24,549 research outputs found
A mathematical framework for analyzing incentives in peer-to-peer networks
The existence and performance of peer-to-peer systems depend on thecontribution of resources from interacting peers. One of the challenges ofresource sharing in peer-to-peer systems is free riding. A situation usersattempt to exploit the system by utilizing the resources of others withoutcontributing. We view this from rationality perspective that every peer inthe network will attempt to maximize their utility of the system. In thispaper, we approach the problem of free riders mitigation from utilityoptimization point of view, by modeling each peer's interest as UtilityMaximization Problem (UTP). We propose analytical model for the wholenetwork as a mixed integer linear programming model. The super peers inthe network are given the responsibility of maximizing the utility of all peers connected to them. This is to ensure fairness among the interacting peers and the stability of the entire system. This technique allows peers to either upload or download resources based on their best strategy and interest.Keywords: Free rider, Utility, Peer-to-Peer, Incentives, Maximization,Resource
Implementation of Volatile Secure Model in P2P System: A Detailed Analysis
In network domain system, the peer to peer systems shows an open access rather than other systems.P2P system defines each peer is able to share the information to other peer without the help of any centralized system. So there are more chances of malicious activities .for better security one peer must send some trust parameters along with the recommendations from other peer. This system is fully based on priority, trust worthiness history and peer satisfaction, recommendation. Those peers who is having more recommendations and trustworthiness value, that peer will connect with other peers only. A trust model is derived by integrating the risk management and security, by applying this new method; it provides the utility maximization of peer to peer system. The main objective of the system is to make sure that the peer to peer communication is reliable and secure by the use of the trust model surrounded each and every peer in the system.
DOI: 10.17762/ijritcc2321-8169.15068
Exploring the catallactic coordination approach for peer-to-peer systems
Efficient discovery and resource allocation is one of the challenges of
current Peer-to-Peer systems. In centralized approaches, the user requests can
be matched to the fastest, cheapest or most available resource. This approach,
however, shows scalability limits. In this paper, we explore the catallactic coordination
as a decentralized economic approach for resource allocation in peer-topeer
networks. The economic model of the catallaxy is based on the selfinterested
maximization of utility and the negotiation of prices between agents.
We evaluate the feasibility of our approach by means of simulations and compare
the proposed system with a centralized baseline approach. Our results indicate
that while in the catallacic approach the number of control messages exchanged
between the peers grows due to the negotiation process, its service
provision rate is fairly constant in different dynamic environments.Peer Reviewe
Decentralized vs. centralized economic coordination of resource allocation in grids
Application layer networks are software architectures that
allow the provisioning of services requiring a huge amount of resources
by connecting large numbers of individual computers, like in Grid or
Peer-to-Peer computing. Controlling the resource allocation in those networks
is nearly impossible using a centralized arbitrator. The network
simulation project CATNET will evaluate a decentralized mechanism
for resource allocation, which is based on the economic paradigm of the
Catallaxy, against a centralized mechanism using an arbitrator object. In
both versions, software agents buy and sell network services and resources
to and from each other. The economic model is based on self-interested
maximization of utility and self-interested cooperation between agents.
This article describes the setup of money and message ïŹows both for
centralized and decentralized coordination in comparison.Peer Reviewe
Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
We study a problem of optimal information gathering from multiple data
providers that need to be incentivized to provide accurate information. This
problem arises in many real world applications that rely on crowdsourced data
sets, but where the process of obtaining data is costly. A notable example of
such a scenario is crowd sensing. To this end, we formulate the problem of
optimal information gathering as maximization of a submodular function under a
budget constraint, where the budget represents the total expected payment to
data providers. Contrary to the existing approaches, we base our payments on
incentives for accuracy and truthfulness, in particular, {\em peer-prediction}
methods that score each of the selected data providers against its best peer,
while ensuring that the minimum expected payment is above a given threshold. We
first show that the problem at hand is hard to approximate within a constant
factor that is not dependent on the properties of the payment function.
However, for given topological and analytical properties of the instance, we
construct two greedy algorithms, respectively called PPCGreedy and
PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the
optimal solution. Finally, we evaluate our methods using a realistic crowd
sensing testbed.Comment: Longer version of AAAI'18 pape
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of âlayering as
optimization decompositionâ for time-varying channel models
Reverse Engineering TCP/IP-like Networks using Delay-Sensitive Utility Functions
TCP/IP can be interpreted as a distributed primal-dual algorithm to maximize aggregate utility over source rates. It has recently been shown that an equilibrium of TCP/IP, if it exists, maximizes the same delay-insensitive utility over both source rates and routes, provided pure congestion prices are used as link costs in the shortest-path calculation of IP. In practice, however, pure dynamic routing is never used and link costs are weighted sums of both static as well as dynamic components. In this paper, we introduce delay-sensitive utility functions and identify a class of utility functions that such a TCP/IP equilibrium optimizes. We exhibit some counter-intuitive properties that any class of delay-sensitive utility functions optimized by TCP/IP necessarily possess. We prove a sufficient condition for global stability of routing updates for general networks. We construct example networks that defy conventional wisdom on the effect of link cost parameters on network stability and utility
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
In this paper we consider a novel partitioned framework for distributed
optimization in peer-to-peer networks. In several important applications the
agents of a network have to solve an optimization problem with two key
features: (i) the dimension of the decision variable depends on the network
size, and (ii) cost function and constraints have a sparsity structure related
to the communication graph. For this class of problems a straightforward
application of existing consensus methods would show two inefficiencies: poor
scalability and redundancy of shared information. We propose an asynchronous
distributed algorithm, based on dual decomposition and coordinate methods, to
solve partitioned optimization problems. We show that, by exploiting the
problem structure, the solution can be partitioned among the nodes, so that
each node just stores a local copy of a portion of the decision variable
(rather than a copy of the entire decision vector) and solves a small-scale
local problem
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