6,624 research outputs found
Distributed Computation and Reconfiguration in Actively Dynamic Networks
In this paper, we study systems of distributed entities that can actively modify their communication network. This gives rise to distributed algorithms that apart from communication can also exploit network reconfiguration in order to carry out a given task. At the same time, the distributed task itself may now require a global reconfiguration from a given initial network Gs to a target network Gf from a family of networks having some good properties, like small diameter. To formally capture costs associated with creating and maintaining connections, we define three reasonable edge-complexity measures: the total edge activations, the maximum activated edges per round, and the maximum activated degree of a node. We give (poly)log(n) time algorithms for the general task of transforming any Gs into a Gf of diameter (poly)log(n), while minimizing the edge-complexity. There is a natural trade-off between time and edge complexity. Our main lower bound shows that Ω(n) total edge activations and Ω(n/log n) activations per round must be paid by any algorithm (even centralized) that achieves an optimum of Θ(log n) rounds. On the positive side, we give three distributed algorithms for our general task. The first runs in O(log n) time, with at most 2n active edges per round, a total of O(n log n) edge activations, a maximum degree n − 1, and a target network of diameter 2. The second achieves bounded degree by paying an additional logarithmic factor in time and in total edge activations. It gives a target network of diameter O(log n) and uses O(n) active edges per round. Our third algorithm shows that if we slightly increase the maximum degree to polylog(n) then we can achieve a running time of o(log2n). This novel model of distributed computation and reconfiguration in actively dynamic networks and the proposed measures of the edge complexity of distributed algorithms, may open new avenues for research in the algorithmic theory of dynamic networks
Distributed Computation and Reconfiguration in Actively Dynamic Networks
We study here systems of distributed entities that can actively modify their communication network. This gives rise to distributed algorithms that apart from communication can also exploit network reconfiguration to carry out a given task. Also, the distributed task itself may now require a global reconfiguration from a given initial network Gs to a target network Gf from a desirable family of networks. To formally capture costs associated with creating and maintaining connections, we define three edge-complexity measures: the total edge activations, the maximum activated edges per round, and the maximum activated degree of a node. We give (poly)log(n) time algorithms for the task of transforming any Gs into a Gf of diameter (poly)log(n), while minimizing the edge-complexity. Our main lower bound shows that Ω(n) total edge activations and Ω(n/logn) activations per round must be paid by any algorithm (even centralized) that achieves an optimum of Θ(logn) rounds. We give three distributed algorithms for our general task. The first runs in O(logn) time, with at most 2n active edges per round, a total of O(nlogn) edge activations, a maximum degree n−1, and a target network of diameter 2. The second achieves bounded degree by paying an additional logarithmic factor in time and in total edge activations. It gives a target network of diameter O(logn) and uses O(n) active edges per round. Our third algorithm shows that if we slightly increase the maximum degree to polylog(n) then we can achieve o(log2n) running time
Dynamic Cloud Network Control under Reconfiguration Delay and Cost
Network virtualization and programmability allow operators to deploy a wide
range of services over a common physical infrastructure and elastically
allocate cloud and network resources according to changing requirements. While
the elastic reconfiguration of virtual resources enables dynamically scaling
capacity in order to support service demands with minimal operational cost,
reconfiguration operations make resources unavailable during a given time
period and may incur additional cost. In this paper, we address the dynamic
cloud network control problem under non-negligible reconfiguration delay and
cost. We show that while the capacity region remains unchanged regardless of
the reconfiguration delay/cost values, a reconfiguration-agnostic policy may
fail to guarantee throughput-optimality and minimum cost under nonzero
reconfiguration delay/cost. We then present an adaptive dynamic cloud network
control policy that allows network nodes to make local flow scheduling and
resource allocation decisions while controlling the frequency of
reconfiguration in order to support any input rate in the capacity region and
achieve arbitrarily close to minimum cost for any finite reconfiguration
delay/cost values.Comment: 15 pages, 7 figure
Dynamic Reconfiguration in Camera Networks: A Short Survey
There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
- …