The average cost of operating a queueing network depends on several factors such as the complexity of the network and the service policy used. Approximate linear programming (ALP) is a method that can be used to compute an accurate lower bound on the optimal average cost as well as generate policies to be used in operating the network. These average cost solutions and policies are dependent on the type of basis function used in the ALP. In this paper, the ALP average cost solutions and policies are analyzed for twelve networks with four different types of basis functions (quadratic, linear, pure exponential, and mixed exponential). An approximate bound on the optimality gap between the ALP average cost solution and the optimal average cost solution is computed for each system, and the size of this bound is determined relative to the ALP average cost solution. Using the same set of networks, the performance of ALP generated policies are compared to the performance of the heuristic policies first-buffer-first-served (FBFS), last-buffer-first-served (LBFS), highest-queue-first-served (HQFS), and random-queue-first-served (RQFS). In general, ALP generated average cost solutions are considerably smaller than the simulated average cost under the corresponding policy, and therefore the approximate bounds on the optimality gaps are quite large. This bound increases with the complexity of the queueing network. Some ALP generated policies are not stabilizing policies for their corresponding networks, especially those produced using pure exponential and mixed exponential basis functions. For almost all systems, at least one of the heuristic policies results in mean average cost less than or nearly equal to the smallest mean average cost of all ALP generated policies in simulation runs. This means that generally there exists a heuristic policy which can perform as well as or better than any ALP generated policy. In conclusion, a useful bound on the optimality gap between the ALP average cost solution and the optimal average cost solution cannot be computed with this method. Further, heuristic policies, which are more computationally tractable than ALP generated policies, can generally match or exceed the performance of ALP generated policies, and thus computing such policies is often unnecessary for realizing cost benefits in queueing networks. / text

Scheduling policies adopted for statistical multiplexing should provide delay differentiation between different traffic classes, where each class represents an aggregate traffic of individual applications having same target-queueing-delay requirements. We propose scheduling to optimally balance weighted mean instanteneous queue lengths and later weighted mean cumulative queue lengths as an approach to delay differentiation, where the class weights are set inversely proportional to the respective products of target delays and packet arrival rates. In particular, we assume a discrete-time, two-class, single-server queueing model with unit service time per packet and provide mathematical frame-work throughout our work. For iid Bernoulli packet arrivals, using a step-wise cost-dominance analytical approach using instantaneous queue lengths alone, for a class of one-stage cost functions not necessarily convex, we find the structure of the total-cost optimal policies for a part of the state space. We then consider two particular one-stage cost functions for finding two scheduling policies that are total-cost optimal for the whole state-space. The policy for the absolute weighted difference cost function minimizes the stationary mean, and the policy for the weighted sum-of-square cost function minimizes the stationary second-order moment, of the absolute value of the weighted difference of queue lengths. For the case of weighted sum-of-square cost function, the ‘iid Bernoulli arrivals’ assumption can be relaxed to either ‘iid arrivals with general batch sizes’ or to ‘Markovian zero-one arrivals’ for all of the state space, but for the linear switching curve. We then show that the average cost, starting from any initial state, exists, and is finite for every stationary work-conserving policy for our choices of the one-stage cost-function. This is shown for arbitrary number of class queues and for any i.i.d. batch arrival processes with finite appropriate moments. We then use cumulative queue lengths information in the one-step cost function of the optimization formulation and obtain an optimal myopic policy with 3 stages to go for iid arrivals with general batch sizes. We show analytically that this policy achieves the given target delay ratio in the long run under finite buffer assumption, given that feasibility conditions are satisfied. We take recourse to numerical value iteration to show the existence of average-cost for this policy. Simulations with varied class-weights for Bernoulli arrivals and batch arrivals with Poisson batch sizes show that this policy achieves mean queueing delays closer to the respective target delays than the policy obtained earlier. We also note that the coefficients of variation of the queueing delays of both the classes using cumulative queue lengths are of the same order as those using instantaneous queue lengths. Moreover, the short-term behaviour of the optimal myopic policy using cumulative queue lengths is superior to the existing standard policy reported by Coffman and Mitrani by a factor in the range of 3 to 8. Though our policy performs marginally poorer compared to the value-iterated, sampled, and then stationarily employed policy, the later lacks any closed-form structure. We then modify the definition of the third state variable and look to directly balance weighted mean delays. We come up with another optimal myopic policy with 3 stages to go, following which the error in the ratio of mean delays decreases as the window-size, as opposed to the policy mentioned in the last paragraph, wherein the error decreases as the square-root of the window-size. We perform numerical value-iteration to show the existence of average-cost and study the performance by simulation. Performance of our policy is comparable with the value-iterated, sampled, and then stationarily employed policy, reported by Mallesh. We have then studied general inter-arrival time processes and obtained the optimal myopic policy for the Pareto inter-arrival process, in particular. We have supported with simulation that our policy fares similarly to the PAD policy, reported by Dovrolis et. al., which is primarily heuristic in nature. We then model the possible packet errors in the multiplexed channel by either a Bernoulli process, or a Markov modulated Bernoulli process with two possible channel states. We also consider two possible round-trip-time values for control information, namely zero and one-slot. The policies that are next-stage optimal (for zero round-trip-time), and two-stage optimal (for one-slot round-trip-time) are obtained. Simulations with varied class-weights for Bernoulli arrivals and batch arrivals with Poisson batch sizes show that these policies indeed achieve mean queueing delays very close to the respective target delays. We also obtain the structure for optimal policies with N = 2 + ⌈rtt⌉ stages-to-go for generic values of rtt, and which need not be multiple of time-slots.