880 research outputs found
Storage and Search in Dynamic Peer-to-Peer Networks
We study robust and efficient distributed algorithms for searching, storing,
and maintaining data in dynamic Peer-to-Peer (P2P) networks. P2P networks are
highly dynamic networks that experience heavy node churn (i.e., nodes join and
leave the network continuously over time). Our goal is to guarantee, despite
high node churn rate, that a large number of nodes in the network can store,
retrieve, and maintain a large number of data items. Our main contributions are
fast randomized distributed algorithms that guarantee the above with high
probability (whp) even under high adversarial churn:
1. A randomized distributed search algorithm that (whp) guarantees that
searches from as many as nodes ( is the stable network size)
succeed in -rounds despite churn, for
any small constant , per round. We assume that the churn is
controlled by an oblivious adversary (that has complete knowledge and control
of what nodes join and leave and at what time, but is oblivious to the random
choices made by the algorithm).
2. A storage and maintenance algorithm that guarantees (whp) data items can
be efficiently stored (with only copies of each data item)
and maintained in a dynamic P2P network with churn rate up to
per round. Our search algorithm together with our
storage and maintenance algorithm guarantees that as many as nodes
can efficiently store, maintain, and search even under churn per round. Our algorithms require only polylogarithmic in bits to
be processed and sent (per round) by each node.
To the best of our knowledge, our algorithms are the first-known,
fully-distributed storage and search algorithms that provably work under highly
dynamic settings (i.e., high churn rates per step).Comment: to appear at SPAA 201
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Simple and stable dynamic traffic engineering for provider scale ethernet
Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaThe high speeds and decreasing costs of Ethernet solutions has motivated providers’ interest in using Ethernet as the link layer technology in their backbone and aggregation networks.
Provider scale Ethernet offers further advantages, providing not only an easy to manage solution for multicast traffic, but also transparent interconnection between clients’ LANs. These Ethernet deployments face altogether different design issues, requiring support for a significantly
higher number of hosts. This support relies on hierarquization, separating address and
virtual network spaces of customers and providers.
In addition, large scale Ethernet solutions need to grant forwarding optimality. This can be achieved using traffic engineering approaches. Traffic engineering defines the set of engineering methods and techniques used to optimize the flow of network traffic. Static traffic engineering
approaches enjoy widespread use in provider networks, but their performance is greatly penalized by sudden load variations. On the other hand, dynamic traffic engineering is tailored to adapt to load changes. However, providers are skeptical to adopt dynamic approaches as these induce problems such as routing instability, and as a result, network performance decreases.
This dissertation presents a Simple and Stable Dynamic Traffic Engineering framework
(SSD-TE), which addresses these concerns in a provider scale Ethernet scenario. The validation results show that SSD-TE achieves better or equal performance to static traffic engineering approaches, whilst remaining both stable and responsive to load variations
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