Different applications of communication networks have different requirements that depend on the type of application. We consider the problem of differentiating between delay-sensitive applications based on their average delay requirements, as may be of interest in signalling networks. We consider packets of different classes that are to be transmitted on the same link with different average delay requirements, to reside in separate queues with the arrival statistics for the queues being specified. This statistical multiplexer has to schedule packets from different queues in so that the average delays of the queues approach the specified target delays as quickly as possible. For simplicity, we initially consider a discrete-time model with two queues and a single work-conserving server, with independent Bernoulli packet arrivals and unit packet service times. With arrival rates specified, achieving mean queue lengths in a ratio which corresponds to the ratio of target mean delays is a means of achieving individual target mean delays. We formulate the problem in the framework of Markov decision theory. We study two scheduling policies called Queue Length Balancing and Delay Balancing respectively, and show through numerical computation that the expectation of magnitude of relative error in θ (1/m) and θ (1/√m) respectively, and that the expectation of the magnitude of relative error in weighted average delays decays as θ (1/√m) and θ (1/m) respectively, where m is the averaging interval length. We then consider the model for an arbitrary number of queues each with i.i.d. batch arrivals, and analyse the errors in the average delays of individual queues. We assume that the fifth moment of busy period is finite for this model. We show that the expectation of the absolute value of error in average queue length for at least one of the queues decays at least as slowly as θ (1/√m), and that the mean squared error in queue length for at least one of the queues decays at least as slowly as θ (1/m). We show that the expectation of the absolute value of error in approximating Little’s law for finite horizon is 0 (1/m). Hence, we show that the mean squared error in delay for at least one of the queues decays at least slowly as θ (1/m). We also show that if the variance of error in delay decays for each queue, then the expectation of the absolute value of error in delay for at least one of the queues decays at least as slowly as θ (1/√m).

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.