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1. Age-aware Scheduling for Asynchronous Arriving Jobs in Edge Applications

   Zhong, Jing ; Zhang, Wuyang ; Yates, Roy D. ; Garnaev, Andrey ; Zhang, Yanyong ( April 2019 , IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   Age of information has been proposed recently to measure information freshness, especially for a class of real-time video applications. These applications often demand timely updates with edge cloud computing to guarantee the user experience. However, the edge cloud is usually equipped with limited computation and network resources and therefore, resource contention among different video streams can contribute to making the updates stale. Aiming to minimize a penalty function of the weighted sum of the average age over multiple end users, this paper presents a greedy traffic scheduling policy for the processor to choose the next processing request with the maximum immediate penalty reduction. In this work, we formulate the service process when requests from multiple users arrive at edge cloud servers asynchronously and show that the proposed greedy scheduling algorithm is the optimal work- conserving policy for a class of age penalty functions. 

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2. Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems

   https://doi.org/10.1109/MILCOM58377.2023.10356278  

   Ornee, Tasmeen Zaman ; Shisher, Md Kamran ; Kam, Clement ; Sun, Yin ( October 2023 , IEEE)

   In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors that are monitoring several safety-critical situations (e.g., carbon monoxide density in forest fire detection, machine safety in industrial automation, and road safety). Based on the received sensor updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited communication resources, the sensor updates may not be timely, resulting in the possibility of misunderstanding the current situation. In particular, if a dangerous situation is misinterpreted as safe, the safety risk is high. In this paper, we introduce a novel framework that quantifies the penalty due to the unawareness of a potentially dangerous situation. This situation-unaware penalty function depends on two key factors: the Age of Information (AoI) and the observed signal value. For optimal estimators, we provide an information-theoretic bound of the penalty function that evaluates the fundamental performance limit of the system. To minimize the penalty, we study a pull-based multi-sensor, multi-channel transmission scheduling problem. Our analysis reveals that for optimal estimators, it is always beneficial to keep the channels busy. Due to communication resource constraints, the scheduling problem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. By utilizing relaxation and Lagrangian decomposition of the RMAB, we provide a low-complexity scheduling algorithm which is asymptotically optimal. Our results hold for both reliable and unreliable channels. Numerical evidence shows that our scheduling policy can achieve up to 100 times performance gain over periodic updating and up to 10 times over randomized policy. 

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3. Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

   https://doi.org/10.1109/TCOMM.2022.3225186  

   Cai, Yang ; Llorca, Jaime ; Tulino, Antonia M. ; Molisch, Andreas F. ( January 2022 , IEEE Transactions on Communications)

   Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly impor-tant role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast na-ture, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty con-trol theory to develop the first fully decentralized, throughput-and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies. 

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4. On the Multi-Activation Oriented Design of D2D-Aided Caching Networks

   Cai, Yang ; Molisch, Andreas F. ( January 2019 , ... IEEE Global Communications Conference)

   Caching at the wireless edge has proven to be a promising approach for efficient video distribution, especially when aided by device-to-device communication. A widely explored scheme is to sub-divide a cell into clusters, and allow one pair of users within each cluster to communicate in each time slot. As more devices are raising frequent requests for popular videos, activating multiple links simultaneously can potentially improve the throughput. However, allowing multiple links at the same time requires to solve the problems of avoiding request clashes, i.e., multiple users requesting transmission from the same caching node, as well as interference management. To address these issues, this paper proposes new designs of both the caching policy and the transmission policy (i.e., link scheduling and power control). Furthermore, the duration of each time slot is optimized to improve the throughput. Finally, some numerical results demonstrate the performance gain of the proposed designs. 

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5. Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for Apache Mesos Cluster

   https://doi.org/10.1109/UCC.2018.00015  

   Saha, Pankaj ; Beltre, Angel ; Govindaraju, Madhusudhan ( January 2019 , 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC))

   Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant clustered environment. Computational resources (i.e., CPU, memory, disk, etc.) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multi-user environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can lead to poor performance as users without any pending task may receive more resource offers, and users with a queue of pending tasks can starve due to their high dominant shares. In a multi-tenant environment, the characteristics of frameworks and workloads must be understood by cluster managers to be able to define fairness based on not only resource share but also resource demand and queue wait time. We have developed a policy driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy. 

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