Stream processing applications handle unbounded and continuous flows of data items which are generated from multiple geographically distributed sources. Two approaches are commonly used for processing: Cloud-based analytics and Edge analytics. The first one routes the whole data set to the Cloud, incurring significant costs and late results from the high latency networks that are traversed. The latter can give timely results but forces users to manually define which part of the computation should be executed on Edge and to interconnect it with the remaining part executed in the Cloud, leading to sub-optimal placements. In this paper, we introduce Planner, a middleware for uniform and transparent stream processing across Edge and Cloud. Planner automatically selects which parts of the execution graph will be executed at the Edge in order to minimize the network cost. Real-world micro-benchmarks show that Planner reduces the network usage by 40% and the makespan (end-to-end processing time) by 15% compared to state-of-the-art.
Causality is an important concept both for proving impossibility results and for synthesizing efficient protocols in distributed computing. For asynchronous agents communicating over unreliable channels, causality is well studied and understood. This understanding, however, relies heavily on the assumption that agents themselves are correct and reliable. We provide the first epistemic analysis of causality in the presence of byzantine agents, i.e., agents that can deviate from their protocol and, thus, cannot be relied upon. Using our new framework for epistemic reasoning in fault-tolerant multiagent systems, we determine the byzantine analog of the causal cone and describe a communication structure, which we call a multipede, necessary for verifying preconditions for actions in this setting.
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