Edge computing is a good complement to cloud computing for deploying large-scale geo-distributed simulation applications, which are very sensitive to the communication delay among different simulation components (also called tasks in this paper) and users. We mainly focus on the efficient scheduling of simulation components in collaborative edge and cloud environments. As components should be deployed jointly with the consideration of capacity constraints of hosts, it is actually an NP-complete multi-dimensional bin packing problem. Meanwhile, dynamic changes of component and host states require the low deployment latency of scheduling algorithms. Unfortunately, most of the existing schedulers for modern clusters are queue-based, in which tasks are scheduled sequentially, thus lacking the ability to process tightly coupled tasks jointly. Other batching-based placement algorithms are usually time-consuming. This paper describes Pond, a novel flow-based scheduler with the awareness of interactions among tasks and users as well as heterogeneous multi-dimensional resources. First, characteristics of distributed simulation tasks are analysed and the scheduling problem is formulated as a min-cost max-flow (MCMF) problem over the flow network by mapping the communication overhead among tasks and users to the costs of arcs in the network. Considering the inherent defects of existing flow-based schedulers in dealing with multi-dimensional resources, a new method based on dominant resource is proposed and some problem specific heuristics are also designed. Extensive simulation experiments based on Alibaba production trace and some random synthetic parameters are conducted. Results show that Pond can reduce the average communication cost for each task significantly in a quite low deployment latency compared with some baselines.

Research on data visualization aims at finding the best way to present data via visual interfaces. We introduce the complementary problem of "data vocalization". Our goal is to present relational data in the most efficient way via voice output. This problem setting is motivated by emerging tools and devices (e.g., Google Home, Amazon Echo, Apple's Siri, or voice-based SQL interfaces) that communicate data primarily via audio output to their users. We treat voice output generation as an optimization problem. The goal is to minimize speaking time while transmitting an approximation of a relational table to the user. We consider constraints on the precision of the transmitted data as well as on the cognitive load placed on the listener. We formalize voice output optimization and show that it is NP-hard. We present three approaches to solve that problem. First, we show how the problem can be translated into an integer linear program which enables us to apply corresponding solvers. Second, we present a two-phase approach that forms groups of similar rows in a pre-processing step, using a variant of the apriori algorithm. Then, we select an optimal combination of groups to generate a speech. Finally, we present a greedy algorithm that runs in polynomial time. Under simplifying assumptions, we prove that it generates near-optimal output by leveraging the sub-modularity property of our cost function. We compare our algorithms experimentally and analyze their complexity.