More than 10,000 enterprises worldwide use the big data stack composed of multiple distributed systems. At Unravel, we build the next-generation APM platform for the big data stack, and we have worked with a representative sample of these enterprises that covers most industry verticals. This sample covers the spectrum of choices for deploying the big data stack across on-premises datacenters, private and public cloud deployments, and hybrid combinations of these. In this paper, we present a solution for assisting enterprises planning the migration of their big data stacks from on-premises deployments to the cloud. Our solution is goal driven and adapts to various migration scenarios. We present the system architecture we built and several cloud mapping options. We also describe a demonstration script that involves practical, real-world use-cases of the path to cloud adoption.

Summary Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud‐based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User‐side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user‐transparent and Cloud‐agnostic regular‐expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular‐expression search functionality over encrypted data by translating the search queries on‐the‐fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large‐scale (and potentially big data scale) data‐sets. We evaluate the performance of RESeED experimentally and demonstrate its scalability and correctness using real‐world data‐sets fromarXiv.organd Internet Engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (≃6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with thegreputility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd.

Capabilities for trustworthy cloud-based computing and data storage require usable, secure and efficient solutions which allow clients to remotely store and process their data in the cloud. In this paper, we present RESeED, a tool which provides user-transparent and cloud-agnostic search over encrypted data using regular expressions without requiring cloud providers to make changes to their existing infrastructure. When a client asks RESeED to upload a new file in the cloud, RESeED analyzes the file's content and updates novel data structures accordingly, encrypting and transferring the new data to the cloud. RESeED provides regular expression search over this encrypted data by translating queries on-the-fly to finite automata and analyzes efficient and secure representations of the data before asking the cloud to download the encrypted files. We evaulate a working prototype of RESeED experimentally (currently publicly available) and show the scalability and correctness of our approach using real-world data sets from arXiv.org and the IETF. We show absolute accuracy for RESeED, with very low (6%) overhead, and high performability, even beating grep for some benchmarks.

We present Reseed, a tool that provides user-transparent and Cloud-agnostic regular expression search over encrypted data without requiring trust in the Cloud, or changes to Cloud infrastructure. Upon receiving a search query, Reseed translates it to a finite automata and analyzes efficient and secure representations of the data before asking the Cloud to download the matching encrypted files. We demonstrate and evaluate a working prototype of Reseed and show the scalability and correctness of our approach using data from arXiv.org. For more details see our Technical Report.