Throughput of distributed queueing-based LoRa for long-distance communication

LoRa, due to its advantage of long-range communication capability, is promising for Internet of Things (IoT) and space-air-ground communications. However, the conventional MAC protocol used with LoRa is classified as an Aloha-based algorithm, which leads to drastic decrease in throughput when a huge amount of end-devices try to access the network. To achieve stable and high throughput of LoRa, we propose a design to combine the distributed queueing (DQ) and in-band-full-duplex (IBFD) technologies. The usage of DQ mechanism is benefit for fast collision resolution, while the IBFD-enabled gateway helps to reduce the heavy control overhead of DQ. The designs of access procedure and frame structure are discussed in detail. The outage probability and average throughput are evaluated under imperfect self-interference cancelation. Also, a mathematical programming method is developed to optimize the spreading factor and code rate. Numerical results show that our proposal gains an extra enhancement of 1.83-fold in throughput.

Throughput of Distributed Queueing Based LoRa for Long-Distance Communication

LoRa, due to its advantage of long-range communication capability, is promising for Internet of Things (IoT) and space-air-ground communications. However, the conventional MAC protocol used with LoRa is classified as an Aloha-based algorithm, which leads to drastic decrease in throughput when a huge amount of end-devices try to access the network. To achieve stable and high throughput of LoRa, we propose a design to combine the distributed queueing (DQ) and in-band-full-duplex (IBFD) technologies. The usage of DQ mechanism is benefit for fast collision resolution, while the IBFD-enabled gateway helps to reduce the heavy-control-overhead of DQ. The designs of access procedure and frame structure are discussed in detail. The outage probability and average throughput are evaluated under imperfect self-interference cancellation. Also, a mathematical programming method is developed to optimize the spreading-factor and code-rate. Numerical results show that our proposal gains an extra enhancement of 1.83-fold in throughput.

Stationary Waiting Time in Parallel Queues with Synchronization

Motivated by database locking problems in today’s massive computing systems, we analyze a queueing network with many servers in parallel (files) to which jobs (writing access requests) arrive according to a Poisson process. Each job requests simultaneous access to a random number of files in the database and will lock them for a random period of time. Alternatively, one can think of a queueing system where jobs are split into several fragments that are then randomly routed to specific servers in the network to be served in a synchronized fashion. We assume that the system operates on a first-come, first-served basis. The synchronization and service discipline create blocking and idleness among the servers, which leads to a strict stability condition compared with other distributed queueing models. We analyze the stationary waiting time distribution of jobs under a many-server limit and provide exact tail asymptotics. These asymptotics generalize the celebrated Cramér–Lundberg approximation for the single-server queue.

Power-of-d-Choices with Memory: Fluid Limit and Optimality

In multiserver distributed queueing systems, the access of stochastically arriving jobs to resources is often regulated by a dispatcher, also known as a load balancer. A fundamental problem consists in designing a load-balancing algorithm that minimizes the delays experienced by jobs. During the last two decades, the power-of-d-choice algorithm, based on the idea of dispatching each job to the least loaded server out of d servers randomly sampled at the arrival of the job itself, has emerged as a breakthrough in the foundations of this area because of its versatility and appealing asymptotic properties. In this paper, we consider the power-of-d-choice algorithm with the addition of a local memory that keeps track of the latest observations collected over time on the sampled servers. Then, each job is sent to a server with the lowest observation. We show that this algorithm is asymptotically optimal in the sense that the load balancer can always assign each job to an idle server in the large-system limit. This holds true if and only if the system load λ is less than [Formula: see text]. If this condition is not satisfied, we show that queue lengths are bounded by [Formula: see text]. This is in contrast with the classic version of the power-of-d-choice algorithm, in which, at the fluid scale, a strictly positive proportion of servers containing [Formula: see text] jobs exists for all [Formula: see text] in equilibrium. Our results quantify and highlight the importance of using memory as a means to enhance performance in randomized load balancing.