The results of an empirical, case-study analysis demonstrate that PV will become cost-effective as a distributed resource long before it becomes a costeffective, central station, energy supply option. This conclusion is based on the assumption that the nontraditional benefits that distributed PV systems offer to the electrical system and to utility customers, including transmission and distribu...

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate fixed quantity resources over nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented parallel and based only on local data resulting need for distributed algorithms. In this articl...

Modern computing is increasingly using distributed systems, and improving their load distribution resource allocation essential for obtaining optimal performance. Distributed algorithms balancing employ a variety of techniques, including heuristic-based, optimization-based, machine learning-based ones. In this work, we present review load-balancing resource-allocation approaches. We explore the...

We study the problem of distributed training neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability computational resources. present an adaptive, resource-aware, on-device learning mechanism, DISTREAL, which is able to fully efficiently utilize available resources in a manner, increasing convergence speed. This achieved dropout mechanism that dynamically adj...

Resource allocation and interference management in wireless small cell networks have been areas of key research interest in the past few years. Although a large number of research studies have been carried out, the needs for high capacity, reliability, and energy efficiency in the emerging fifth-generation (5G) networks warrants the development of methodologies focusing on ultradense and self-o...