. On the other hand, this paper presents an efficient and scalable index-based method for Semantic Web service discovery that allows for fast selection of services at query time and is suitable for both centralized and P2P environments. We employ a novel encoding of the service descriptions, allowing the match between a request and an advertisement to be evaluated in constant time, and we index these representations to prune the search space, reducing the number of comparisons required. Given a desired ranking function, the search algorithm can retrieve the top-k matches progressively, i.e., better matches are computed and returned first, thereby further reducing the search engines response time. We also show how this search can be performed efficiently in a suitable structured P2P overlay network. The benefits of the proposed method are demonstrated through experimental evaluation on both real and synthetic data.

Storing and Indexing Spatial Data in P2P Systems

The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. At first, research focused on P2P systems that host 1D data. Nowadays, the need for P2P applications with multidimensional data has emerged, motivating research on P2P systems that manage such data. The majority of the proposed techniques are based either on the distribution of centralized indexes or on the reduction of multidimensional data to one dimension. Our goal is to create from scratch a technique that is inherently distributed and also maintains the multidimensionality of data. Our focus is on structured P2P systems that share spatial information. We present SpatialP2P, a totally decentralized indexing and searching framework that is suitable for spatial data. SpatialP2P supports P2P applications in which spatial information of various sizes can be dynamically inserted or deleted, and peers can join or leave. The proposed technique preserves well locality and directionality of space.

SLA data management criteria

Service Level Agreements (SLAs) represent service management contracts that are processed by monitoring and measurement mechanisms for the evaluation of the signatories adherence to the agreed service levels during service execution. The paper discusses SLA data management characteristics that need to be considered in the design of data models for SLA documents. The SLA anatomy is introduced with respect to the Web Service Level Agreement (WSLA) [1] language specification. Furthermore, the paper highlights current obstacles for the integration of automated SLA management in the cloud business setting. The contributed SLA data analysis maps SLA terms to data management attributes according to their operational relevance during the SLA activity. We present an SLA digraph model for the automated SLA formulation and data handling. The SLA digraph is introduced as a programming module that sits on the application layer and communicates with backend data stores for the SLA persistence.

A SLA graph model for data services

Cloud computing has given rise to on-demand service provisioning and massive outsourcing of IT infrastructures and applications to virtual, commoditized ones. Despite the broad Service Level Agreement (SLA) usage in scientific settings, their role in cloud markets is peripheral and misinterpreted. The paper introduces a SLA graph data model that supports automated SLA formalization and data management through a property digraph. The data model is described as a directed graph (digraph). We elaborate on node and edge properties that indicate dependencies in the SLA data management flow. We sketch a realistic scenario of cloud data service provisioning to extract attributes that characterize the data service. The SLA graph model and data service attributes are used to demonstrate the formalization of a SLA template that is managed as a property graph. The graph structure enables the manipulation of SLA information in a modular, extensible way that considers the data flow and all inclusive data dependencies.

Using ECA rules to implement mobile query agents for fast-evolving pure P2P database systems

A challenging issue in fast-evolving pure P2P networks is the design of an appropriate mechanism for processing queries. Since both the data content of the peers as well as their acquaintances, change rapidly the typical P2P querying techniques become inappropriate. We are interested in P2P networks where peers are mobile and own a database. In this dynamic context the usage of a Mobile Agent framework appears very promising. The paper investigates the issues related to the above problem and proposes a P2P and Mobile Agent architecture based on Active Database technology. We argue that, the employment of ECA rules both for answering queries and deploying agents leads to an efficient as well as simple query processing technique. Furthermore, the proposed mobile agent system architecture offers a number of advantages due to the performance and scalability that can be achieved using Active Databases.

Query Relaxation across Heterogeneous Data Sources

The fundamental assumption for query rewriting in heterogeneous environments is that the mappings used for the rewriting are complete, i.e., every relation and attribute mentioned in the query is associated, through mappings, to relations and attributes in the schema of the source that the query is rewritten. In reality, it is rarely the case that such complete sets of mappings exist between sources, and the presence of partial mappings is the norm rather than the exception. So, practically, existing query answering algorithms fail to generate any rewriting in the majority of cases. The question is then whether we can somehow relax queries that cannot be rewritten as such (due to insufficient mappings), and whether we can identify the interesting query relaxations, given the mappings at hand. In this paper, we propose a technique to compute query relaxations of an input query that can be rewritten and evaluated in an environment of collaborating autonomous and heterogeneous data sources. We extend traditional techniques for query rewriting, and we propose both an exhaustive and an optimized heuristic algorithm to compute and evaluate these relaxations. Our technique works with input of any query similarity measure. The experimental study proves the effectiveness and efficiency of our technique.