1,220 research outputs found
Resource Allocation Among Agents with MDP-Induced Preferences
Allocating scarce resources among agents to maximize global utility is, in
general, computationally challenging. We focus on problems where resources
enable agents to execute actions in stochastic environments, modeled as Markov
decision processes (MDPs), such that the value of a resource bundle is defined
as the expected value of the optimal MDP policy realizable given these
resources. We present an algorithm that simultaneously solves the
resource-allocation and the policy-optimization problems. This allows us to
avoid explicitly representing utilities over exponentially many resource
bundles, leading to drastic (often exponential) reductions in computational
complexity. We then use this algorithm in the context of self-interested agents
to design a combinatorial auction for allocating resources. We empirically
demonstrate the effectiveness of our approach by showing that it can, in
minutes, optimally solve problems for which a straightforward combinatorial
resource-allocation technique would require the agents to enumerate up to 2^100
resource bundles and the auctioneer to solve an NP-complete problem with an
input of that size
Dynamic remapping of parallel computations with varying resource demands
A large class of computational problems is characterized by frequent synchronization, and computational requirements which change as a function of time. When such a problem must be solved on a message passing multiprocessor machine, the combination of these characteristics lead to system performance which decreases in time. Performance can be improved with periodic redistribution of computational load; however, redistribution can exact a sometimes large delay cost. We study the issue of deciding when to invoke a global load remapping mechanism. Such a decision policy must effectively weigh the costs of remapping against the performance benefits. We treat this problem by constructing two analytic models which exhibit stochastically decreasing performance. One model is quite tractable; we are able to describe the optimal remapping algorithm, and the optimal decision policy governing when to invoke that algorithm. However, computational complexity prohibits the use of the optimal remapping decision policy. We then study the performance of a general remapping policy on both analytic models. This policy attempts to minimize a statistic W(n) which measures the system degradation (including the cost of remapping) per computation step over a period of n steps. We show that as a function of time, the expected value of W(n) has at most one minimum, and that when this minimum exists it defines the optimal fixed-interval remapping policy. Our decision policy appeals to this result by remapping when it estimates that W(n) is minimized. Our performance data suggests that this policy effectively finds the natural frequency of remapping. We also use the analytic models to express the relationship between performance and remapping cost, number of processors, and the computation's stochastic activity
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
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