Modern GPU clusters increasingly comprise diverse heterogeneous GPUs, driven by the continuous release of new GPU models. Achieving a balance between fairness and efficiency when scheduling multi-tenant Deep Learning (DL) training jobs on such clusters is inherently challenging. Existing DL training schedulers largely emphasize fairness through GPU temporal sharing, while the spatial dimension of resource allocation is often underexplored. This oversight can lead to GPU fragmentation and suboptimal system performance. In this paper, we propose STS-Fairness, a spatiotemporal sharing fairness scheduler. STS-Fairness partitions each GPU into multiple isolated slots under a novel spatiotemporal fairness constraint and allocates jobs using a round-based allocation mechanism. We guarantee that STS-Fairness achieves overall performance optimality while satisfying spatiotemporal fairness constraints. The scheduling problem is formulated as an integer nonlinear program (INLP) that is solved to optimality in polynomial time via dynamic programming. We deployed the STS-Fairness framework on both physical and simulated heterogeneous clusters and conducted large-scale experiments. These results demonstrate that STS-Fairness reduces average JCT by$1.2 \times$, shortens makespan by$1.24 \times$, and increases throughput by$\mathbf{1. 2 5} \times$compared to state-of-the-art (SoTA) schedulers.

Geo-distributed computing, a paradigm that assigns computational tasks to globally distributed nodes, has emerged as a promising approach in cloud computing, edge computing, cloud-edge computing, and supercomputer computing (SC). It enables low-latency services, ensures data locality, and handles large-scale applications. As global computing capacity and task demands increase rapidly, scheduling tasks for efficient execution in geo-distributed computing systems has become an increasingly critical research challenge. It arises from the inherent characteristics of geographic distribution, including heterogeneous network conditions, region-specific resource pricing, and varying computational capabilities across locations. Researchers have developed diverse task scheduling methods tailored to geo-distributed scenarios, aiming to achieve objectives such as performance enhancement, fairness assurance, and fault-tolerance improvement. This survey provides a comprehensive and systematic review of task scheduling techniques across four major distributed computing environments, with an in-depth analysis of these approaches based on their core scheduling objectives. Through our analysis, we identify key research challenges and outline promising directions for advancing task scheduling in geo-distributed computing.

Pay-as-you-go caching systems are now widely used as storage services in cloud computing. However, users’ data caching requirements not only change over time, but also they are affected by workload characteristics, making it difficult to always ensure high efficient use of cache resources. Cache resource sharing is an effective way to improve the efficiency of cache usage. To incentivize users to share caches, it is essential to ensure long-term fairness among multiple users. However, traditional resource allocation strategies canonly guarantee memory less fairness among users,which is not applicable to long-term cache sharing systems. In this paper, we propose a fair allocation policy named FairCache for Pay-as-you-go cache resources. First, FairCache can satisfy four desirable properties of resource allocation: sharing incentive, pay-as-you-usefairness, strategy proofness, and pare to efficiency. Second, FairCache is an efficiency-fairness resource allocation policy based on the efficiency knob θ. The strategy keeps sensitive to the constantly changing cache demands of multiple users within the system by adjusting the efficiency knob θ, thus ensuring long-term multi-user fairness while maximizing the efficiency of cache usage. In addition, FairCache also has an anti-cheating mechanism to avoid possible free-rider problems when multiple users cache access files. Finally, this paper implements the FairCache policy in Alluxio. The experimental results show that FairCache is a lightweight scheduler and that it can maximize the efficiency usage of cache resources while ensuring the long-term for multiple users in the pay-as-you-go Cache systems fairness.

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods.

Recent trends show that cloud computing is growing to span more and more globally distributed data centers. For geo-distributed data centers, there is an increasing need for scheduling algorithms to place tasks across data centers, by jointly considering data and computation. This scheduling must deal with situations such as wide-area distributed data, data sharing, WAN bandwidth costs and data center capacity limits, while also minimizing completion time. However, this kind of scheduling problems is known to be NP-Hard. In this paper, inspired by real applications in astronomy field, we propose a two-phase scheduling algorithm that addresses these challenges. The mapping phase groups tasks considering the data-sharing relations, and dispatches groups to data centers by way of one-to-one correspondence. The reassigning phase balances the completion time across data centers according to relations between tasks and groups. We utilize the real China-Astronomy-Cloud model and typical applications to evaluate our proposal. Simulations show that our algorithm obtains up to 22% better completion time and effectively reduces the amount of data transfers compared with other similar scheduling algorithms.

With the arrival of cloud computing and Big Data, many scientific applications with large amount of data can be abstracted as scientific workflows and running on a cloud environment. Distributing these datasets intelligently can decrease data transfers efficiently during the workflow's execution. In this paper, we proposed a 2- stage data placement strategy. In the initial stage, we cluster the datasets based on their correlation, and allocate these clusters onto data centers. Compared with existing works, we have incorporated the data size into correlation calculation, and have proposed a new type of data correlation for the intermediate data named "the first order conduction correlation". Hence the data transmission cost can be measured more reasonable. In the runtime stage, the re-distribution algorithm can adjust data layout according to the changed factors, and the overhead of re-layout itself has also been measured. Compared with previous work, simulation results show that our proposed strategy can effectively reduce the time consumption of data movements during the workflow execution.

Spectral calculation and analysis have very important practical applications in astrophysics. The main portion of spectral calculation is to solve a large number of one-dimensional numerical integrations at each point of a large three-dimensional parameter space. However, existing widely used solutions still remain in process-level parallelism, which is not competent to tackle numerous compute-intensive small integral tasks. This paper presented a GPU-optimized approach to accelerate the numerical integration in massive spectral calculation. We also proposed a load balance strategy on hybrid multiple CPUs and GPUs architecture via share memory to maximize performance. The approach was prototyped and tested on the Astrophysical Plasma Emission Code (APEC), a commonly used spectral toolset. Comparing with the original serial version and the 24 CPU cores (2.5GHz) parallel version, our implementation on 3 Tesla C2075 GPUs achieves a speed-up of up to 300 and 22 respectively.

Deployed on Dome A of Antarctica, the AST3 astronomical telescopes are required to perform the sky survey in the extreme unmanned environments and process the observed data in real-time to offer the astronomers far back home with the timely observation results. A scalable real-time photometric system is proposed and designed for the automatic astronomical observations of AST3. A GPU-based algorithm for image subtraction photometry is proposed to improve the efficiency of this most time-consuming prodedure in the data processing workflow. To impove the reliability, the system is organized in a HA cluster and equipped with a specially designed daemon. The system has been practically utilized in the astronomical observations with the development of AST3 telescopes.