I would like to invite you to my thesis defence that will be held on 06/12/2022 at 14:30 in the thesis room at Hannah Arendt campus (City centre). I would be glad to see your presence.
For those who can not attend in person, a BBB link will be sent soon.
Abstract: The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In the first part of this thesis, we consider a service provider that launches a smart city service based on IoT data readings: to serve IoT data collected across different locations, the SP dynamically negotiates and re-scales bandwidth and service functions. Network slicing is becoming the platform of choice for several applications and services. Nowadays, most applications are virtualized to gain flexibility and portability. With network slicing, operators can create multiple network slices, which can be used for different applications with specific requirements. Behind the network slicing, a slice expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. Also, different infrastructure providers charge slicing services depending on specific access technology supported across sites and IoT data collection patterns.
In the first part of this work, we introduce a pricing mechanism based on the age of information to reduce the cost of service providers. This provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to locations with cheaper prices while meeting the quality of service requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data, based on the optimal uploading policy. First, the upload of collected data to reduce the costs of the SPs is considered to be a decision problem. By employing a Markov decision process framework, we determine threshold-based optimal policies to achieve the primary objective using dynamic programming. We establish that the pricing of the locations can be reduced to finding appropriate thresholds respectively for each location, which shifts part of traffic from the highly congested locations to locations with lower congestion. Given the nature of the problem, we propose an algorithm based on simulated annealing to find the best combination of the thresholds. Then, we modify the algorithm to perform parallel computation using a well-known coloring technique that exploits the neighborhood structure of the locations to reduce the convergence time twofold.
One of the key contributors to the service provider cost is the cost of leasing a network slice. For this reason, we study the resource allocation for network slices in 5G wireless networks in the later part of the thesis. Resource allocation encompasses a combination of different resource types (e.g., radio resource, CPU, memory, bandwidth). In this work, we explore a differential pricing scheme that maximizes social welfare among slices as well as among end-users. To do so, we propose a pricing mechanism that makes fairness at multiple levels: fairness among slices and fairness among slice locations. Therefore, the proposed scheme is beneficial for both the slices and the end-users independent of their location. In addition, we study the case where slices can manipulate their preferences to improve their utility. We show that the Fisher market game always has a pure Nash equilibrium and we prove Price of Anarchy is $1/N$ , where $N$ is the number of slices.
A major drawback of resource allocation with a centralized approach is the privacy concerns of the service providers and infrastructure providers. In general, infrastructure providers do not prefer to reveal information related to the available resource quantity. On the other hand, service providers do not prefer to reveal their utility functions. In the final part of this thesis, we study a decentralized resource allocation mechanism inspired by the Kelly Mechanism that preserves multi-level fairness. In addition, we show that each infrastructure provider can implement its own allocation rule independent of the other. With the proposed mechanism, we establish that the resulting allocation is a social optimum. Each theoretical finding in this work is validated by numerical simulations in respective chapters.