14,913 research outputs found
Cloud-Based Optimization: A Quasi-Decentralized Approach to Multi-Agent Coordination
New architectures and algorithms are needed to reflect the mixture of local
and global information that is available as multi-agent systems connect over
the cloud. We present a novel architecture for multi-agent coordination where
the cloud is assumed to be able to gather information from all agents, perform
centralized computations, and disseminate the results in an intermittent
manner. This architecture is used to solve a multi-agent optimization problem
in which each agent has a local objective function unknown to the other agents
and in which the agents are collectively subject to global inequality
constraints. Leveraging the cloud, a dual problem is formulated and solved by
finding a saddle point of the associated Lagrangian.Comment: 7 pages, 3 figure
Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays
We present and analyze a computational hybrid architecture for performing
multi-agent optimization. The optimization problems under consideration have
convex objective and constraint functions with mild smoothness conditions
imposed on them. For such problems, we provide a primal-dual algorithm
implemented in the hybrid architecture, which consists of a decentralized
network of agents into which centralized information is occasionally injected,
and we establish its convergence properties. To accomplish this, a central
cloud computer aggregates global information, carries out computations of the
dual variables based on this information, and then distributes the updated dual
variables to the agents. The agents update their (primal) state variables and
also communicate among themselves with each agent sharing and receiving state
information with some number of its neighbors. Throughout, communications with
the cloud are not assumed to be synchronous or instantaneous, and communication
delays are explicitly accounted for in the modeling and analysis of the system.
Experimental results are presented to support the theoretical developments
made.Comment: 8 pages, 4 figure
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
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