Spectrum Leasing as an Incentive towards Uplink Macrocell and Femtocell Cooperation

The concept of femtocell access points underlaying existing communication infrastructure has recently emerged as a key technology that can significantly improve the coverage and performance of next-generation wireless networks. In this paper, we propose a framework for macrocell-femtocell cooperation under a closed access policy, in which a femtocell user may act as a relay for macrocell users. In return, each cooperative macrocell user grants the femtocell user a fraction of its superframe. We formulate a coalitional game with macrocell and femtocell users being the players, which can take individual and distributed decisions on whether to cooperate or not, while maximizing a utility function that captures the cooperative gains, in terms of throughput and delay.We show that the network can selforganize into a partition composed of disjoint coalitions which constitutes the recursive core of the game representing a key solution concept for coalition formation games in partition form. Simulation results show that the proposed coalition formation algorithm yields significant gains in terms of average rate per macrocell user, reaching up to 239%, relative to the non-cooperative case. Moreover, the proposed approach shows an improvement in terms of femtocell users' rate of up to 21% when compared to the traditional closed access policy.Comment: 29 pages, 11 figures, accepted at the IEEE JSAC on Femtocell Network

Revisiting Actor Programming in C++

The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the "C++ Actor Framework". CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts---in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities---that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPGPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime behaviour for up to 64 cores at very low memory footprint, or in the presence of GPUs. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page

Maximising microprocessor reliability through game theory and heuristics

PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most routine daily tasks now involving their use in some form and the market predicted to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect more functionality with each design iteration, but for no detriment in perceived performance. These devices can range from simple low-cost chips to expensive and complex systems and are a major cost driver in the equipment design phase. For more than 35 years, designers have kept pace with Moore's Law, but as device size approaches the atomic limit, layouts are becoming so complicated that current scheduling techniques are also reaching their limit, meaning that more resource must be reserved to manage and deliver reliable operation. With the advent of many-core systems and further sources of unpredictability such as changeable power supplies and energy harvesting, this reservation of capability may become so large that systems will not be operating at their peak efficiency. These complex systems can be controlled through many techniques, with jobs scheduled either online prior to execution beginning or online at each time or event change. Increased processing power and job types means that current online scheduling methods that employ exhaustive search techniques will not be suitable to define schedules for such enigmatic task lists and that new techniques using statistic-based methods must be investigated to preserve Quality of Service. A new paradigm of scheduling through complex heuristics is one way to administer these next levels of processor effectively and allow the use of more simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes would be beneficial in terms of cost reduction and system exibility within the next generation of device. This thesis investigates the use of heuristics and statistical methods in the operation of real-time systems, with the feasibility of Game Theory and Statistical Process Control for the successful supervision of high-load and critical jobs investigated. Heuristics are identified as an effective method of controlling complex real-time issues, with two-person non-cooperative games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within small embedded RISC devices and an increase in reliability for systems operating at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB simulation before testing on an ARM Cortex M3 architecture functioning as a simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Decentralized scheduling through an adaptive, trading-based multi-agent system

In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. In this environment, reinforcement learning agents learn to trade successfully. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs. However, due to combinatorial effects, the action and observation spaces of a simple reinforcement learning agent in this environment scale exponentially with key parameters of the problem size. However, the exponential scaling behavior can be transformed into a linear one if the agent is split into several independent sub-units. We further improve this distributed architecture using agent-internal parameter sharing. Moreover, it can be extended to set the exchange prices autonomously. We show that in our scheduling environment, the advantages of a distributed agent architecture clearly outweigh more aggregated approaches. We demonstrate that the distributed agent architecture becomes even more performant using agent-internal parameter sharing. Finally, we investigate how two different reward functions affect autonomous pricing and the corresponding scheduling.Comment: Accepted at ABMHuB 2022 worksho