12,308 research outputs found
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
Characteristics of WAP traffic
This paper considers the characteristics of Wireless Application Protocol (WAP) traffic. We start by constructing a WAP traffic model by analysing the behaviour of users accessing public WAP sites via a monitoring system. A wide range of different traffic scenarios were considered, but most of these scenarios resolve to one of two basic types. The paper then uses this traffic model to consider the effects of large quantities of WAP traffic on the core network. One traffic characteristic which is of particular interest in network dimensioning is the degree of self-similarity, so the paper looks at the characteristics of aggregated traffic with WAP, Web and packet speech components to estimate its self-similarity. The results indicate that, while WAP traffic alone does not exhibit a significant degree of self-similarity, a combined load from various traffic sources retains almost the same degree of self-similarity as the most self-similar individual source
Small-World File-Sharing Communities
Web caches, content distribution networks, peer-to-peer file sharing
networks, distributed file systems, and data grids all have in common that they
involve a community of users who generate requests for shared data. In each
case, overall system performance can be improved significantly if we can first
identify and then exploit interesting structure within a community's access
patterns. To this end, we propose a novel perspective on file sharing based on
the study of the relationships that form among users based on the files in
which they are interested.
We propose a new structure that captures common user interests in data--the
data-sharing graph-- and justify its utility with studies on three
data-distribution systems: a high-energy physics collaboration, the Web, and
the Kazaa peer-to-peer network. We find small-world patterns in the
data-sharing graphs of all three communities. We analyze these graphs and
propose some probable causes for these emergent small-world patterns. The
significance of small-world patterns is twofold: it provides a rigorous support
to intuition and, perhaps most importantly, it suggests ways to design
mechanisms that exploit these naturally emerging patterns
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign
Until recently, social media was seen to promote democratic discourse on
social and political issues. However, this powerful communication platform has
come under scrutiny for allowing hostile actors to exploit online discussions
in an attempt to manipulate public opinion. A case in point is the ongoing U.S.
Congress' investigation of Russian interference in the 2016 U.S. election
campaign, with Russia accused of using trolls (malicious accounts created to
manipulate) and bots to spread misinformation and politically biased
information. In this study, we explore the effects of this manipulation
campaign, taking a closer look at users who re-shared the posts produced on
Twitter by the Russian troll accounts publicly disclosed by U.S. Congress
investigation. We collected a dataset with over 43 million election-related
posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
million distinct users. This dataset included accounts associated with the
identified Russian trolls. We use label propagation to infer the ideology of
all users based on the news sources they shared. This method enables us to
classify a large number of users as liberal or conservative with precision and
recall above 90%. Conservatives retweeted Russian trolls about 31 times more
often than liberals and produced 36x more tweets. Additionally, most retweets
of troll content originated from two Southern states: Tennessee and Texas.
Using state-of-the-art bot detection techniques, we estimated that about 4.9%
and 6.2% of liberal and conservative users respectively were bots. Text
analysis on the content shared by trolls reveals that they had a mostly
conservative, pro-Trump agenda. Although an ideologically broad swath of
Twitter users was exposed to Russian Trolls in the period leading up to the
2016 U.S. Presidential election, it was mainly conservatives who helped amplify
their message
Basis Token Consistency: A Practical Mechanism for Strong Web Cache Consistency
With web caching and cache-related services like CDNs and edge services playing an increasingly significant role in the modern internet, the problem of the weak consistency and coherence provisions in current web protocols is becoming increasingly significant and drawing the attention of the standards community [LCD01]. Toward this end, we present definitions of consistency and coherence for web-like environments, that is, distributed client-server information systems where the semantics of interactions with resource are more general than the read/write operations found in memory hierarchies and distributed file systems. We then present a brief review of proposed mechanisms which strengthen the consistency of caches in the web, focusing upon their conceptual contributions and their weaknesses in real-world practice. These insights motivate a new mechanism, which we call "Basis Token Consistency" or BTC; when implemented at the server, this mechanism allows any client (independent of the presence and conformity of any intermediaries) to maintain a self-consistent view of the server's state. This is accomplished by annotating responses with additional per-resource application information which allows client caches to recognize the obsolescence of currently cached entities and identify responses from other caches which are already stale in light of what has already been seen. The mechanism requires no deviation from the existing client-server communication model, and does not require servers to maintain any additional per-client state. We discuss how our mechanism could be integrated into a fragment-assembling Content Management System (CMS), and present a simulation-driven performance comparison between the BTC algorithm and the use of the Time-To-Live (TTL) heuristic.National Science Foundation (ANI-9986397, ANI-0095988
Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach
This paper proposes a novel energy-efficient multimedia delivery system
called EStreamer. First, we study the relationship between buffer size at the
client, burst-shaped TCP-based multimedia traffic, and energy consumption of
wireless network interfaces in smartphones. Based on the study, we design and
implement EStreamer for constant bit rate and rate-adaptive streaming.
EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over
Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications
and Applications (ACM TOMCCAP), November 201
The pseudo-self-similar traffic model: application and validation
Since the early 1990¿s, a variety of studies has shown that network traffic, both for local- and wide-area networks, has self-similar properties. This led to new approaches in network traffic modelling because most traditional traffic approaches result in the underestimation of performance measures of interest. Instead of developing completely new traffic models, a number of researchers have proposed to adapt traditional traffic modelling approaches to incorporate aspects of self-similarity. The motivation for doing so is the hope to be able to reuse techniques and tools that have been developed in the past and with which experience has been gained. One such approach for a traffic model that incorporates aspects of self-similarity is the so-called pseudo self-similar traffic model. This model is appealing, as it is easy to understand and easily embedded in Markovian performance evaluation studies. In applying this model in a number of cases, we have perceived various problems which we initially thought were particular to these specific cases. However, we recently have been able to show that these problems are fundamental to the pseudo self-similar traffic model. In this paper we review the pseudo self-similar traffic model and discuss its fundamental shortcomings. As far as we know, this is the first paper that discusses these shortcomings formally. We also report on ongoing work to overcome some of these problems
- …