Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.

Microsoft Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.

To lower hosting costs and service prices, database-as-a-service (DBaaS) providers strive to maximize cluster utilization without negatively affecting their users' service experience. Some of the most effective approaches for increasing service efficiency result in the over-booking of the cluster with user databases. For instance, one approach is to reclaim cluster capacity from a database when it is idle, temporarily re-using the capacity for some other purpose, and over-booking the cluster's resources. Such approaches are largely driven by policies that determine when it is prudent to temporarily reclaim capacity from an idle database. In this paper, we examine policies that inherently tune the system's idle sensitivity. Increased sensitivity to idleness leads to aggressive over-booking while the converse leads to conservative reclamation and lower utilization levels. Aggressive over-booking also incurs a "reserve" capacity cost (for when we suddenly "owe" capacity to previously idle databases.) We answer these key questions in this paper: (1) how to find a "good" resource reclamation policy for a given DBaaS cluster of users; and (2) how to forecast the needed near-term reserve capacity. To help us answer these questions, we used production user activity traces from Azure SQL DB and built models of an over-booking mechanism. We show that choosing the right policy can substantially boost the efficiency of the service, facilitating lower service prices via lower amortized infrastructure costs.

The rapid development of Web2.0 leads to significant information redundancy.Especially for a complex news event, it is difficult to understand its general idea within a single coherent picture.A complex event often contains branches, intertwining narratives and side news which are all called storylines.In this paper, we propose a novel solution to tackle the challenging problem of storylines extraction and reconstruction.Specifically, we first investigate two requisite properties of an ideal storyline.Then a unified algorithm is devised to extract all effective storylines by optimizing these properties at the same time.Finally, we reconstruct all extracted lines and generate the high-quality story map.Experiments on real-world datasets show that our method is quite efficient and highly competitive, which can bring about quicker, clearer and deeper comprehension to readers.

Summarization methods such as timelines have greatly helped people to understand all kinds of news events within limited time. However, there are few studies probing into cross-media summarization, for example, generating timelines which contain both texts and images that can reinforce each other. In this paper, we tackle this important and challenging problem by proposing a novel solution. Specifically, we first reveal three requisite characteristics of an ideal image-text timeline. With the idea of recommendation, all these requisites will be modeled respectively, and fused compactly in a unified cross-media framework. Finally, we put all sentences and images into either the schema of referrer or the schema of recommended candidate, and the former recommends the latter. After changing their roles iteratively, we can achieve the optimal timelines which will significantly improve user experience and satisfaction. Experiments on real-world datasets show that the timelines generated by our framework outperform several competitive baselines.

In many cases, people would like to read the news with great importance on the Internet. However, what users can grasp covers a very small part compared with the huge amount of news which never stops increasing. In this paper, we try to find what users are most likely to be interested in. We notice that media focus plays an essential role in distinguishing news topics and user attention is also an important factor. Therefore, we first propose five strategies which only exploit media focus to decide news influence impact. Then we provide three strategies to combine user attention with media focus. Meanwhile, we also take four types of interaction between user attention and media focus into consideration. To the best of our knowledge, this is the first work to establish different models for computing influence decay of news topics. Experiments show that better influence scores will be achieved by a decay algorithm based on Ebbinghaus forgetting curve and information fusion by considering interactions between user attention and media focus.

Manual timelines have greatly helped us to keep pace with the big world. In this paper, we introduce a novel solution which generates image-text timelines for news events based on Evolutionary Image-Text Summarization, which is an important and challenging problem. We first extract image's semantic information under translation model, and then fuse the high quality images with text timeline under an image assignment algorithm which can optimize the global coordination of the final timeline. The experimental results show that news readers can receive more satisfaction from the image-text timelines we generate.