Supporting Concurrency and Multiple Indexes in Private Access to Outsourced Data

Data outsourcing has recently emerged as a successful solution allowing individuals and organizations to delegate data and service management to external third parties. A major challenge in the data outsourcing scenario is how to guarantee proper privacy protection against the external server. Recent promising approaches rely on the organization of data in indexing structures that use encryption and the dynamic allocation of encrypted data to physical blocks for destroying the otherwise static relationship between data and the blocks in which they are stored. However, dynamic data allocation implies the need to re-write blocks at every read access, thus requesting exclusive locks that can affect concurrency. Also, these solutions only support search conditions on the values of the attribute used for building the indexing structure. In this paper, we present an approach that overcomes such limitations by extending the recently proposed shuffle index structure with support for concurrency and multiple indexes. Support for concurrency relies on the use of several differential versions of the data index that are periodically reconciled and applied to the main data structure. Support for multiple indexes relies on the definition of secondary shuffle indexes that are then combined with the primary index in a single data structure whose content and allocation is unintelligible to the server. We show how using such differential versions and combined index structure guarantees privacy, provides support for concurrent accesses and multiple search conditions, and considerably increases the performance of the system and the applicability of the proposed solution

A heuristic approach for the allocation of resources in large-scale computing infrastructures

An increasing number of enterprise applications are intensive in their consumption of IT, but are infrequently used. Consequently, organizations either host an oversized IT infrastructure or they are incapable of realizing the benefits of new applications. A solution to the challenge is provided by the large-scale computing infrastructures of Clouds and Grids which allow resources to be shared. A major challenge is the development of mechanisms that allow efficient sharing of IT resources. Market mechanisms are promising, but there is a lack of research in scalable market mechanisms. We extend the Multi-Attribute Combinatorial Exchange mechanism with greedy heuristics to address the scalability challenge. The evaluation shows a trade-off between efficiency and scalability. There is no statistical evidence for an influence on the incentive properties of the market mechanism. This is an encouraging result as theory predicts heuristics to ruin the mechanism’s incentive properties. Copyright © 2015 John Wiley & Sons, Ltd