Arezou Mohammadi

Optimal linear-time algorithm for uplink scheduling of packets with hard deadlines in WiMAX

This paper focuses on a recent application of real-time scheduling in wireless communications industry; i.e., uplink scheduling for WiMAX systems. We address the problem of maximizing the number of packets to be sent in uplink such that the expectations from the system are guaranteed. In this work, we present, for the first time, a formal model for the general problem. Then, we use the properties of the application and derive an algorithm which has two highly favorable features: it finds the optimal solution in linear time. Finally, we present a method to fine-tune our general model in order to make sure that the model represents the actual system. This approach guarantees that the optimal algorithm for the model is indeed the optimal scheduler for the system.

Optimal linear-time QoS-based scheduling for WiMAX

We address a recent application of realtime scheduling in wireless communication industry; namely, the uplink scheduling problem for WiMAX systems. More specially, we have worked on the problem of maximizing the number of data packets to be sent through an uplink subframe such that the expectations from the system are guaranteed. We argue that this problem is NP-hard. Thus far, only a number of heuristic algorithms have been developed for special cases of the problem and the problem has not been modeled formally. In this work, we present two formal models for the system. Then, we derive an algorithm for uplink scheduling which has two highly favourable features: it finds the optimal solution in linear time.

Optimal Linear-Time Algorithm for Uplink Scheduling of Packets with Hard or Soft Deadlines in WiMAX

We present, for the first time, a formal model for the general problem of uplink scheduling of a set of packets with various QoS classes and soft or hard deadlines. Our goal is to maximize the number of packets to be sent in uplink such that the expectations from the system are guaranteed. We use our general model and the properties of the application to derive an algorithm which has two highly favorable features: it finds the optimal solution in linear time. Finally, we present a method to fine-tune our general model in order to make sure that the model represents the actual system. This approach guarantees that the optimal algorithm for the model is indeed the optimal scheduler for the system.

Number of processors with partitioning strategy and EDF-schedulability test: upper and lower bounds with comparison

In this paper, we study the problem of scheduling a set of n periodic preemptive independent hard real-time tasks on the minimum number of processors. We assume that the partitioning strategy is used to allocate the tasks to the processors and the EDF method is used to schedule the tasks on each processor. It is known that this scenario is NPhard; thus, it is unlikely to find a polynomial time algorithm to schedule the tasks on the minimum number of processors. In this work, we derive a lower and an upper bound for the number of processors required to satisfy the constraints of our problem. We also compare a number of heuristic algorithms with each other and with the bounds derived in this paper. Numerical results demonstrate that our lower bound is very tight and it is very close to the optimal solution.

QoS-based optimal logarithmic-time uplink scheduling algorithm for packets with hard or soft deadlines in WiMAX

We present, for the first time, a formal model for the general problem of uplink scheduling of a set of packets with various QoS classes and soft or hard deadlines. Our goal is to maximize the value of packets to be sent in uplink such that the expectations from the system are guaranteed. We use our general model and the properties of the application to derive an algorithm which has two highly favorable features: it finds the globally optimal solution in logarithmic time. We also present a method to fine-tune our general model. This approach guarantees that the optimal algorithm for the model is indeed the optimal scheduler for the system. Simulation results demonstrate the effectiveness of our algorithms.