First-come, first-served

About: First-come, first-served is a research topic. Over the lifetime, 111 publications have been published within this topic receiving 1442 citations.

On optimal batching policies for video-on-demand storage servers

Abstract: In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are first-come-first-served (FCFS) and maximum queue length (MOL). Neither of these policies lead to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by only considering the queue length to maximize batch size, while FCFS has the opposite effect. We introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQ policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve the best weighting factors for the various videos. A simulation is developed to compare the proposed MFQ policy with FCFS and MQL. Our study shows that MFQ yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates and fairness.

The M/G/1 queue with negative customers

Abstract: We derive expressions for the generating function of the equilibrium queue length probability distribution in a single server queue with general service times and independent Poisson arrival streams of both ordinary, positive customers and negative customers which eliminate a positive customer if present. For the case of first come first served queueing discipline for the positive customers, we compare the killing strategies in which either the last customer in the queue or the one in service is removed by a negative customer. We then consider preemptive-restart with resampling last come first served queueing discipline for the positive customers, combined with the elimination of the customer in service by a negative customer- the case of elimination of the last customer yields an analysis similar to first come first served discipline for positive customers. The results show different generating functions in contrast to the case where service times are exponentially distributed. This is also reflected in the stability conditions. Incidently, this leads to a full study of the preemptive-restart with resampling last come first served case without negative customers. Finally, approaches to solving the Fredholm integral equation of the first kind which arises, for instance, in the first case are considered as well as an alternative iterative solution method.

On Optimizing Reservation-Based Intersection Controls

Abstract: Tile-based reservation intersection control for autonomous vehicles has the potential to reduce intersection delays beyond optimized traffic signals. A major question in implementing reservations is the underdetermined problem of resolving conflicting reservation requests. Previous work studied prioritizing requests by first come first served or holding auctions at intersections, but the possibilities are infinite. Furthermore, although selfish routing behavior could affect the benefits of the reservation prioritization, reservation control has not been studied with user equilibrium routing due to its microsimulation definition. This paper addresses these issues by presenting an integer program formulation of the conflict point simplification of reservations. The feasible region is transformed, resulting in a more tractable integer program on conflict regions for dynamic traffic assignment. Because the integer program is NP-hard we present a polynomial-time heuristic. Finally, we demonstrate the potential utility of this heuristic by demonstrating objective functions that reduce travel time and energy consumption on a city network.

Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

Abstract: The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.