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.

Gridding operation, which is to map non-uniform data samples on to a uniformly distributed grid, is one of the key steps in radio astronomical data reduction process. One of the main bottlenecks of gridding is the poor computing performance, and a typical solution for such performance issue is the implementation of multi-core CPU platforms. Although such a method could usually achieve good results, in many cases, the performance of gridding is still restricted to an extent due to the limitations of CPU, since the main workload of gridding is a combination of a large number of single instruction, multi-data-stream operations, which is more suitable for GPU, rather than CPU implementations. To meet the challenge of massive data gridding for the modern large single-dish radio telescopes, e.g. the Five-hundred-meter Aperture Spherical radio Telescope (FAST), inspired by existing multi-core CPU gridding algorithms such as Cygrid, here we present an easy-to-install, high-performance, and open-source convolutional gridding framework, HCGrid, in CPU-GPU heterogeneous platforms. It optimises data search by employing multi-threading on CPU, and accelerates the convolution process by utilising massive parallelisation of GPU. In order to make HCGrid a more adaptive solution, we also propose the strategies of thread organisation and coarsening, as well as optimal parameter settings under various GPU architectures. A thorough analysis of computing time and performance gain with several GPU parallel optimisation strategies show that it can lead to excellent performance in hybrid computing environments.

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

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.