Nearly-optimal scheduling of users with Markovian time-varying transmission rates

We address the problem of developing a well-performing and implementable scheduler of users with wireless connections to the central controller, which arise in areas such as mobile data networks, heterogeneous networks, or vehicular communications systems. The main feature of such systems is that the connection quality of each user is time-varying, resulting in time-varying transmission rate corresponding to available channel states. We assume that this evolution is Markovian, relaxing the common but unrealistic assumption of stationary channels. We first focus on the three-state channel and study the optimal policy, showing that threshold policies (of giving higher priority to users with higher transmission rate) are not necessarily optimal. For the general channel we design a scheduler which generalizes the recently proposed Potential Improvement (PI) scheduler, and propose its two practical approximations, whose performance is analyzed and compared to existing alternative schedulers in a variety of simulation scenarios. We suggest and give evidence that the variant of PI which only relies on the steady-state distribution of the channel, performs extremely well, and therefore should be used for practical implementation

Scheduling of users with Markovian time-varying transmission rates

We address the problem of developing a well-performing and implementable scheduler of users with wireless connection to the base station. The main feature of such real-life systems is that the quality conditions of the user channels are time-varying, which turn into the time-varying transmission rate due to different modulation and coding schemes. We assume that this phenomenon follows a Markovian law and most of the discussion is dedicated to the case of three quality conditions of each user, for which we characterize an optimal index policy and show that threshold policies (of giving higher priority to users with higher transmission rate) are not necessarily optimal. For the general case of arbitrary number of quality conditions we design a scheduler and propose its two practical approximations, and illustrate the performance of the proposed index-based schedulers and existing alternatives in a variety of simulation scenarios

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments

Smart Grid communications in high traffic environments

The establishment of a previously non-existent data class known as the Smart Grid will pose many difficulties on current and future communication infrastructure. It is imperative that the Smart Grid (SG), as the reactionary and monitory arm of the Power Grid (PG), be able to communicate effectively between grid controllers and individual User Equipment (UE). By doing so, the successful implementation of SG applications can occur, including support for higher capacities of Renewable Energy Resources. As the SG matures, the number of UEs required is expected to rise increasing the traffic in an already burdened communications network. This thesis aims to optimally allocate radio resources such that the SG Quality of Service (QoS) requirements are satisfied with minimal effect on pre-existing traffic. To address this resource allocation problem, a Lotka-Volterra (LV) based resource allocation and scheduler was developed due to its ability to easily adapt to the dynamics of a telecommunications environment. Unlike previous resource allocation algorithms, the LV scheme allocated resources to each class as a function of its growth rate. By doing so, the QoS requirements of the SG were satisfied, with minimal effect on pre-existing traffic. Class queue latencies were reduced by intelligent scheduling of periodic traffic and forward allocation of resources. This thesis concludes that the SG will have a large effect on the telecommunications environment if not successfully controlled and monitored. This effect can be minimized by utilizing the proposed LV based resource allocation and scheduler system. Furthermore, it was shown that the allocation of periodic SG radio channels was optimized by continual updates of the LV model. This ensured the QoS requirements of the SG are achieved and provided enhanced performance. Successful integration of SG UEs in a wireless network can pave the way for increased capacity of Renewable and Intermittent Energy Resources operating on the PG

Comparison Study of Resource Allocation Strategies for OFDM Multimedia Networks

Advanced MAC scheduling schemes provide efficient support of multimedia services in multiuser OFDM wireless networks. Designed in a cross layer approach, they opportunistically consider the channel state and are well adapted to the wireless multipath fading environment. These schedulers take advantage of time, frequency, and multiuser diversity. Thereby they maximize the global system throughput while ensuring the highest possible level of fairness. However their performances heavily depend on the bandwidth granularity (i.e., the number of elementary resource units) that is used in the resource allocation process. This paper presents and compares the main OFDM scheduling techniques. In particular it studies the influence of bandwidth granularity on the resource allocation strategies performances. The paper reveals that though bandwidth granularity has never been considered in former studies, it is of major importance for determining the application range of advanced OFDM scheduling techniques

Cross-layer design for OFDMA wireless networks with finite queue length based on game theory

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.In next generation wireless networks such as 4G- LTE and WiMax, the demand for high data rates, the scarcity of wireless resources and the time varying channel conditions has led to the adoption of more sophisticated and robust techniques in PHY such as orthogonal frequency division multiplexing (OFDM) and the corresponding access technique known as orthogonal frequency division multiplexing access (OFDMA). Cross-layer schedulers have been developed in order to describe the procedure of resource allocation in OFDMA wireless networks. The resource allocation in OFDMA wireless networks has received great attention in research, by proposing many different ways for frequency diversity exploitation and system’s optimization. Many cross-layer proposals for dynamic resource allocation have been investigated in literature approaching the optimization problem from different viewpoints i.e. maximizing total data rate, minimizing total transmit power, satisfying minimum users’ requirements or providing fairness amongst users. The design of a cross-layer scheduler for OFDMA wireless networks is the topic of this research. The scheduler utilizes game theory in order to make decisions for subcarrier and power allocation to the users with the main concern being to maintain fairness as well as to maximize overall system’s performance. A very well known theorem in cooperative game theory, the Nash Bargaining Solution (NBS), is employed and solved in a close form way, resulting in a Pareto optimal solution. Two different cases are proposed. The first one is the symmetric NBS (S-NBS) where all users have the same weight and therefore all users have the same opportunity for resources and the second one, is the asymmetric NBS (A-NBS), where users have different weights, hence different priorities where the scheduler favours users with higher priorities at expense of lower priority users. As MAC layer is vital for cross-layer, the scheduler is combined with a queuing model based on Markov chain in order to describe more realistically the incoming procedure from the higher layers