Convergence rates for distributed stochastic optimization over random networks

Multi-agent networked linear dynamic systems have attracted the attention of researchers in power systems, intelligent transportation, and industrial automation. The agents might cooperatively optimize a global performance objective, resulting in social optimization, or try to satisfy their own selfish objectives using a noncooperative differential game. However, in these solutions, large volumes of data must be sent from system states to possibly distant control inputs, thus resulting in high cost of the underlying communication network. To enable economically viable communication, a game-theoretic framework is proposed under the communication cost, or sparsity, constraint, given by the number of communicating state/control input pairs. As this constraint tightens, the system transitions from dense to sparse communication, providing the tradeoff between dynamic system performance and information exchange. Moreover, using the proposed sparsity-constrained distributed social optimization and noncooperative game algorithms, we develop a method to allocate the costs of the communication infrastructure fairly and according to the agents' diverse needs for feedback and cooperation. Numerical results illustrate utilization of the proposed algorithms to enable and ensure economic fairness of wide-area control among power companies.

Future power systems in terms of Cyber - Physical Energy Systems (CPES) apply the integration of IT and physical processes using local and wide area communication networks. The smart grid is a typical example of the application of CPES and poses additional challenges to the engineering as these networks consist of two components: the power system itself and an underlying communication network applied for transmitting monitoring and control information. Therefore, performance evaluations of CPES need to take into account both networks in detail in order to provide meaningful results. In this paper, we introduce our simulation environment INtegrated co-Simulation of Power and ICT systems for Real-time Evaluation (INSPIRE), which is based on the Hybrid Simulator Architecture [1] and capable of evaluating both power system and communication network within a co-simulation framework. Besides the simulator architecture, we detail our time synchronization approach, which is applied for interconnecting communication and power system simulation. Secondly, we present reference scenarios and configuration settings for the combined simulation system. Finally, we introduce the first performance evaluation carried out using INSPIRE, covering characteristics of the communication network and highlighting the retroactive effects on the power system using an exemplary control algorithm.

Inter-organizational networks often have multiple dimensions, each one denoting a type of relationship. In this research, we studied how one dimension of the network, a collaboration network, emerges from organizations' interaction on another dimension--the communication network. We modeled the emergence of collaboration as an event-based multi-agent team formation process that incorporates the structure of the underlying communication network, the diffusion of project information, and network-based influence. As a case study, we used an agent-based simulation to study the collaboration among humanitarian organizations. We also analyzed how changes to the communication network among humanitarian organizations affect their collaboration network. The experiment results suggest that different strategies to promote communication may have positive or negative impacts on the future collaboration network.

Networked control systems offer significant advantages in terms of reliability, commissioning and maintenance, especially for complex systems. They must be able to withstand delays and data corruption caused by the underlying communication network. Existing results in networked control systems provide varying degrees of robustness. However, cost is also an important factor for real world implementations, which further restricts the bandwidth of the underlying network and complexity of the communication protocol. In this paper we introduce a networked control system method, WMBPNCS, that we have previously proposed, and implement a control system using IEEE 802.15.4 (ZigBee) as its communication protocol, in an attempt to overcome these problems. We find through simulations that performance of our implementation is acceptable even under large amounts of random network delay and data loss.

Decentralized learning of randomization-based neural networks with centralized equivalence

We address a co-design of a controller and the underlying communication network in cloud-based cyber-physical systems. When communication occurs over shared resources, delays often arise that may have destabilizing effects on the closed-loop system. In order to ensure an optimal control design in the presence of such delays, not only is it useful to have a delay-aware controller but a method by which optimal assignment of delays can be imposed on the communication network links. In this paper, we propose a delay-aware stable feedback controller that judiciously accommodates the most recent state information and a machine learning (ML) based method for determining the optimal delay assignment. This ML method consists of an offline training of a neural network whose inputs are a set of selected delays and outputs are relevant performance-optimizing metrics. The resulting neural network is shown to be capable of learning the optimal delay assignment to the various links in the communication network and therefore yielding optimal performance. The proposed method is validated using a power system case study of an IEEE 68-bus, and shown to result in a notable performance improvement where in 91% of the cases a near-optimal performance can be realized.

We consider quasi-aggregative games for large populations of heterogeneous agents, whose interaction is determined by an underlying communication network. Specifically, each agent minimizes a quadratic cost function, which depends on its own strategy and on a convex combination of the strategies of its neighbors, and is subject to heterogeneous convex constraints. We suggest two distributed algorithms that can be implemented to steer the best responses of the rational agents to a Nash equilibrium configuration. The convergence of these schemes is guaranteed under different sufficient conditions depending on the matrices defining the agents' cost functions and on the communication network.

Wide Area Measurement Systems (WAMS) use an underlying communication network to collect and analyze data from devices in the power grid, aimed to improve grid operations. For WAMS to be effective, the communication network needs to support low packet latency and low packet losses. Internet Protocol (IP), the pervasive technology used in today's communication networks uses loop-free best-paths for data forwarding, which increases the load on these paths causing delays and losses in delivery. Information-Centric Networking (ICN), a new networking paradigm, designed to enable a data-centric information sharing, natively supports the concurrent use of multiple transmission interfaces, in-networking caching, as well as per-packet security and can provide better application support. In this paper, we present iCASM, an ICN-based network architecture for wide area smart grid communications. We demonstrate through simulations that iCASM achieves low latency and 100% data delivery even during network congestion by leveraging multiple available paths; thus significantly improving communication resiliency in comparison to an IP-based approach. iCASM can be used immediately on today's Internet as an overlay.

In this survey paper, we review three recent main results on cache networks, which not only considerably sharpen the approximate characterization of the rate-memory trade off, but also extend those results to more general networks. In these systems, a server with a database of some files (e.g. movies) is connected to multiple users via a communication network. Each user has an isolated memory of limited size that can be used for caching. The system operates in two phases: a placement phase where users each store a portion of the files in their local cache, and a delivery phase, where the users each request a file and the server delivers coded messages to the users, fulfilling their file requests. We start by considering the shared bottleneck network in two flavors of the system, with uncoded prefetching and with coded prefetching. First, for uncoded prefetching, an optimal design is proposed, under both centralized and decentralized settings, for both peak rate and average rate. The exact optimality is proven through a matching converse. Second, for caching with coded prefetching, we present a design that is optimal within a factor of approximately 2, which strictly improves the state of the art. Lastly, we move the focus to more general network topologies, and present an order-wise optimal scheme that is independent of the underlying communication network between the server and the users. This scheme is shown to achieve the minimum delivery delay with a constant factor for all memoryless networks.

A survey of distributed optimization

Mobile Social Network (MSN), built on inter-connected mobile devices, enables the flexibility of information exchanges of individuals within a virtual community. MSN may be self-organized and/or infrastructure-less, and the communication links may frequently fluctuate. When link qualities degrade, it remains critical to maintain the connections of important social pairs when supporting all social pairs is impossible. To achieve this goal, we propose to proactively place some reliable links (e.g., satellite links or UAV links) into the underlying communication network, so as to improve the service quality of the important social pairs, referred to as the Maintaining Social Connections (MSC) problem. We formulate the MSC problem and prove it is NP-hard. As such, we first study a special case of MSC, which is submodular and solvable with high approximation-ratio. Since the general MSC problem is not submodular, we propose an efficient approximation algorithm. Specially, by carefully choosing two submodular functions to lower/upper bound the MSC problem, we are able to prove the high approximation ratio of the proposed algorithm using these selected submodular functions. We further develop evolutionary algorithms to iteratively adjust the link placement via different exploration strategies, which also yield guaranteed approximation-ratio. Extensive evaluations based on both synthetic and real-world social network traces demonstrate the effectiveness of our proposed algorithms.

Multiagent systems are widely recognized as a method of choice for realization of distributed time-critical applications for the smart grid. However, no general solutions have been proposed for the difficult task of system development and validation, ready for deployment, which would fully account for the underlying communication network performance. We propose a novel platform designed for this purpose, which integrates a standard multiagent development framework [Java Agent Development (JADE)] and an industry standard communications network simulator (OPNET modeler). It was realized through generic extensions of the JADE framework to provide discrete event scheduling capabilities, while the OPNET modeler was extended to provide a generic method of associating the network nodes with agents running in JADE. The adopted method adheres to the high-level architecture standard. Importantly, applications developed using this platform may be deployed on the target system without manual modifications. A distributed protection application is presented and the performance is analyzed with respect to candidate agent behaviors and communication scenarios, demonstrating that the feasibility of the application critically depends on the choices made during its design and implementation.

In this paper we examine the key elements determining the performance of the HPC Challenge RandomAccess benchmark on next generation supercomputers. We find that the performance of this benchmark is closely related to the bisection bandwidth of the underlying communication network, performance of integer divide operation and details of benchmark specifications such as error tolerance and permissible multi-core mapping strategies. We demonstrate that seemingly small and innocuous changes in the benchmark can lead to significantly different system performance. We also present an algorithm to optimize RandomAccess benchmark for multi-core systems. Our algorithm uses aggregation and software routing and balances the load on the cores by specializing each of the cores for one specific routing or update function. This algorithm gives approximately a factor of 3 speedup on the Blue Gene/P system which is based on quad-core nodes.

Coordinated Path Following of Multiple UAVs for Time-Critical Missions in the Presence of Time-Varying Communication Topologies

Emerging large-scale monitoring applications rely on continuous tracking of complex data-analysis queries over collections of massive, physically-distributed data streams. Thus, in addition to the space- and time-efficiency requirements of conventional stream processing (at each remote monitor site), effective solutions also need to guarantee communication efficiency (over the underlying communication network). The complexity of the monitored query adds to the difficulty of the problem -- this is especially true for nonlinear queries (e.g., joins), where no obvious solutions exist for distributing the monitor condition across sites. The recently proposed geometric method offers a generic methodology for splitting an arbitrary (non-linear) global threshold-monitoring task into a collection of local site constraints; still, the approach relies on maintaining the complete stream(s) at each site, thus raising serious efficiency concerns for massive data streams. In this paper, we propose novel algorithms for efficiently tracking a broad class of complex aggregate queries in such distributed-streams settings. Our tracking schemes rely on a novel combination of the geometric method with compact sketch summaries of local data streams, and maintain approximate answers with provable error guarantees, while optimizing space and processing costs at each remote site and communication cost across the network. One of our key technical insights for the effective use of the geometric method lies in exploiting a much lower-dimensional space for monitoring the sketch-based estimation query. Due to the complex, highly nonlinear nature of these estimates, efficiently monitoring the local geometric constraints poses challenging algorithmic issues for which we propose novel solutions. Experimental results on real-life data streams verify the effectiveness of our approach.