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.

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.

Interference limits the throughput of modern wireless communication networks, and thus the successful mitigation of interference can have a significant impact on network performance. Successive interference cancellation (SIC) has emerged as a promising physical layer method, where multiple packets received simultaneously need not be treated as a ``collision'' requiring retransmission; rather, under certain conditions, all of the packets can be decoded. Obviously, using SIC can thus serve as an important design element that can provide higher performance for the network. However, it also requires a rethinking of the way that traditional routing and scheduling algorithms, which are designed for a traditional physical layer, are developed. In order to consider routing and scheduling over a physical layer employing SIC, some tools such as the oft-employed conflict graph need to be modified. In particular, a notion of links interfering with other links ``indirectly'' is required, and this issue has been ignored in many past works. Therefore, considering the dependencies and interferences between links, a joint routing and scheduling algorithm that employs an understanding of the SIC that will be employed at the physical layer is presented and shown to surpass previous algorithms. We know that the maximum throughput scheduling problem is NP-hard. On the other hand, even if we can reach maximum throughput scheduling, while being throughput efficient, it can result in highly unfair rates among the users. Hence, proportional fairness is developed in the proposed algorithm.

Scheduling allocates scarce resources to activities such that certain constraints are satisfied and specific objectives are optimized. The activities to be executed are com- monly known or determined a priori in the planning stage. To improve the flexibility of scheduling systems, the concept of optional activities was invented. Optional activities are those activities whose presence in the resulting schedule is to be decided. Rather than determining which activities need to be executed and scheduling them in two consecu- tive phases, flexibility and efficiency can be improved significantly when both activity selection and time allocation are integrated within the same solver. Such an approach was implemented in a few Constraint Programming solvers and manifested great perfor- mance on multiple scheduling problems. In this thesis, we apply the concept of optional activities to scheduling problems that do not seem to involve optional activities, such as the production scheduling problem with sequence-dependent non-overlapping setups, but also on problems beyond the scheduling domain, such as the multi-agent path finding problem and its extension with weighted and capacitated arcs. 1