The second major cloud outage in less than two weeks, Azure's downtime highlights the "brittleness" of a digital ecosystem that depends on a few companies never making mistakes. Microsoft's Azure cloud platform, its widely used 365 services, Xbox, and Minecraft started suffering outages at roughly noon Eastern time on Wednesday, the result of what Microsoft said was "an inadvertent configuration change." The incident--which marks the second major cloud provider outage in less than two weeks--highlights the instability of an internet built largely on infrastructure run by a few tech giants. Microsoft's problems specifically originated from Azure's Front Door content delivery network and emerged just hours before Microsoft's scheduled earnings announcement. The company website, including its investor relations page, was still down on Wednesday afternoon, and the Azure status page where Microsoft provides updates was having intermittent issues as well.

Amazon Web Services experienced DNS resolution issues on Monday morning, taking down wide swaths of the web--and highlighting a longstanding weakness in the internet's infrastructure. A massive cloud outage stemming from Amazon Web Services's key US-EAST-1 region, its hub near the United States capitol in northern Virginia, caused widespread disruptions of websites and platforms around the world on Monday morning. Amazon's main e-commerce platform and other properties including Ring doorbells and the Alexa smart assistant suffered interruptions and outages throughout the morning, as did Meta's communication platform WhatsApp, OpenAI's ChatGPT, PayPal's Venmo payment platform, multiple web services from Epic Games, multiple British government sites, and many others. The outages stemmed from Amazon's "DynamoDB" database application programming interfaces in US-EAST-1, and AWS said in status updates that the problem was specifically related to DNS resolution issues. The "Domain Name System" is a foundational internet service that essentially acts as an automatic phonebook lookup to translate web URLs like "www.wired.com"

We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI performance at the destination. This tradeoff has been optimized in the existing literature under the assumption of having a complete knowledge of the channel statistics. In this work, we develop online learning-based algorithms with finite-time guarantees that optimize this tradeoff in the practical scenario where the channel statistics are unknown to the scheduler. In particular, when the channel statistics are known, the optimal scheduling policy is first proven to have a threshold-based structure with respect to the value of AoI (i.e., it is optimal to drop updates when the AoI value is below some threshold). This key insight was then utilized to develop the proposed learning algorithms that surprisingly achieve an order-optimal regret (i.e., $O(1)$) with respect to the time horizon length.

Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants. While the outsourcing of human decision-making to AI can be both efficient and effective, it raises a fundamental question: how does delegating identity-defining choices to AI reshape who people become? We study the impact of agentic LLMs on two identity-relevant outcomes: interpersonal distinctiveness - how unique a person's choices are relative to others - and intrapersonal diversity - the breadth of a single person's choices over time. Using real choices drawn from social-media behavior of 1,000 U.S. users (110,000 choices in total), we compare a generic and personalized agent to a human baseline. Both agents shift people's choices toward more popular options, reducing the distinctiveness of their behaviors and preferences. While the use of personalized agents tempers this homogenization (compared to the generic AI), it also more strongly compresses the diversity of people's preference portfolios by narrowing what they explore across topics and psychological affinities. Understanding how AI agents might flatten human experience, and how using generic versus personalized agents involves distinctiveness-diversity trade-offs, is critical for designing systems that augment rather than constrain human agency, and for safeguarding diversity in thought, taste, and expression.

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

In compute-first networking, maintaining fresh and accurate status information at the network edge is crucial for effective access to remote services. This process typically involves three phases: Status updating, user accessing, and user requesting. However, current studies on status effectiveness, such as Age of Information at Query (QAoI), do not comprehensively cover all these phases. Therefore, this paper introduces a novel metric, TPAoI, aimed at optimizing update decisions by measuring the freshness of service status. The stochastic nature of edge environments, characterized by unpredictable communication delays in updating, requesting, and user access times, poses a significant challenge when modeling. To address this, we model the problem as a Markov Decision Process (MDP) and employ a Dueling Double Deep Q-Network (D3QN) algorithm for optimization. Extensive experiments demonstrate that the proposed TPAoI metric effectively minimizes AoI, ensuring timely and reliable service updates in dynamic edge environments. Results indicate that TPAoI reduces AoI by an average of 47\% compared to QAoI metrics and decreases update frequency by an average of 48\% relative to conventional AoI metrics, showing significant improvement.

Popular methods to quantify transmitted data quality are the Age of Information (AoI), the Query Age of Information (QAoI), and the Age of Incorrect Information (AoII). We consider these metrics in a point-to-point wireless communication system, where the transmitter monitors a process and sends status updates to a receiver. The challenge is to decide on the best time for an update, balancing the transmission energy and the age-based metric at the receiver. Due to the inherent risk of high age-based metric values causing complications such as unstable system states, we introduce the new concept of risky states to denote states with high age-based metric. We use this new notion of risky states to quantify and minimize this risk of experiencing high age-based metrics by directly deriving the frequency of risky states as a novel risk-metric. Building on this foundation, we introduce two risk-sensitive strategies for AoI, QAoI and AoII. The first strategy uses system knowledge, i.e., channel quality and packet arrival probability, to find an optimal strategy that transmits when the age-based metric exceeds a tunable threshold. A lower threshold leads to higher risk-sensitivity. The second strategy uses an enhanced Q-learning approach and balances the age-based metric, the transmission energy and the frequency of risky states without requiring knowledge about the system. Numerical results affirm our risk-sensitive strategies' high effectiveness.

This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair that is jointly trained while considering channel effects. The encoder converts data samples into feature vectors of small dimension and transmits them with a small number of channel uses thereby reducing the number of transmissions and latency. Instead of reconstructing input samples, the decoder performs a task, e.g., classification, on the received signals. Applying different deep neural networks of encoder-decoder pairs on MNIST and CIFAR-10 image datasets, the classifier accuracy is shown to increase with the number of channel uses at the expense of longer service time. The peak age of task information (PAoTI) is introduced to analyze this accuracy-latency tradeoff when the age grows unless a received signal is classified correctly. By incorporating channel and traffic effects, design guidelines are obtained for task-oriented communications by characterizing how the PAoTI first decreases and then increases with the number of channel uses. A dynamic update mechanism is presented to adapt the number of channel uses to channel and traffic conditions, and reduce the PAoTI in task-oriented communications.

The age of information metric fails to correctly describe the intrinsic semantics of a status update. In an intelligent reflecting surface-aided cooperative relay communication system, we propose the age of semantics (AoS) for measuring semantics freshness of the status updates. Specifically, we focus on the status updating from a source node (SN) to the destination, which is formulated as a Markov decision process (MDP). The objective of the SN is to maximize the expected satisfaction of AoS and energy consumption under the maximum transmit power constraint. To seek the optimal control policy, we first derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. However, implementing the online DAC in practice poses the key challenge in infinitely repeated interactions between the SN and the system, which can be dangerous particularly during the exploration. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system. Numerical experiments verify the theoretical results and show that our offline DAC scheme significantly outperforms the online DAC scheme and the most representative baselines in terms of mean utility, demonstrating strong robustness to dataset quality.

A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile. To this end, we model social media users by incorporating and reasoning over multiple sources of UGC as well as social relations. Our model is based on a statistical relational learning framework using Hinge-loss Markov Random Fields (HL-MRFs), a class of probabilistic graphical models that can be defined using a set of first-order logical rules. We validate our approach on data from Facebook with more than 5k users and almost 725k relations. We show how HL-MRFs can be used to develop a generic and extensible user profiling framework by leveraging textual, visual, and relational content in the form of status updates, profile pictures and Facebook page likes. Our experimental results demonstrate that our proposed model successfully incorporates multiple sources of information and outperforms competing methods that use only one source of information or an ensemble method across the different sources for modeling of users in social media.