5,582 research outputs found
Auction-based Bandwidth Allocation Mechanisms for Wireless Future Internet
An important aspect of the Future Internet is the efficient utilization of
(wireless) network resources. In order for the - demanding in terms of QoS -
Future Internet services to be provided, the current trend is evolving towards
an "integrated" wireless network access model that enables users to enjoy
mobility, seamless access and high quality of service in an all-IP network on
an "Anytime, Anywhere" basis. The term "integrated" is used to denote that the
Future Internet wireless "last mile" is expected to comprise multiple
heterogeneous geographically coexisting wireless networks, each having
different capacity and coverage radius. The efficient management of the
wireless access network resources is crucial due to their scarcity that renders
wireless access a potential bottleneck for the provision of high quality
services. In this paper we propose an auction mechanism for allocating the
bandwidth of such a network so that efficiency is attained, i.e. social welfare
is maximized. In particular, we propose an incentive-compatible, efficient
auction-based mechanism of low computational complexity. We define a repeated
game to address user utilities and incentives issues. Subsequently, we extend
this mechanism so that it can also accommodate multicast sessions. We also
analyze the computational complexity and message overhead of the proposed
mechanism. We then show how user bids can be replaced from weights generated by
the network and transform the auction to a cooperative mechanism capable of
prioritizing certain classes of services and emulating DiffServ and time-of-day
pricing schemes. The theoretical analysis is complemented by simulations that
assess the proposed mechanisms properties and performance. We finally provide
some concluding remarks and directions for future research
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing
In this paper we propose a two-stage protocol for resource management in a
hierarchically organized cloud. The first stage exploits spatial locality for
the formation of coalitions of supply agents; the second stage, a combinatorial
auction, is based on a modified proxy-based clock algorithm and has two phases,
a clock phase and a proxy phase. The clock phase supports price discovery; in
the second phase a proxy conducts multiple rounds of a combinatorial auction
for the package of services requested by each client. The protocol strikes a
balance between low-cost services for cloud clients and a decent profit for the
service providers. We also report the results of an empirical investigation of
the combinatorial auction stage of the protocol.Comment: 14 page
Incentive Mechanisms for Hierarchical Spectrum Markets
In this paper, we study spectrum allocation mechanisms in hierarchical
multi-layer markets which are expected to proliferate in the near future based
on the current spectrum policy reform proposals. We consider a setting where a
state agency sells spectrum channels to Primary Operators (POs) who
subsequently resell them to Secondary Operators (SOs) through auctions. We show
that these hierarchical markets do not result in a socially efficient spectrum
allocation which is aimed by the agency, due to lack of coordination among the
entities in different layers and the inherently selfish revenue-maximizing
strategy of POs. In order to reconcile these opposing objectives, we propose an
incentive mechanism which aligns the strategy and the actions of the POs with
the objective of the agency, and thus leads to system performance improvement
in terms of social welfare. This pricing-based scheme constitutes a method for
hierarchical market regulation. A basic component of the proposed incentive
mechanism is a novel auction scheme which enables POs to allocate their
spectrum by balancing their derived revenue and the welfare of the SOs.Comment: 9 page
Spectrum Trading: An Abstracted Bibliography
This document contains a bibliographic list of major papers on spectrum
trading and their abstracts. The aim of the list is to offer researchers
entering this field a fast panorama of the current literature. The list is
continually updated on the webpage
\url{http://www.disp.uniroma2.it/users/naldi/Ricspt.html}. Omissions and papers
suggested for inclusion may be pointed out to the authors through e-mail
(\textit{[email protected]})
Agent-based transportation planning compared with scheduling heuristics
Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods
Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems
We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
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