Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Virtualization with Limited Hardware Support

<p>In recent years, as mobile devices started to become an essential part of everyday computing, virtualization on mobile devices has begun to emerge as a solution for supporting multiple profiles on the same device. However, virtualization on mobile and embedded systems, and to a greater extent, on systems with limited hardware support for virtualization, often face different hardware environment than x86 platforms.</p><p>First of all, these platforms were usually equipped with CPUs that did not have hardware virtualization support. We propose a transparent and portable CPU virtualization solution for all types of CPUs that have hardware breakpoint functionality. We use a combination of the hardware breakpoint support and guest kernel control flow graph analysis to trap and emulate sensitive instructions.</p><p>Second, the traditional way of implementing record and replay which is an important feature of virtualization, cannot be implemented the same way on CPUs without hardware branch counters. We propose a record and replay implementation without using hardware branch counters on paravirtualized guests. We inspect guest virtual machine internal states to carefully rearrange recorded instructions during replay to achieve the same end result without having to literally repeat the same stream of instructions.</p><p>Third, these platforms are often equipped with storage systems with distinct I/O characteristics. SD card, for example, is a prevalent storage media on smartphones. We discuss the mismatch of I/O characteristics between SD card write speed characteristics and guest virtual machine workload characteristics using VMware Mobile Virtualization Platform as an example. We then propose a solution to bridge the gap and achieve efficient guest I/O when storing guest virtual disk images on SD cards.</p><p>This dissertation shows that it is possible to efficiently virtualize and provide advanced virtualization functionality to a range of systems without relying on x86 and PC specific virtualization technologies.</p>Dissertatio

Sampling Algorithms for Evolving Datasets

Perhaps the most flexible synopsis of a database is a uniform random sample of the data; such samples are widely used to speed up the processing of analytic queries and data-mining tasks, to enhance query optimization, and to facilitate information integration. Most of the existing work on database sampling focuses on how to create or exploit a random sample of a static database, that is, a database that does not change over time. The assumption of a static database, however, severely limits the applicability of these techniques in practice, where data is often not static but continuously evolving. In order to maintain the statistical validity of the sample, any changes to the database have to be appropriately reflected in the sample. In this thesis, we study efficient methods for incrementally maintaining a uniform random sample of the items in a dataset in the presence of an arbitrary sequence of insertions, updates, and deletions. We consider instances of the maintenance problem that arise when sampling from an evolving set, from an evolving multiset, from the distinct items in an evolving multiset, or from a sliding window over a data stream. Our algorithms completely avoid any accesses to the base data and can be several orders of magnitude faster than algorithms that do rely on such expensive accesses. The improved efficiency of our algorithms comes at virtually no cost: the resulting samples are provably uniform and only a small amount of auxiliary information is associated with the sample. We show that the auxiliary information not only facilitates efficient maintenance, but it can also be exploited to derive unbiased, low-variance estimators for counts, sums, averages, and the number of distinct items in the underlying dataset. In addition to sample maintenance, we discuss methods that greatly improve the flexibility of random sampling from a system's point of view. More specifically, we initiate the study of algorithms that resize a random sample upwards or downwards. Our resizing algorithms can be exploited to dynamically control the size of the sample when the dataset grows or shrinks; they facilitate resource management and help to avoid under- or oversized samples. Furthermore, in large-scale databases with data being distributed across several remote locations, it is usually infeasible to reconstruct the entire dataset for the purpose of sampling. To address this problem, we provide efficient algorithms that directly combine the local samples maintained at each location into a sample of the global dataset. We also consider a more general problem, where the global dataset is defined as an arbitrary set or multiset expression involving the local datasets, and provide efficient solutions based on hashing