2,401 research outputs found
Cloud-based Content Distribution on a Budget
To leverage the elastic nature of cloud computing, a solution provider must be able to accurately gauge demand for its offering. For applications that involve swarm-to-cloud interactions, gauging such demand is not straightforward. In this paper, we propose a general framework, analyze a mathematical model, and present a prototype implementation of a canonical swarm-to-cloud application, namely peer-assisted content delivery. Our system – called Cyclops – dynamically adjusts the off-cloud bandwidth consumed by content servers (which represents the bulk of the provider's cost) to feed a set of swarming clients, based on a feedback signal that gauges the real-time health of the swarm. Our extensive evaluation of Cyclops in a variety of settings – including controlled PlanetLab and live Internet experiments involving thousands of users – show significant reduction in content distribution costs (by as much as two orders of magnitude) when compared to non-feedback-based swarming solutions, with minor impact on content delivery times
Exploring heterogeneity of unreliable machines for p2p backup
P2P architecture is a viable option for enterprise backup. In contrast to
dedicated backup servers, nowadays a standard solution, making backups directly
on organization's workstations should be cheaper (as existing hardware is
used), more efficient (as there is no single bottleneck server) and more
reliable (as the machines are geographically dispersed).
We present the architecture of a p2p backup system that uses pairwise
replication contracts between a data owner and a replicator. In contrast to
standard p2p storage systems using directly a DHT, the contracts allow our
system to optimize replicas' placement depending on a specific optimization
strategy, and so to take advantage of the heterogeneity of the machines and the
network. Such optimization is particularly appealing in the context of backup:
replicas can be geographically dispersed, the load sent over the network can be
minimized, or the optimization goal can be to minimize the backup/restore time.
However, managing the contracts, keeping them consistent and adjusting them in
response to dynamically changing environment is challenging.
We built a scientific prototype and ran the experiments on 150 workstations
in the university's computer laboratories and, separately, on 50 PlanetLab
nodes. We found out that the main factor affecting the quality of the system is
the availability of the machines. Yet, our main conclusion is that it is
possible to build an efficient and reliable backup system on highly unreliable
machines (our computers had just 13% average availability)
ISP-friendly Peer-assisted On-demand Streaming of Long Duration Content in BBC iPlayer
In search of scalable solutions, CDNs are exploring P2P support. However, the
benefits of peer assistance can be limited by various obstacle factors such as
ISP friendliness - requiring peers to be within the same ISP, bitrate
stratification - the need to match peers with others needing similar bitrate,
and partial participation - some peers choosing not to redistribute content.
This work relates potential gains from peer assistance to the average number
of users in a swarm, its capacity, and empirically studies the effects of these
obstacle factors at scale, using a month-long trace of over 2 million users in
London accessing BBC shows online. Results indicate that even when P2P swarms
are localised within ISPs, up to 88% of traffic can be saved. Surprisingly,
bitrate stratification results in 2 large sub-swarms and does not significantly
affect savings. However, partial participation, and the need for a minimum
swarm size do affect gains. We investigate improvements to gain from increasing
content availability through two well-studied techniques: content bundling -
combining multiple items to increase availability, and historical caching of
previously watched items. Bundling proves ineffective as increased server
traffic from larger bundles outweighs benefits of availability, but simple
caching can considerably boost traffic gains from peer assistance.Comment: In Proceedings of IEEE INFOCOM 201
In-Network View Synthesis for Interactive Multiview Video Systems
To enable Interactive multiview video systems with a minimum view-switching
delay, multiple camera views are sent to the users, which are used as reference
images to synthesize additional virtual views via depth-image-based rendering.
In practice, bandwidth constraints may however restrict the number of reference
views sent to clients per time unit, which may in turn limit the quality of the
synthesized viewpoints. We argue that the reference view selection should
ideally be performed close to the users, and we study the problem of in-network
reference view synthesis such that the navigation quality is maximized at the
clients. We consider a distributed cloud network architecture where data stored
in a main cloud is delivered to end users with the help of cloudlets, i.e.,
resource-rich proxies close to the users. In order to satisfy last-hop
bandwidth constraints from the cloudlet to the users, a cloudlet re-samples
viewpoints of the 3D scene into a discrete set of views (combination of
received camera views and virtual views synthesized) to be used as reference
for the synthesis of additional virtual views at the client. This in-network
synthesis leads to better viewpoint sampling given a bandwidth constraint
compared to simple selection of camera views, but it may however carry a
distortion penalty in the cloudlet-synthesized reference views. We therefore
cast a new reference view selection problem where the best subset of views is
defined as the one minimizing the distortion over a view navigation window
defined by the user under some transmission bandwidth constraints. We show that
the view selection problem is NP-hard, and propose an effective polynomial time
algorithm using dynamic programming to solve the optimization problem.
Simulation results finally confirm the performance gain offered by virtual view
synthesis in the network
Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction
Reciprocal questioning is essential for effective teaching and learning,
fostering active engagement and deeper understanding through collaborative
interactions, especially in large classrooms. Can large language model (LLM),
such as OpenAI's GPT (Generative Pre-trained Transformer) series, assist in
this? This paper investigates a pedagogical approach of classroom flipping
based on flipped interaction in LLMs. Flipped interaction involves using
language models to prioritize generating questions instead of answers to
prompts. We demonstrate how traditional classroom flipping techniques,
including Peer Instruction and Just-in-Time Teaching (JiTT), can be enhanced
through flipped interaction techniques, creating student-centric questions for
hybrid teaching. In particular, we propose a workflow to integrate prompt
engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a
quiz-prompt-discuss routine to empower students to self-regulate their learning
capacity and enable teachers to swiftly personalize training pathways. We
develop an LLM-driven chatbot software that digitizes various elements of
classroom flipping and facilitates the assessment of students using these
routines to deliver peer-generated questions. We have applied our LLM-driven
chatbot software for teaching both undergraduate and graduate students from
2020 to 2022, effectively useful for bridging the gap between teachers and
students in remote teaching during the COVID-19 pandemic years. In particular,
LLM-driven classroom flipping can be particularly beneficial in large class
settings to optimize teaching pace and enable engaging classroom experiences.Comment: Submitte
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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