Deadline is not Enough: How to Achieve Importance-aware Server-centric Data Centers via a Cross Layer Approach
Ming-Hung Chen, Shi-Chen Wang, Cheng-Fu Chou
备注：
This version is finished on 2012/7/30. New versions which include improved rate-based importance-aware protocol and DCTCP-based importance-aware protocol are considered to be submitted before release
Today's datacenters face important challenges for providing low-latency high-quality interactive services to meet user's expectation. For improving the application throughput, recent research works have embedded application deadline information into design of network flow schedule to meet the latency requirement. Here, arises a critical question: does application-level throughput mean providing better quality service? We note that there are usually a set of semantic related responses (or flows) for answering a query; and, some responses are highly correlative with the query while others do not. Thus, this observation motivates us to associate the importance of the contents with the application flows (or responses) in order to enhance the service quality. We first model the application importance maximization problem in a generic network and in a server-centric network. Since both of them are too complicated to be deployed in the real world, we propose the importance-aware delivery protocol, which is a distributed event-driven rate-based delivery control protocol, for server-centric datacenter networks. The proposed protocol is able to make use of the multiple disjoin paths of server-centric network, and jointly consider flow importance, flow size, and deadline to maximize the goodput of most-related semantic data of a query. Through real-data-based or synthetic simulations, the results show that our proposed protocol significantly outperforms D3 and MPTCP in terms of the precision at K and the sum of application-level importance.
Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing
Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger functions by following high-level invocation dependencies but are oblivious to the underlying data exchanges between functions. This design is neither efficient nor easy to use in orchestrating complex workflows -- developers often have to manage complex function interactions by themselves, with customized implementation and unsatisfactory performance. In this paper…
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
Institute for Infocomm Research（信息通信研究院）；A*STAR（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing（高性能计算研究院）；National University of Singapore（新加坡国立大学）
