We consider a general polling model with @math stations. The stations are served exhaustively and in cyclic order. Once a station queue falls empty, the server does not immediately switch to the next station. Rather, it waits at the station for the possible arrival of new work ("wait-and-see") and, in the case of this happening, it restarts service in an exhaustive fashion. The total time the server waits idly is set to be a fixed, deterministic parameter for each station. Switchover times and service times are allowed to follow some general distribution, respectively. In some cases, which can be characterised, this strategy yields strictly lower average queueing delay than for the exhaustive strategy, which corresponds to setting the "wait-and-see credit" equal to zero for all stations. This extends results of Pek "oz (Probability in the Engineering and Informational Sciences 13 (1999)) and of (Annals of Operations Research 112 (2002)). Furthermore, we give a lower bound for the delay for all strategies that allow the server to wait at the stations even though no work is present.
The strategy employed in @cite_13 and in the present paper is somehow related to a so-called forced idle time . We refer e.g. to @cite_7 @cite_5 for some work on this. However, in the present setup, the server is not forced to be idle; whenever it is set to wait-and-see'', it rather resumes service as soon as new messages arrive. This is the reason we prefer the term wait-and-see'' rather than forced idle time''.
@cite_5 @cite_13 @cite_7
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We consider two-queue polling models with the special feature that a timer mechanism is employed at Q1: whenever the server polls Q1 and finds it empty, it activates a timer and remains dormant, waiting for the first arrival. If such an arrival occurs before the timer expires, a busy period starts in accordance with Q1's service discipline. However, if the timer is shorter than the interarrival time to Q1, the server does not wait any more and switches back to Q2. We consider three configurations: (i) Q1 is controlled by the 1-limited protocol while Q2 is served exhaustively, (ii) Q1 employs the exhaustive regime while Q2 follows the 1-limited procedure, and (iii) both queues are served exhaustively. In all cases, we assume Poisson arrivals and allow general service and switchover time distributions. Our main results include the queue length distributions at polling instants, the waiting time distributions and the distribution of the total workload in the system. In this paper,we consider two-queue polling model with a Timer and a Randomly-Timed Gated(RTG) mechanism.At queue Q1,we employ a Timer T(1):whenever the server polls queue Q1 and finds it empty,it activates a Timer.If a customer arrives before the Timer expires,a busy period starts in accordance with exhaustive service discipline.However,if the Timer is shorter than the interarrival time to queue Q1,the server does not wait any more and switches back to queue Q2.At queue Q2,we operate a RTG mechanism T(2),that is,whenever the server reenters queue Q2,an exponential time T(2) is activated.If the server empties the queue before T(2),it immediately leaves for queue Q1.Otherwise,the server completes all the work accumulated up to time T(2) and leaves.Under the assumption of Poisson arrivals,general service and switchover time distributions,we obtain probability generating function(PGF) of the queue lengths at polling instant and mean cycle length and Laplace Stieltjes transform(LST) of the workload. Recent computer systems research has proposed using redundant requests to reduce latency. The idea is to replicate a request so that it joins the queue at multiple servers. The request is considered complete as soon as any one of its copies completes. Redundancy allows us to overcome server-side variability–the fact that a server might be temporarily slow due to factors such as background load, network interrupts, and garbage collection to reduce response time. In the past few years, queueing theorists have begun to study redundancy, first via approximations, and, more recently, via exact analysis. Unfortunately, for analytical tractability, most existing theoretical analysis has assumed an Independent Runtimes (IR) model, wherein the replicas of a job each experience independent runtimes (service times) at different servers. The IR model is unrealistic and has led to theoretical results that can be at odds with computer systems implementation results. This paper introduces a much more realistic model of redundancy. Our model decouples the inherent job size ( @math ) from the server-side slowdown ( @math ), where we track both @math and @math for each job. Analysis within the @math model is, of course, much more difficult. Nevertheless, we design a dispatching policy, Redundant-to-Idle-Queue, which is both analytically tractable within the @math model and has provably excellent performance. In a Fork-Join (FJ) queueing system an upstream fork station splits incoming jobs into N tasks to be further processed by N parallel servers, each with its own queue; the response time of one job is determined, at a downstream join station, by the maximum of the corresponding tasks' response times. This queueing system is useful to the modelling of multi-service systems subject to synchronization constraints, such as MapReduce clusters or multipath routing. Despite their apparent simplicity, FJ systems are hard to analyze. This paper provides the first computable stochastic bounds on the waiting and response time distributions in FJ systems. We consider four practical scenarios by combining 1a) renewal and 1b) non-renewal arrivals, and 2a) non-blocking and 2b) blocking servers. In the case of non blocking servers we prove that delays scale as O(logN), a law which is known for first moments under renewal input only. In the case of blocking servers, we prove that the same factor of log N dictates the stability region of the system. Simulation results indicate that our bounds are tight, especially at high utilizations, in all four scenarios. A remarkable insight gained from our results is that, at moderate to high utilizations, multipath routing 'makes sense' from a queueing perspective for two paths only, i.e., response times drop the most when N = 2; the technical explanation is that the resequencing (delay) price starts to quickly dominate the tempting gain due to multipath transmissions.