The last decade has witnessed an unprecedented growth in the demand for data-driven real-time services. These services are fueled by emerging applications that require rapidly injecting data streams and computing updated analytics results in real-time. In many of such applications, the computing resources are often shared for processing both updates from information sources and queries from end users. This requires joint scheduling of updates and queries because the service provider needs to make a critical decision upon receiving a user query: either it responds immediately with currently available but possibly stale information, or it first processes new updates and then responds with fresher information. Hence, the tradeoff between service performance and information freshness naturally arises in this context. To that end, we propose a simple single-server two-queue model that captures the coupled scheduling of updates and queries and aim to design scheduling policies that can properly address the important tradeoff between performance and freshness. Specifically, we consider the response time as a performance metric and the Age of Information (AoI) as a freshness metric. After demonstrating the limitations of the simplest FCFS policy, we propose two threshold-based policies: the Query-k policy that prioritizes queries and the Update-k policy that prioritizes updates. Then, we rigorously analyze both the response time and the Peak AoI (PAoI) of the threshold-based policies. Further, we propose the Joint-(M,N) policy, which allows flexibly prioritizing updates or queries through choosing different values of two thresholds M and N. Finally, we conduct simulations to evaluate the response time and the PAoI of the proposed policies. The results show that our proposed threshold-based policies can effectively control the balance between performance and freshness.
The notion of AoI is formally introduced in @cite_14 , where the authors analyze the time average AoI in M M 1, M D 1, and D M 1 systems under the FCFS policy. Since this seminal work, the study on the AoI has attracted a lot of research interests. There is a large body of work that focuses on the analysis of the AoI under a number of queueing model. For example, the work of @cite_7 @cite_6 @cite_12 focuses on the model where the updates arrive according to the Poisson process and are served by a single server. There is another body of work that considers how to minimize the AoI by carefully designing scheduling policies in different scenarios (e.g., wireless networks @cite_15 @cite_9 and energy harvesting networks @cite_11 @cite_13 ). In @cite_22 , the authors propose the Pull model for investigating the expected AoI at the user's side and discover a new tradeoff between different levels of information freshness and different response times across the servers. Besides the above work that focuses on the analysis and optimization of the AoI, several other work also considers applications where the AoI is highly relevant (see, e.g., @cite_17 @cite_18 ).
@cite_18 @cite_14 @cite_22 @cite_7 @cite_9 @cite_17 @cite_6 @cite_15 @cite_13 @cite_12 @cite_11
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Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3 , an improvement with significant impact to the overall system performance. We then explore how a number of fundamental parameters impact the final prediction performance of our system. Not surprisingly, the most important thing is to have the right features: those capturing historical information about the user or ad dominate other types of features. Once we have the right features and the right model (decisions trees plus logistic regression), other factors play small roles (though even small improvements are important at scale). Picking the optimal handling for data freshness, learning rate schema and data sampling improve the model slightly, though much less than adding a high-value feature, or picking the right model to begin with. Increasingly ubiquitous communication networks and connectivity via portable devices have engendered a host of applications in which sources, for example people and environmental sensors, send updates of their status to interested recipients. These applications desire status updates at the recipients to be as timely as possible; however, this is typically constrained by limited network resources. In this paper, we employ a time-average age metric for the performance evaluation of status update systems. We derive general methods for calculating the age metric that can be applied to a broad class of service systems. We apply these methods to queue-theoretic system abstractions consisting of a source, a service facility and monitors, with the model of the service facility (physical constraints) a given. The queue discipline of first-come-first-served (FCFS) is explored. We show the existence of an optimal rate at which a source must generate its information to keep its status as timely as possible at all its monitors. This rate differs from those that maximize utilization (throughput) or minimize status packet delivery delay. While our abstractions are simpler than their real-world counterparts, the insights obtained, we believe, are a useful starting point in understanding and designing systems that support real time status updates. The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. Prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push" (i.e., generate and transmit) the updated information to the user. In stark contrast, in this paper we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull" the information of interest. Moreover, we propose to employ request replication to reduce the AoI. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process at the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Finally, we conduct numerical simulations to elucidate our theoretical results. Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios. We consider the problem of scheduling real-time traffic with hard deadlines in a wireless ad hoc network. In contrast to existing real-time scheduling policies that merely ensure a minimal timely throughput, our design goal is to provide guarantees on both the timely throughput and data freshness in terms of age-of-information (AoI), which is a newly proposed metric that captures the "age" of the most recently received information at the destination of a link. The main idea is to introduce the AoI as one of the driving factors in making scheduling decisions. We first prove that the proposed scheduling policy is feasibility-optimal, i.e., satisfying the per-traffic timely throughput requirement. Then, we derive an upper bound on a considered data freshness metric in terms of AoI, demonstrating that the network-wide data freshness is guaranteed and can be tuned under the proposed scheduling policy. Interestingly, we reveal that the improvement of network data freshness is at the cost of slowing down the convergence of the timely throughput. Extensive simulations are performed to validate our analytical results. Both analytical and simulation results confirm the capability of the proposed scheduling policy to improve the data freshness without sacrificing the feasibility optimality. Recent advances in vehicular networks have enforced researchers to focus on various information dissemination techniques. Exchanging information among the vehicles is imperative due to the ever-changing network topology in vehicular networks. However, random transmitter selection in traditional CSMA based channel access mechanism limits the delay performance. Data, such as state information, is often time critical, and hence, efficient information dissemination techniques to improve delay performance are essential. In this work, we aim to minimize the average system age which is the mean number of time slots old a vehicle's information is at all other vehicles in the network. To achieve this, we explore the benefits of simultaneous transmission along with piggybacking of information for multi-hop communication. While allowing simultaneous transmission guarantees faster dissemination of information, piggybacking facilitates dissemination of more information per transmission, thereby keeping the network more updated. We have also analysed the relationship between piggybacked information and number of vehicles in the network. Simulation results show improvement in network performance. Our analytical results are in good agreement with the simulation results. We consider the system where a source randomly generates status update messages and transmits them via a network cloud to the intended destination. These update message can take different times to traverse the network, which we model as exponential service times, and may result in packets reaching the destination out of order, rendering some of the earlier transmissions obsolete. We analyze the status update age for such a system, and show that it tracks well with simulation results. We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a transmission scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. We first show that in symmetric networks a Greedy policy, which transmits the packet with highest current age, is optimal. For general networks, we develop three low-complexity scheduling policies: a randomized policy, a Max-Weight policy and a Whittle's Index policy, and derive performance guarantees as a function of the network configuration. To the best of our knowledge, this is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels. Numerical results show that both Max-Weight and Whittle's Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance. In this paper, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information , or simply age , which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution). We examine multiple independent sources providing status updates to a monitor through a first-come-first-served M M 1 queue. We formulate a status-age timeliness metric and find the region of feasible average status ages for a pair of updating sources. In the presence of interfering traffic with a given offered load, we show the existence of an optimal rate at which a source should generate its updates. A source submits status updates to a service facility for delivery to a monitor. Each update requires energy and the source is powered by a stochastic energy harvesting system. With knowledge of the service facility state, the source avoids queue-induced delays by submitting a fresh update only after the service completion of a prior update. For a source with a large battery, we evaluate updating policies using a status age timeliness metric. We show that an optimal policy is lazy; following a service completion, the service facility is frequently left idle even though the server may have sufficient energy to submit an update.