A Stackelberg game for modelling asymmetric users' behavior in grid scheduling

In traditional distributed computing the users and owners of the computational resources usually belong to the same administrative domain. Therefore all users are equally entitled to use the resources. The situation is completely different in large-scale emergent distributed computing systems, such as Grid systems, where the roles of the users are asymmetric as regards their access rights and usage of resources. Further, unlike traditional distributed computing case, Grid systems introduce hierarchical levels, which are to be taken into account for optimizing the overall system's performance. In this paper we present a Stackelberg game for modelling asymmetric users' behavior in Grid scheduling scenario. We define a two-level game with a Leader at the first level and the rest of users, called Followers, at the second one. The Leader is responsible for computing a planning of his tasks, which is usually a large fraction of the total pool of tasks in the batch. The Followers try to select the best strategy for the assignments of their tasks subject to Leader's strategy. The Stackelberg game is then translated into a hierarchical optimization problem, which is solved by Genetic Algorithm (GA) on the Leader's level and by ad hoc heuristic combined with GA on the Followers' level. We have experimentally evaluated the approach through a benchmark of static instances and report computational results for resource utilization, makespan and flowtimePeer ReviewedPostprint (published version

Game-theoretic, market and meta-heuristics approaches for modelling scheduling and resource allocation in grid systems

Task scheduling and resource allocation are the crucial issues in any large scale distributed system, such as Computational Grids (CGs). However, traditional computational models and resolution methods cannot effectively tackle the complex nature of Grid, where the resources and users belong to many administrative domains with their own access policies, users' privileges, etc. Recently, researchers are investigating the use of game theoretic approaches for modelling task and resource allocation problems in CGs. In this paper, we present a compact survey of the most relevant research proposals in the literature to use game-based models for the resource allocation problems and their resolution using metaheuristic methods. We emphasize the need of the translation of the traditional economical models into the game scenarios and the use of metaheuristic schedulers for solving such games in order to address the new complex scheduling and allocation criterions. We study the case of asymmetric Stackelberg game used for modelling the Grid users' behavior, where the security and reliability criterions are aggregated and defined as the users' costs functions. The obtained results show the efficiency of the hybridization of heuristic-based approaches with game models, which enables to include additional requirements and features into the computational models and tackle more effectively the resolution of the applied schedulers.Peer ReviewedPostprint (published version

Modelling of user requirements and behaviors in computational grids

In traditional distributed computing systems a few user types are found having ratherPeer ReviewedPostprint (published version

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions.Market power, Electricity, Networks, Numeric models, Model comparison

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios