Breeding Diameter-Optimal Topologies for Distributed Indexes

Patil, Sanket; Srinivasa, Srinath; Mukherjee, Saikat; Rachakonda, Aditya Ramana; Venkatasubramanian, Venkat

doi:10.25088/complexsystems.18.2.175

Cited by 6 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A related application area that have used the rational choice paradigm for modeling agents, is multi-agent networks. These applications study complex networks, by combining network science, rational choice theory and other related areas like evolutionary algorithms, to study different kinds of emergent properties arising from agents acting rationally in a networked environment Mei et al (2015); Patil, Srinivas, et al (2009);Patil, Srinivasa, & Venkatasubramanian (2009); Villez et al (2011). Some indicative problems addressed by multi-agent networks include: constrained negotiation and agreement protocols (Nedic et al, 2010;Meng & Chen, 2013), modeling diffusion and synchrony Faber et al (2010); Kiesling et al (2012); Kim et al (2011), etc.…”

Section: Rational Choice Based Modelsmentioning

confidence: 99%

The Evolution of Computational Agency

Srinivasa

Deshmukh

2020

Novel Approaches to Information Systems Design

Self Cite

View full text Add to dashboard Cite

Agent-based models have emerged as a promising paradigm for addressing ever increasing complexity of information systems. In its initial days in the 1990s when object-oriented modeling was at its peak, an agent was treated as a special kind of “object” that had a persistent state and its own thread of execution. But since then, agent-based models have diversified enormously to even open new conceptual insights about the nature of systems in general. This chapter presents a perspective on the disparate ways in which our understanding of agency, as well as computational models of agency, have evolved. Advances in hardware like GPUs that brought neural networks back to life may also similarly infuse new life into agent-based models and pave the way for significant advancements in research on artificial general intelligence (AGI).

show abstract

Section: Rational Choice Based Modelsmentioning

confidence: 99%

The Evolution of Computational Agency

Srinivasa

Deshmukh

2020

Novel Approaches to Information Systems Design

Self Cite

View full text Add to dashboard Cite

show abstract

“…The design of an optimal topology is obtained by deriving these measures from past usage patterns and then using network simulation to obtain an optimal topology. Such simulations, extensively described in [5], [6], are often based on neural networks and genetic algorithms and in which optimal topologies are obtained after executing the software for several hours. The premise is that once an optimal topology is chosen then it will remain static for the duration of the network operation.…”

Section: Related Workmentioning

confidence: 99%

“…In most adaptive networks such as ubiquitous networks [1] and overlay networks [7], communication pattens vary so significantly that it is often difficult to obtain an representative usage pattern to perform a simulation. In the past [6], research proposed to perform simulation repeatedly to obtain an optimal topology and the system reconfigures itself. However, in these systems communication patterns are aggregated over large time scales and the reconfiguration is slow.…”

Section: Related Workmentioning

confidence: 99%

“…In scale-free topologies some of the nodes act as "super nodes" and have a relatively larger load than other nodes. Scale-free topologies have increasingly been shown to be a better design choice for peer-to-peer data networks [6], [5]. To take into account the effect of symmetric and scale-free topologies, we assign a factor, ρ ∈ [0, 1], for a topology which measures the skew in degree distribution [6].…”

Section: Minimizing the Cost Of Data Sharing In A Udnmentioning

confidence: 99%

See 1 more Smart Citation

Providing Scalable Data Services in Ubiquitous Networks

Malik

Prasad²,

Patil

et al. 2010

Database Systems for Advanced Applications

Self Cite

View full text Add to dashboard Cite

Abstract. Topology is a fundamental part of a network that governs connectivity between nodes, the amount of data flow and the efficiency of data flow between nodes. In traditional networks, due to physical limitations, topology remains static for the course of the network operation. Ubiquitous data networks (UDNs), alternatively, are more adaptive and can be configured for changes in their topology. This flexibility in controlling their topology makes them very appealing and an attractive medium for supporting "anywhere, any place" communication. However, it raises the problem of designing a dynamic topology. The dynamic topology design problem is of particular interest to application service providers who need to provide cost-effective data services on a ubiquitous network. In this paper we describe algorithms that decide when and how the topology should be reconfigured in response to a change in the data communication requirements of the network. In particular, we describe and compare a greedy algorithm, which is often used for topology reconfiguration, with a non-greedy algorithm based on metrical task systems. Experiments show the algorithm based on metrical task system has comparable performance to the greedy algorithm at a much lower reconfiguration cost.

show abstract

Theoretical Notes on Regular Graphs as Applied to Optimal Network Design

Patil

Srinivasa

2010

Distributed Computing and Internet Technology

View full text Add to dashboard Cite

Breeding Diameter-Optimal Topologies for Distributed Indexes

Cited by 6 publications

References 14 publications

The Evolution of Computational Agency

The Evolution of Computational Agency

Providing Scalable Data Services in Ubiquitous Networks

Theoretical Notes on Regular Graphs as Applied to Optimal Network Design

Contact Info

Product

Resources

About