Run Generation Revisited: What Goes Up May or May Not Come Down

In this paper, we revisit the classic problem of run generation. Run generation is the first phase of external-memory sorting, where the objective is to scan through the data, reorder elements using a small buffer of size M , and output runs (contiguously sorted chunks of elements) that are as long as possible. We develop algorithms for minimizing the total number of runs (or equivalently, maximizing the average run length) when the runs are allowed to be sorted or reverse sorted. We study the problem in the online setting, both with and without resource augmentation, and in the offline setting. (1) We analyze alternating-up-down replacement selection (runs alternate between sorted and reverse sorted), which was studied by Knuth as far back as 1963. We show that this simple policy is asymptotically optimal. Specifically, we show that alternating-up-down replacement selection is 2-competitive and no deterministic online algorithm can perform better. (2) We give online algorithms having smaller competitive ratios with resource augmentation. Specifically, we exhibit a deterministic algorithm that, when given a buffer of size 4M , is able to match or beat any optimal algorithm having a buffer of size M . Furthermore, we present a randomized online algorithm which is 7/4-competitive when given a buffer twice that of the optimal. (3) We demonstrate that performance can also be improved with a small amount of foresight. We give an algorithm, which is 3/2-competitive, with foreknowledge of the next 3M elements of the input stream. For the extreme case where all future elements are known, we design a PTAS for computing the optimal strategy a run generation algorithm must follow. (4) Finally, we present algorithms tailored for nearly sorted inputs which are guaranteed to have optimal solutions with sufficiently long runs

Write-limited sorts and joins for persistent memory

To mitigate the impact of the widening gap between the memory needs of CPUs and what standard memory technology can deliver, system architects have introduced a new class of memory technology termed persistent memory. Persistent memory is byteaddressable, but exhibits asymmetric I/O: writes are typically one order of magnitude more expensive than reads. Byte addressability combined with I/O asymmetry render the performance profile of persistent memory unique. Thus, it becomes imperative to find new ways to seamlessly incorporate it into database systems. We do so in the context of query processing. We focus on the fundamental operations of sort and join processing. We introduce the notion of write-limited algorithms that effectively minimize the I/O cost. We give a high-level API that enables the system to dynamically optimize the workflow of the algorithms; or, alternatively, allows the developer to tune the write profile of the algorithms. We present four different techniques to incorporate persistent memory into the database processing stack in light of this API. We have implemented and extensively evaluated all our proposals. Our results show that the algorithms deliver on their promise of I/O-minimality and tunable performance. We showcase the merits and deficiencies of each implementation technique, thus taking a solid first step towards incorporating persistent memory into query processing. 1

The swiss army knife of job submission tools: grid-control

Grid-control is a lightweight and highly portable open source submission tool that supports virtually all workflows in high energy physics (HEP). Since 2007 it has been used by a sizeable number of HEP analyses to process tasks that sometimes consist of up 100k jobs. grid-control is built around a powerful plugin and configuration system, that allows users to easily specify all aspects of the desired workflow. Job submission to a wide range of local or remote batch systems or grid middleware is supported. Tasks can be conveniently specified through the parameter space that will be processed, which can consist of any number of variables and data sources with complex dependencies on each other. Dataset information is processed through a configurable pipeline of dataset filters, partition plugins and partition filters. The partition plugins can take the number of files, size of the work units, metadata or combinations thereof into account. All changes to the input datasets or variables are propagated through the processing pipeline and can transparently trigger adjustments to the parameter space and the job submission. While the core functionality is completely experiment independent, integration with the CMS computing environment is provided by a small set of plugins.Comment: 8 pages, 7 figures, Proceedings for the 22nd International Conference on Computing in High Energy and Nuclear Physic

The Melbourne Shuffle: Improving Oblivious Storage in the Cloud

We present a simple, efficient, and secure data-oblivious randomized shuffle algorithm. This is the first secure data-oblivious shuffle that is not based on sorting. Our method can be used to improve previous oblivious storage solutions for network-based outsourcing of data

Instant restore after a media failure

Media failures usually leave database systems unavailable for several hours until recovery is complete, especially in applications with large devices and high transaction volume. Previous work introduced a technique called single-pass restore, which increases restore bandwidth and thus substantially decreases time to repair. Instant restore goes further as it permits read/write access to any data on a device undergoing restore--even data not yet restored--by restoring individual data segments on demand. Thus, the restore process is guided primarily by the needs of applications, and the observed mean time to repair is effectively reduced from several hours to a few seconds. This paper presents an implementation and evaluation of instant restore. The technique is incrementally implemented on a system starting with the traditional ARIES design for logging and recovery. Experiments show that the transaction latency perceived after a media failure can be cut down to less than a second and that the overhead imposed by the technique on normal processing is minimal. The net effect is that a few "nines" of availability are added to the system using simple and low-overhead software techniques

B-LOG: A branch and bound methodology for the parallel execution of logic programs

We propose a computational methodology -"B-LOG"-, which offers the potential for an effective implementation of Logic Programming in a parallel computer. We also propose a weighting scheme to guide the search process through the graph and we apply the concepts of parallel "branch and bound" algorithms in order to perform a "best-first" search using an information theoretic bound. The concept of "session" is used to speed up the search process in a succession of similar queries. Within a session, we strongly modify the bounds in a local database, while bounds kept in a global database are weakly modified to provide a better initial condition for other sessions. We also propose an implementation scheme based on a database machine using "semantic paging", and the "B-LOG processor" based on a scoreboard driven controller

GraphSE2^2: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE$^2$, an encrypted graph database for online social network services to address massive data breaches. GraphSE$^2$ preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE$^2$ provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE$^2$ with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE$^2$ is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE$^2$: An Encrypted Graph Database for Privacy-Preserving Social Search". It includes the security proof of the proposed scheme. If you want to cite our work, please cite the conference version of i