Paula Ta-Shma

Virtual machine time travel using continuous data protection and checkpointing

Virtual machine (VM) time travel enables reverting a virtual machines state, both transient and persistent, to past points in time. This capability can be used to improve virtual machine availability, to enable forensics on past VM states, and to recover from operator errors. We present an approach to virtual machine time travel which combines Continuous Data Protection (CDP) storage support with live-migration-based virtual machine checkpointing. In particular, we present a novel approach for CDP which enables efficient reverts of the storage state to past points in time and makes it possible to undo a revert, and this is achieved using a simple branched-temporal data structure. We also present a design and implementation of a simple live-migration-based checkpointing mechanism in Xen.

Using virtualization for high availability and disaster recovery

Traditional high-availability and disaster recovery solutions require proprietary hardware, complex configurations, applicationspecific logic, highly skilled personnel, and a rigorous and lengthy testing process. The resulting high costs have limited their adoption to environments with the most critical applications. However, high availability and disaster recovery are becoming increasingly important in many environments that cannot bear the complexity and the expense involved. In this paper, we show that virtualization can be used to develop solutions that meet this market demand. We describe the recently released Virtual Availability Manager (VAM) product offering, which provides simplified availability solutions using Xent-based virtualization, and which is available as part of the IBM Systems Director product. We present key design principles of VAM, explain its architecture and current capabilities, and describe the way it is being extended to enable recovery in case of disaster.

Reliability of Geo-replicated Cloud Storage Systems

Network bandwidth between sites is typically more scarce than bandwidth within a site in geo-replicated cloud storage systems, and can potentially be a bottleneck for recovery operations. We study the reliability of geo-replicated cloud storage systems taking into account different bandwidths within a site and between sites. We consider a new recovery scheme called staged rebuild and compare it with both a direct scheme and a scheme known as intelligent rebuild. To assess the reliability gains achieved by these schemes, we develop an analytical model that incorporates various relevant aspects of storage systems, such as bandwidths, latent sector errors, and failure distributions. The model applies in the context of Open Stack Swift, a widely deployed cloud storage system. Under certain practical system configurations, we establish that order of magnitude improvements in mean time to data loss (MTTDL) can be achieved using these schemes.

An Ingestion and Analytics Architecture for IoT Applied to Smart City Use Cases

As sensors are adopted in almost all fields of life, the Internet of Things (IoT) is triggering a massive influx of data. We need efficient and scalable methods to process this data to gain valuable insight and take timely action. Existing approaches which support both batch processing (suitable for analysis of large historical data sets) and event processing (suitable for real-time analysis) are complex. We propose the hut architecture, a simple but scalable architecture for ingesting and analyzing IoT data, which uses historical data analysis to provide context for real-time analysis. We implement our architecture using open source components optimized for Big Data applications and extend them, where needed. We demonstrate our solution on two real-world smart city use cases in transportation and energy management.

Brief Announcement: A Consent Management Solution for Enterprises

Technologies such as cloud, mobile and the Internet of Things (IoT) are resulting in the collection of more and more personal data. While this sensitive data can be a gold mine for enterprises, it can also constitute a major risk for them. Legislation and privacy norms are becoming stricter when it comes to collecting and processing personal data, requiring the informed consent of individuals to process their data for specific purposes. However, IT solutions that can address these privacy issues are still lacking. We briefly outline our solution and its main component called “Consent Manager”, for the management, automatic enforcement and auditing of user consent. We then describe how the Consent Manager was adopted as part of the European FP7 project COSMOS.