eBPF-based Content and Computation-aware Communication for Real-time Edge Computing Sabur Baidya1, Yan Chen2 and Marco Levorato1 1Donald Bren School of Information and Computer Science, UC Irvine e-mail: fsbaidya,
[email protected] 2America Software Laboratory, Huawei, e-mail:
[email protected] Abstract—By placing computation resources within a one-hop interference constraints on IoT data streams and facilitate their wireless topology, the recent edge computing paradigm is a key coexistence. enabler of real-time Internet of Things (IoT) applications. In In this paper, we propose a computation-aware commu- the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the nication control framework for real-time IoT applications data stream needs to be replicated. However, the transportation generating high-volume data traffic processed at the network of parallel streams might not be feasible due to limitations in the edge. Driven by QoC requirements, the framework provides capacity of the network transporting the data. To address this real-time user-controlled packet replication and forwarding issue, a content and computation-aware communication control inside the in-kernel Virtual Machines (VM) using an extended framework is proposed based on the Software Defined Network (SDN) paradigm. The framework supports multi-streaming using Berkeley Packet Filter (eBPF) [9]. The implementation uses the extended Berkeley Packet Filter (eBPF), where the traffic flow the concepts of SDN and NFV to achieve highly program- and packet replication for each specific computation process is able and dynamic packet replication. Resource allocation is controlled by a program running inside an in-kernel Virtual Ma- semantic and content-aware, and, in the considered case, chine (VM).