APEROL: Adaptive Parallel Edge-to-Cloud Runtime Optimization for Layered Workflow Execution

The execution of streaming analytics workflows across large-scale IoT infrastructures poses unique challenges. Central data collection depletes the available bandwidth and leaves IoT device resources unutilized. Therefore, workflow execution should be performed in-network, assigning workflow operator execution on devices across the cloud-to-edge continuum. However, the vast scale of devices results in an exponential number of possible combinations of workflow operator assignments. On top of that, workflows are executed on dynamic environments where volatile data stream distributions and device churn may render a deployed plan inefficient and, therefore, rapid adaptation decisions are crucial. To address these challenges, we present APEROL, the first suite of parallel optimization algorithms for timely and efficient workflow execution in IoT environments. APEROL introduces a novel conceptualization of the optimization search space, coupled with a signature-based execution plan enumeration scheme, that enable scalable, parallel plan exploration. The suite includes exhaustive, heuristic, greedy, and random sampling algorithms, which are complementary in algorithm speed vs. plan quality trade-offs under different setups. The current implementation examines up to 2M candidate plans per second on commodity hardware. Experiments with 5 challenging workflows from 2 streaming benchmarks, over real and simulated networks ranging from 10s to 1000s sites show APEROL's effectiveness and timeliness.