Design and Experimental Evaluation of Algorithms for Optimizing the Throughput of Dispersed Computing

TL;DR

This paper introduces a new scheduling algorithm, TPHEFT, for optimizing throughput in dispersed computing systems, along with enhancements like node splitting and task duplication, validated through experiments on real testbeds.

Contribution

It presents a novel throughput-oriented scheduling algorithm and enhancements, along with an open-source framework implementation and experimental evaluation for dispersed computing.

Findings

TPHEFT significantly outperforms HEFT in throughput (up to 2.3x).

Node splitting improves performance especially with imbalanced workloads.

Task duplication benefits are notable on slow communication links.

Abstract

With growing deployment of Internet of Things (IoT) and machine learning (ML) applications, which need to leverage computation on edge and cloud resources, it is important to develop algorithms and tools to place these distributed computations to optimize their performance. We address the problem of optimally placing computations (described as directed acyclic graphs (DAGs)) on a set of machines to maximize the steady-state throughput for pipelined inputs. Traditionally, such optimization has focused on a different metric, minimizing single-shot makespan, and a well-known algorithm is the Heterogeneous Earliest Finish Time (HEFT) algorithm. Maximizing throughput however, is more suitable for many real-time, edge, cloud and IoT applications, we present a different scheduling algorithm, namely Throughput HEFT (TPHEFT). Further, we present two throughput-oriented enhancements which can be applied to any baseline schedule, that we refer to as "node splitting" (SPLIT) and "task duplication" (DUP). In order to implement and evaluate these algorithms, we built new subsystems and plugins for an open-source dispersed computing framework called Jupiter. Experiments with varying DAG structures indicate that: 1) TPHEFT can significantly improve throughput performance compared to HEFT (up to 2.3 times in our experiments), with greater gains when there is less degree of parallelism in the DAG, 2) Node splitting can potentially improve performance over a baseline schedule, with greater gains when there's an imbalanced allocation of computation or inter-task communication, and 3) Task duplication generally gives improvements only when running upon a baseline that places communication over slow links. To our knowledge, this is the first study to present a systematic experimental implementation and exploration of throughput-enhancing techniques for dispersed computing on real testbeds.