Unmanned aerial vehicles (UAVs) have emerged as a new and flexible paradigm to offer low-latency and diverse mobile edge computing (MEC) services for user equipment (UE). To minimize the service delay, caching is introduced in UAV-assisted MEC networks to bring service contents closer to UEs. However, UAV-assisted MEC is challenged by the heavy communication overhead introduced by service caching and UAV’s limited energy capacity. In this article, we propose an online algorithm,OOA, that jointly optimizes caching and offloading decisions for UAV-assisted MEC networks, to minimize the overall service delay. Specifically, to improve the caching effectiveness and reduce the caching overhead,OOAemploys a greedy algorithm to dynamically make caching decisions based on UEs’ preferences on services and UAVs’ historical trajectories, with the goal of maximizing the probability of successful offloading. To realize the rational utilization of energy from a long-term perspective,OOAdecomposes the online problem into a series of single-slot problems by scaling the UAV’s energy constraint into the objective, and iteratively optimizes UAV trajectory and task offloading at each time slot. Theoretical analysis proves thatOOAconverges to a suboptimal solution with polynomial time complexity. Extensive simulations based on real world data further show thatOOAcan reduce the service delay by up to 33% while satisfying the UAV’s energy constraint, compared to three state-of-the-art algorithms.

The emergence of edge intelligence has made smart IoT services (e.g.,video/audio surveillance, autonomous driving and smart city) a reality. To ensure the quality of service, edge service providers train unbiased models of distributed machine learning jobs over the local datasets collected by edge networks, and usually adopt the parameter server (PS) architecture. However, the training ofunbiased distributed learning(UDL) depends on geo-distributed data and edge resources, bringing a new challenge for service providers: how to effectively schedule and price UDL jobs such that the long-term system utility (i.e.,social welfare) can be maximized. In this paper, we propose an online auction-based scheduling algorithmEris, which determines the data workload, the number and the placement of concurrent workers and PSs for each arriving UDL job, and dynamically prices limited edge resources based on current resource consumption.Erisapplies a primal-dual framework which calls an efficient dual subroutine to schedule UDL jobs, achieving a good competitive ratio and pseudo-polynomial time complexity. To evaluate the effectiveness ofEris, we implement both a testbed and a large-scaled simulator. The results demonstrate thatErisoutperforms and achieves up to 44% more social welfare compared to state-of-the-art algorithms in today's cloud system.

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs,i.e.,high mobility as well as limited energy and computing capacity, and propose privacy-preserving auction frameworks, Ptero, to schedule offloading tasks on the fly and incentivize UAVs’ participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV’s energy constraint into the objective. To protect UE’s privacy, Ptero calculates UAV’s coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system’s utility ( i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy-preserving and a nontrivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to$116\%$compared with four state-of-the-art algorithms.

As the applications of AI proliferate, it is critical to increase the throughput of online DNN inference services. Multi-process service (MPS) improves the utilization rate of GPU resources by spatial-sharing, but it also brings unique challenges. First, interference between co-located DNN models deployed on the same GPU must be accurately modeled. Second, inference tasks arrive dynamically online, and each task needs to be served within a bounded time to meet the service-level objective (SLO). Third, the problem of fragments has become more serious. To address the above three challenges, we propose anIntelligentScheduling orchestrator for multi-GPU inference servers with spatio-temporalSharing (InSS), aiming to maximize the system throughput.InSSexploits two key innovations: i) An interference-aware latency analytical model which estimates the task latency. ii) A two-stage intelligent scheduler is tailored to jointly optimize the model placement, GPU resource allocation and adaptively decides batch size by coupling the latency analytical model. Our prototype implementation on four NVIDIA A100 GPUs shows thatInSScan improve the throughput by up to 86% compared to the state-of-the-art GPU schedulers, while satisfying SLOs. We further show the scalability ofInSSon 64 GPUs.

The emergence of unmanned aerial vehicles (UAVs) extends the mobile edge computing (MEC) services in broader coverage to offer new flexible and low-latency computing services for user equipment (UE) in the era of 5G and beyond. One of the fundamental requirements in UAV-assisted mobile wireless systems is the low latency, which can be jointly optimized with service caching and task offloading. However, this is challenged by the communication overhead involved with service caching and constrained by limited energy capacity. In this work, we present a comprehensive optimization framework with the objective of minimizing the service latency while incorporating the unique features of UAVs. Specifically, to reduce the caching overhead, we make caching placement decision every T slots (specified by service providers), and adjust UAV trajectory, user equipment or UE-UAV association, and task offloading decisions at each time slot under the constraints of UAV’s energy and resource capacity. By leveraging Lyapunov optimization approach and dependent rounding technique, we design an alternating optimization-based algorithm, named TJSO, which iteratively optimizes caching and offloading decisions. Theoretical analysis proves that TJSO converges to the near-optimal solution in polynomial time. Extensive simulations further verify that our proposed solution can significantly reduce the service delay for UEs while maintaining low energy consumption when compared to the three state-of-the-art baselines.

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs, i.e., high mobility as well as limited energy and computing capacity, and propose a privacy-preserving auction framework, Ptero, to schedule offloading tasks on the fly and incentivize UAVs' participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV's energy constraint into the objective. To protect UE's privacy, Ptero calculates UAV's coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system's utility (i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy preserving and a non-trivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to 116% compared with four state-of-the-art algorithms.

Recent advances in 5G and edge computing enable rapid development and deployment of edge-cloud systems, which are ideal for delay-sensitive machine learning (ML) applications such as autonomous driving and smart city. Distributed ML jobs often need to train a large model with enormous datasets, which can only be handled by deploying a distributed set of workers in an edge-cloud system. One common approach is to employ a parameter server (PS) architecture, in which training is carried out at multiple workers, while PSs are used for aggregation and model updates. In this architecture, one of the fundamental challenges is how to dispatch ML jobs to workers and PSs such that the average job completion time (JCT) can be minimized. In this work, we propose a novel online preemptive scheduling framework to decide the location and the execution time window of concurrent workers and PSs upon each job arrival. Specifically, our proposed scheduling framework consists of: i) a job dispatching and scheduling algorithm that assigns each ML job to workers and decides the schedule to train each data chunk; ii) a PS assignment algorithm that determines the placement of PS. We prove theoretically that our proposed algorithm is$D_{max}(1+1/\epsilon)$-competitive with$(1 + \epsilon)$-speed augmentation, where$D_{max}$is the maximal number of data chunks in any job. Extensive testbed experiments and trace-driven simulations show that our algorithm can reduce the average JCT by up to 30% compared with state-of-the-art baselines.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, causing significant mortality. There is a mechanistic relationship between intracellular coronavirus replication and deregulated autophagosome-lysosome system. We performed transcriptome analysis of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and identified the aberrant upregulation of genes in the lysosome pathway. We further determined the capability of two circulating markers, namely microtubule-associated proteins 1A/1B light chain 3B (LC3B) and (p62/SQSTM1) p62, both of which depend on lysosome for degradation, in predicting the emergence of moderate-to-severe disease in COVID-19 patients requiring hospitalization for supplemental oxygen therapy. Logistic regression analyses showed that LC3B was associated with moderate-to-severe COVID-19, independent of age, sex and clinical risk score. A decrease in LC3B concentration <5.5 ng/ml increased the risk of oxygen and ventilatory requirement (adjusted odds ratio: 4.6; 95% CI: 1.1-22.0; P = 0.04). Serum concentrations of p62 in the moderate-to-severe group were significantly lower in patients aged 50 or below. In conclusion, lysosome function is deregulated in PBMCs isolated from COVID-19 patients, and the related biomarker LC3B may serve as a novel tool for stratifying patients with moderate-to-severe COVID-19 from those with asymptomatic or mild disease. COVID-19 patients with a decrease in LC3B concentration <5.5 ng/ml will require early hospital admission for supplemental oxygen therapy and other respiratory support.