We study the scheduling problem for a multi-source single-servergenerate-at-will(GAW) status update system with sources having heterogeneous service times and weights, for which the goal is to minimize the system age of information (AoI), or system peak AoI (PAoI), by employing scheduling algorithms with low runtime complexity. Here, system AoI/PAoI refers to weighted sum of the average AoI/PAoI values of information sources. In particular, we focus on open-loopcyclic schedulerswithO(1) runtime complexity, where status updates are scheduled according to a fixed finite transmission pattern whose construction is the main scope of this paper. We first develop an analytical method to obtain the exact average AoI/PAoI of the sources when a transmission pattern is given. Subsequently, we derive the optimum transmission pattern for system AoI in closed form, for the specific case of two sources. For general number of sources, a novel method is proposed based on adeletion search(DS) based algorithm which constructs a pattern whose system PAoI can be brought arbitrarily close to the minimum system PAoI that is attainable using open-loop scheduling. Using another outcome of the same DS-based algorithm, a heuristic scheduler is proposed for system AoI minimization, which is shown to outperform various existing age-agnostic schedulers in the literature, including theinsertion search(IS) based algorithm, in the majority of the examples we studied.

We consider a cache update system with a remote server delivering time-varying contents of multiple Internet of Things (IoT) items with heterogeneous popularities and service times to a local cache so as to keep the items as fresh as possible at the cache. New content for an item arrives at the server according to a Poisson process and the server is equipped with multiple queues each of which holds the most up-to-date content for the corresponding item. In this setting, we study several scheduling policies employed at the server so as to maximize the popularity-weighted binary freshness across the items. The scheduling problem is first formulated as an infinite-horizon average-reward Markov decision process (MDP) which suffers from the curse of dimensionality when the number of items is large. We then propose a water-filling-based scheduling (WFS) policy and its extension, namely, extended WFS (E-WFS) policy, with worst case complexities being quadratic and cubic in the number of items, respectively, based on convex optimization applied to a relaxation of the original system. Simulation results are provided to validate the effectiveness of the proposed policies.

We study a multisource status update system with Poisson information packet arrivals and exponentially distributed service times. The server is equipped with a waiting room holding the freshest packet from each source referred to as single buffer per-source queueing (SBPSQ). The sources are assumed to be equally important, i.e., (nonweighted) average Age of Information (AoI) or average age violation probability are used as the information freshness metrics to optimize for, and subsequently, two symmetric SBPSQ-based scheduling policies are studied in this article, namely, first source first serve (FSFS) and the earliest served first serve (ESFS) policies. By employing the theory of Markov fluid queues (MFQs), an analytical model is proposed to obtain the exact distribution of the AoI for each source when the FSFS and ESFS policies are employed at the server. Additionally, a benchmark scheduling-free scheme named single buffer with replacement (SBR), which uses a single buffer to hold the freshest packet across all sources, is also studied with a similar but less complex analytical model. We comparatively study the performance of the three policies through numerical examples in terms of the average AoI and the age violation probability averaged across all sources, in a scenario of sources possessing different traffic intensities but sharing a common service time.

A transmission profile refers to a transmission power and a modulation and coding scheme to be used for packet transmissions over a wireless link. The goal of this paper is to develop transmission profile selection policies so as to minimize the average power consumption on a wireless link while satisfying a certain delay constraint given in terms of a delay violation probability. Toward the assessment of profile selection policies, a multi-regime Markov fluid queue model is proposed to obtain the average power consumption and the queue waiting time distribution which allows one to analyze the energy-delay tradeoff in queuing systems for which the packet transmission duration is allowed to depend on the delay experienced by the packet until the beginning of service. Numerical examples are presented with transmission profiles obtained from realistic LTE simulations. Several transmission profile selection policies are proposed and subsequently compared using the analytical model.