Strategies for setting time-to-live values in result caches

In web query result caching, staleness of queries are often bounded via a time-to-live (TTL) mechanism, which expires the validity of cached query results at some point in time. In this work, we evaluate the performance of three alternative TTL mechanisms: time-based TTL, frequency-based TTL, and click-based TTL. Moreover, we propose hybrid approaches obtained by pair-wise combination of these mechanisms. Our results indicate that combining time-based TTL with frequency-based TTL yields superior performance (i.e., lower stale query traffic and less redundant computation) than using a particular mechanism in isolation. Copyright is held by the owner/author(s)

Jointly Optimal Routing and Caching for Arbitrary Network Topologies

We study a problem of fundamental importance to ICNs, namely, minimizing routing costs by jointly optimizing caching and routing decisions over an arbitrary network topology. We consider both source routing and hop-by-hop routing settings. The respective offline problems are NP-hard. Nevertheless, we show that there exist polynomial time approximation algorithms producing solutions within a constant approximation from the optimal. We also produce distributed, adaptive algorithms with the same approximation guarantees. We simulate our adaptive algorithms over a broad array of different topologies. Our algorithms reduce routing costs by several orders of magnitude compared to prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing and Caching for Arbitrary Network Topologies", appearing in the 4th ACM Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28, 201

Performance analysis of a caching algorithm for a catch-up television service

The catch-up TV (CUTV) service allows users to watch video content that was previously broadcast live on TV channels and later placed on an on-line video store. Upon a request from a user to watch a recently missed episode of his/her favourite TV series, the content is streamed from the video server to the customer's receiver device. This requires that an individual flow is set up for the duration of the video, and since it is hard to impossible to employ multicast streaming for this purpose (as users seldomly issue a request for the same episode at the same time), these flows are unicast. In this paper, we demonstrate that with the growing popularity of the CUTV service, the number of simultaneously running unicast flows on the aggregation parts of the network threaten to lead to an unwieldy increase in required bandwidth. Anticipating this problem and trying to alleviate it, the network operators deploy caches in strategic places in the network. We investigate the performance of such a caching strategy and the impact of its size and the cache update logic. We first analyse and model the evolution of video popularity over time based on traces we collected during 10 months. Through simulations we compare the performance of the traditional least-recently used and least-frequently used caching algorithms to our own algorithm. We also compare their performance with a "perfect" caching algorithm, which knows and hence does not have to estimate the video request rates. In the experimental data, we see that the video parameters from the popularity evolution law can be clustered. Therefore, we investigate theoretical models that can capture these clusters and we study the impact of clustering on the caching performance. Finally, some considerations on the optimal cache placement are presented

A Guide to Distributed Digital Preservation

This volume is devoted to the broad topic of distributed digital preservation, a still-emerging field of practice for the cultural memory arena. Replication and distribution hold out the promise of indefinite preservation of materials without degradation, but establishing effective organizational and technical processes to enable this form of digital preservation is daunting. Institutions need practical examples of how this task can be accomplished in manageable, low-cost ways."--P. [4] of cove

Efficient Management of Short-Lived Data

Motivated by the increasing prominence of loosely-coupled systems, such as mobile and sensor networks, which are characterised by intermittent connectivity and volatile data, we study the tagging of data with so-called expiration times. More specifically, when data are inserted into a database, they may be tagged with time values indicating when they expire, i.e., when they are regarded as stale or invalid and thus are no longer considered part of the database. In a number of applications, expiration times are known and can be assigned at insertion time. We present data structures and algorithms for online management of data tagged with expiration times. The algorithms are based on fully functional, persistent treaps, which are a combination of binary search trees with respect to a primary attribute and heaps with respect to a secondary attribute. The primary attribute implements primary keys, and the secondary attribute stores expiration times in a minimum heap, thus keeping a priority queue of tuples to expire. A detailed and comprehensive experimental study demonstrates the well-behavedness and scalability of the approach as well as its efficiency with respect to a number of competitors.Comment: switched to TimeCenter latex styl

ret2spec: Speculative Execution Using Return Stack Buffers

Speculative execution is an optimization technique that has been part of CPUs for over a decade. It predicts the outcome and target of branch instructions to avoid stalling the execution pipeline. However, until recently, the security implications of speculative code execution have not been studied. In this paper, we investigate a special type of branch predictor that is responsible for predicting return addresses. To the best of our knowledge, we are the first to study return address predictors and their consequences for the security of modern software. In our work, we show how return stack buffers (RSBs), the core unit of return address predictors, can be used to trigger misspeculations. Based on this knowledge, we propose two new attack variants using RSBs that give attackers similar capabilities as the documented Spectre attacks. We show how local attackers can gain arbitrary speculative code execution across processes, e.g., to leak passwords another user enters on a shared system. Our evaluation showed that the recent Spectre countermeasures deployed in operating systems can also cover such RSB-based cross-process attacks. Yet we then demonstrate that attackers can trigger misspeculation in JIT environments in order to leak arbitrary memory content of browser processes. Reading outside the sandboxed memory region with JIT-compiled code is still possible with 80\% accuracy on average.Comment: Updating to the cam-ready version and adding reference to the original pape