189 research outputs found
Dynamic Policies for Cooperative Networked Systems
A set of economic entities embedded in a network graph collaborate by
opportunistically exchanging their resources to satisfy their dynamically
generated needs. Under what conditions their collaboration leads to a
sustainable economy? Which online policy can ensure a feasible resource
exchange point will be attained, and what information is needed to implement
it? Furthermore, assuming there are different resources and the entities have
diverse production capabilities, which production policy each entity should
employ in order to maximize the economy's sustainability? Importantly, can we
design such policies that are also incentive compatible even when there is no a
priori information about the entities' needs? We introduce a dynamic production
scheduling and resource exchange model to capture this fundamental problem and
provide answers to the above questions. Applications range from infrastructure
sharing, trade and organisation management, to social networks and sharing
economy services.Comment: 6-page version appeared at ACM NetEcon' 1
Exchange of Services in Networks: Competition, Cooperation, and Fairness
Exchange of services and resources in, or over, networks is attracting
nowadays renewed interest. However, despite the broad applicability and the
extensive study of such models, e.g., in the context of P2P networks, many
fundamental questions regarding their properties and efficiency remain
unanswered. We consider such a service exchange model and analyze the users'
interactions under three different approaches. First, we study a centrally
designed service allocation policy that yields the fair total service each user
should receive based on the service it others to the others. Accordingly, we
consider a competitive market where each user determines selfishly its
allocation policy so as to maximize the service it receives in return, and a
coalitional game model where users are allowed to coordinate their policies. We
prove that there is a unique equilibrium exchange allocation for both game
theoretic formulations, which also coincides with the central fair service
allocation. Furthermore, we characterize its properties in terms of the
coalitions that emerge and the equilibrium allocations, and analyze its
dependency on the underlying network graph. That servicing policy is the
natural reference point to the various mechanisms that are currently proposed
to incentivize user participation and improve the efficiency of such networked
service (or, resource) exchange markets.Comment: to appear in ACM Sigmetrics 201
A Framework for Routing and Congestion Control for Multicast Information Flows
We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances among various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source node. The scheduling is such that the flow of a session depends on the congestion of the next hop links. MMRS is throughput optimal. In addition, it has several other attractive features. It is computationally simple and can be implemented in a distributed, asynchronous manner. It has several parameters which can be suitably modified to control the end-to-end delay and packet loss in a topology-specific manner. These parameters can be adjusted to offer limited priorities to some desired sessions. MMRS is expected to play a significant role in end-to-end congestion control in the multicast scenario
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Traffic flow forecasting on graphs has real-world applications in many
fields, such as transportation system and computer networks. Traffic
forecasting can be highly challenging due to complex spatial-temporal
correlations and non-linear traffic patterns. Existing works mostly model such
spatial-temporal dependencies by considering spatial correlations and temporal
correlations separately and fail to model the direct spatial-temporal
correlations. Inspired by the recent success of transformers in the graph
domain, in this paper, we propose to directly model the cross-spatial-temporal
correlations on the spatial-temporal graph using local multi-head
self-attentions. To reduce the time complexity, we set the attention receptive
field to the spatially neighboring nodes, and we also introduce an adaptive
graph to capture the hidden spatial-temporal dependencies. Based on these
attention mechanisms, we propose a novel Adaptive Graph Spatial-Temporal
Transformer Network (ASTTN), which stacks multiple spatial-temporal attention
layers to apply self-attention on the input graph, followed by linear layers
for predictions. Experimental results on public traffic network datasets,
METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of
our model
Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks
Payment channel networks (PCNs) are a layer-2 blockchain scalability
solution, with its main entity, the payment channel, enabling transactions
between pairs of nodes "off-chain," thus reducing the burden on the layer-1
network. Nodes with multiple channels can serve as relays for multihop payments
by providing their liquidity and withholding part of the payment amount as a
fee. Relay nodes might after a while end up with one or more unbalanced
channels, and thus need to trigger a rebalancing operation. In this paper, we
study how a relay node can maximize its profits from fees by using the
rebalancing method of submarine swaps. We introduce a stochastic model to
capture the dynamics of a relay node observing random transaction arrivals and
performing occasional rebalancing operations, and express the system evolution
as a Markov Decision Process. We formulate the problem of the maximization of
the node's fortune over time over all rebalancing policies, and approximate the
optimal solution by designing a Deep Reinforcement Learning (DRL)-based
rebalancing policy. We build a discrete event simulator of the system and use
it to demonstrate the DRL policy's superior performance under most conditions
by conducting a comparative study of different policies and parameterizations.
Our work is the first to introduce DRL for liquidity management in the complex
world of PCNs.Comment: Best Paper Award at the 4th International Conference on Mathematical
Research for the Blockchain Economy (MARBLE 2023). 28 pages; minor language
edits and fixes; acknowledgments added; results unchange
Fair Bandwidth Allocation for Multicasting in Networks with Discrete Feasible Set
We study fairness in allocating bandwidth for loss-tolerant real-time multicast applications. We assume that the traffic is encoded in several layers so that the network can adapt to the available bandwidth and receiver processing capabilities by varying the number of layers delivered. We consider the case where receivers cannot subscribe to fractional layers. Therefore, the network can allocate only a discrete set of bandwidth to a receiver, whereas a continuous set of rates can be allocated when receivers can subscribe to fractional layers. Fairness issues differ vastly in these two different cases. Computation of lexicographic optimal rate allocation becomes NP-hard in this case, while lexicographic optimal rate allocation is polynomial complexity computable when fractional layers can be allocated. Furthermore, maxmin fair rate vector may not exist in this case. We introduce a new notion of fairness, maximal fairness. Even though maximal fairness is a weaker notion of fairness, it has many intuitively appealing fairness properties. For example, it coincides with lexicographic optimality and maxmin fairness, when maxmin fair rate allocation exists. We propose a polynomial complexity algorithm for computation of maximally fair rates allocated to various source-destination pairs, which incidentally computes the maxmin fair rate allocation, when the latter exists
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