Delay analysis for wireless applications using a multiservice multiqueue processor sharing model

The ongoing development of wireless networks supporting multimedia applications requires service providers to efficiently deliver complex Quality of Service (QoS) requirements. The wide range of new applications in these networks significantly increases the difficulty of network design and dimensioning to meet QoS requirements. Medium Access Control (MAC) protocols affect QoS achieved by wireless networks. Research on analysis and performance evaluation is important for the efficient protocol design. As wireless networks feature scarce resources that are simultaneously shared by all users, processor sharing (PS) models were proposed for modelling resource sharing mechanisms in such systems. In this thesis, multi-priority MAC protocols are proposed for handling the various service traffic types. Then, an investigation of multiservice multiqueue PS models is undertaken to analyse the delay for some recently proposed wireless applications. We start with an introduction to MAC protocols for wireless networks which are specified in IEEE standards and then review scheduling algorithms which were proposed to work with the underlying MAC protocols to cooperatively achieve QoS goals. An overview of the relevant literature is given on PS models for performance analysis and evaluation of scheduling algorithms. We propose a multiservice multiqueue PS model using a scheduling scheme in multimedia wireless networks with a comprehensive description of the analytical solution. Firstly, we describe the existing multiqueue processor sharing (MPS) model, which uses a fixed service quantum at each queue, and correct a subtle incongruity in previous solutions presented in the literature. Secondly, a new scheduling framework is proposed to extend the previous MPS model to a general case. This newly proposed analytical approach is based on the idea that the service quantum arranged by a MAC scheduling controller to service data units can be priority-based. We obtain a closed-form expression for the mean delay of each service class in this model. In summary, our new approach simplifies MAC protocols for multimedia applications into an analytical model that includes more complex and realistic traffic models without compromising details of the protocol and significantly reduces the number of MAC headers, thus the overall average delay will be decreased. In response to using the studied multiservice multiqueue PS models, we apply the MPS model to two wireless applications: Push to Talk (PTT) service over GPRS/GSM networks and the Worldwide Interoperability for Microwave Access (WiMAX) networks. We investigate the uplink delay of PTT over traditional GPRS/GSM networks and the uplink delay for WiMAX Subscriber Station scheduler under a priority-based fair scheduling. MAC structures capable of supporting dynamically varying traffic are studied for the networks, especially, with the consideration of implementation issues. The model provides useful insights into the dynamic performance behaviours of GPRS/GSM and WiMAX networks with respect to various system parameters and comprehensive traffic conditions. We then evaluate the model under some different practical traffic scenarios. Through modelling of the operation of wireless access systems, under a variety of multimedia traffic, our analytical approaches provide practical analysis guidelines for wireless network dimensioning

Resource allocation in wireless access network : A queueing theoretic approach

To meet its performance targets, the future 5G networks need to greatly optimize the Radio Access Networks (RANs), which connect the end users to the core network. In this thesis, we develop mathematical models to study three aspects of the operation of the RAN in modern wireless systems. The models are analyzed using  the techniques borrowed mainly from queueing theory and stochastic control. Also, simulations are extensively used to gain further insights. First, we provide a detailed Markov model of the random access process in LTE. From this, we observe that the bottleneck in the signaling channel causes congestion in the  access  when a large number of M2M devices attempt to enter the network. Then, in the context of the so-called Heterogeneous networks (HetNets), we suggest  dynamic load balancing schemes that alleviate this congestion and reduce the overall access delay. We then use flow-level models for elastic data traffic to study the problem of coordinating the activities of the neighboring base stations.  We seek to minimize the flow-level delay when there are various classes of users. We classify the users based on their locations, or, in dynamic TDD systems, on the direction of service the network is providing to them. Using interacting queues and different operating policies of running such queues, we study the amount of gain the dynamic policies can provide over the static probabilistic policies. Our results show that simple dynamic policies can  provide very good performance in the cases considered. Finally, we consider the problem of opportunistically scheduling the flows of users with time-varying channels  taking into account   the size of data they need to transfer. Using flow-level models in a system with homogeneous channels, we provide the optimal scheduling policy when there are  no new job arrivals. We also suggest the method to implement such a policy in a time-slotted system. With heterogeneous channels, the problem is intractable for the flow-level techniques. Therefore, we utilize the framework of the restless-multi-armed-bandit (RMAB) problems employing the so-called Whittle index approach. The Whittle index approach, by relaxing the scheduling constraints, makes the problem separable, and thereby provides an exact solution to the modified problem. Our simulations suggest that when  this solution is applied as a heuristic to the original problem, it gives good performance, even with dynamic job arrivals

Resource Management and Pricing in Networks

Resource management is important for network design and deployment. Resource management and allocation have been studied under a wide variety of scenarios --- routing in wired networks, scheduling in cellular networks, multiplexing, switching, and channel access in opportunistic networks are but a few examples. In this dissertation, we revisit resource management in the context of routing and scheduling in multihop wireless networks and pricing in single resource systems. The first issue addressed is of delays in multihop wireless networks. The resource under contention is capacity which is allocated by a joint routing and scheduling algorithm. Delay in wireless networks is a key issue gaining interest with the growth of interactive applications and proliferation of wireless networks. We start with an investigation of the back-pressure algorithm (BPA), an algorithm that activates the schedule with the largest sum of link weights in a timeslot. Though the BPA is throughput-optimal, it has poor end-to-end delays. Our investigation identifies poor routing decisions at low loads as one cause for it. We improve the delay performance of max-weight algorithms by proposing a general framework for routing and scheduling algorithms that allow directing packets towards the sink node dynamically. For a stationary environment, we explicitly formulate delay minimization as a static problem while maintaining stability. We see similar improved delay performance with the advantage of reduced per time-slot complexity. Next, the issue of pricing for flow based models is studied. The increasing popularity of cloud computing and the ease of commerce over the Internet is making pricing a key issue requiring greater attention. Although pricing has been extensively studied in the context of maximizing revenue and fairness, we take a different perspective and investigate pricing with predictability. Prior work has studied resource allocations that link insensitivity and predictability. In this dissertation, we present a detailed analysis of pricing under insensitive allocations. We study three common pricing models --- fixed rate pricing, Vickrey-Clarke-Groves (VCG) auctions, and congestion-based pricing, and provide the expected operator revenue and user payments under them. A pre-payment scheme is also proposed where users pay on arrival a fee for their estimated service costs. Such a mechanism is shown to have lower variability in payments under fixed rate pricing and VCG auctions while generating the same long-term revenue as in a post-payment scheme, where users pay the exact charge accrued during their sojourn. Our formulation and techniques further the understanding of pricing mechanisms and decision-making for the operator

Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

Scheduling for today’s computer systems: bridging theory and practice

Scheduling is a fundamental technique for improving performance in computer systems. From web servers to routers to operating systems, how the bottleneck device is scheduled has an enormous impact on the performance of the system as a whole. Given the immense literature studying scheduling, it is easy to think that we already understand enough about scheduling. But, modern computer system designs have highlighted a number of disconnects between traditional analytic results and the needs of system designers. In particular, the idealized policies, metrics, and models used by analytic researchers do not match the policies, metrics, and scenarios that appear in real systems. The goal of this thesis is to take a step towards modernizing the theory of scheduling in order to provide results that apply to today’s computer systems, and thus ease the burden on system designers. To accomplish this goal, we provide new results that help to bridge each of the disconnects mentioned above. We will move beyond the study of idealized policies by introducing a new analytic framework where the focus is on scheduling heuristics and techniques rather than individual policies. By moving beyond the study of individual policies, our results apply to the complex hybrid policies that are often used in practice. For example, our results enable designers to understand how the policies that favor small job sizes are affected by the fact that real systems only have estimates of job sizes. In addition, we move beyond the study of mean response time and provide results characterizing the distribution of response time and the fairness of scheduling policies. These results allow us to understand how scheduling affects QoS guarantees and whether favoring small job sizes results in large job sizes being treated unfairly. Finally, we move beyond the simplified models traditionally used in scheduling research and provide results characterizing the effectiveness of scheduling in multiserver systems and when users are interactive. These results allow us to answer questions about the how to design multiserver systems and how to choose a workload generator when evaluating new scheduling designs