At internet scale companies like ByteDance, data is generated and consumed at enormously high speed by many different applications. Achieving low latency on such big data jobs is an important problem. However, the naive approach of aggregating all the data required by a job to a single location is not always feasible in a geo-distributed environment. Similarly, existing approaches in geo-distributed job scheduling often try to minimize WAN usage, which may come at the cost of latency. Another crucial element to ensure low latency is resource load balancing among DCs, which enables flexibility in job scheduling and avoids resource bottlenecks. Therefore, to minimize latency, optimizing job completion time (JCT) while maintaining resource utilization balance is important. To this end, we propose ResLake , a global scheduling platform for data-intensive workloads. ResLake aims to reduce JCT of geo-distributed applications while balancing the compute (CPU/Memory) and storage (Disk) usages across DCs and efficiently using WAN interconnections. We have deployed ResLake in ByteDance's production for over 1.5 years. ResLake has scheduled billions of jobs since its deployment. We find that ResLake improves JCT of jobs by at least 20%, and can improve resource utilization balance across DCs by up to 53%.

Document summarization is playing an important role in coping with information overload on the Web. Many summarization models have been proposed recently, but few try to adjust the summary length and sentence order according to application scenarios. With the popularity of handheld devices, presenting key information first in summaries of flexible length is of great convenience in terms of faster reading and decision-making and network consumption reduction. Targeting this problem, we introduce a novel task of generating summaries of incremental length. In particular, we require that the summaries should have the ability to automatically adjust the coverage of general-detailed information when the summary length varies. We propose a novel summarization model that incrementally maximizes topic coverage based on the document’s hierarchical topic model. In addition to the standard Rouge-1 measure, we define a new evaluation metric based on the similarity of the summaries’ topic coverage distribution in order to account for sentence order and summary length. Extensive experiments on Wikipedia pages, DUC 2007, and general noninverted writing style documents from multiple sources show the effectiveness of our proposed approach. Moreover, we carry out a user study on a mobile application scenario to show the usability of the produced summary in terms of improving judgment accuracy and speed, as well as reducing the reading burden and network traffic.

User generated contents (UGCs) from various social media sites give analysts the opportunity to obtain a comprehensive and dynamic view of any topic from multiple heterogeneous information sources. Summarization provides a promising means of distilling the overview of the targeted topic by aggregating and condensing the related UGCs. However, the mass volume, uneven quality, and dynamics of UGCs, pose new challenges that are not addressed by existing multi-document summarization techniques. In this paper, we introduce a timely task of dynamic structural and textual summarization. We generate topic hierarchy from the UGCs as a high level overview and structural guide for exploring and organizing the content. To capture the evolution of events in the content, we propose a unified dynamic reconstruction approach to detect the update points and generate the time-sequence textual summary. To enhance the expressiveness of the reconstruction space, we further use the topic hierarchy to organize the UGCs and the hierarchical subtopics to augment the sentence representation. Experimental comparison with the state-of-the-art summarization models on a multi-source UGC dataset shows the superiority of our proposed methods. Moreover, we conducted a user study on our usability enhancement measures. It suggests that by disclosing some meta information of the summary generation process in the proposed framework, the time-sequence textual summaries can pair with the structural overview of the topic hierarchy to achieve interpretable and verifiable summarization.