Max-Stretch Minimization on an Edge-Cloud Platform

Abstract

We consider the problem of scheduling independent jobs that are generated by processing units at the edge of the network. These jobs can either be executed locally, or sent to a centralized cloud platform that can execute them at greater speed. Such edge-generated jobs may come from various applications, such as e-health, disaster recovery, autonomous vehicles or flying drones. The problem is to decide where and when to schedule each job, with the objective to minimize the maximum stretch incurred by any job. The stretch of a job is the ratio of the time spent by that job in the system, divided by the minimum time it could have taken if the job was alone in the system. We formalize the problem and explain the differences with other models that can be found in the literature. We prove that minimizing the max-stretch is NP-complete, even in the simpler instance with no release dates (all jobs are known in advance). This result comes from the proof that minimizing the max-stretch with homogeneous processors and without release dates is NP-complete, a complexity problem that was left open before this work. We design several algorithms to propose efficient solutions to the general problem, and we conduct simulations based on real platform parameters to evaluate the performance of these algorithms.

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

“…EDF (Earliest Deadline First) ( Benoit, Elghazi & Robert, 2021 ) iteratively assigns the task with the earliest deadline to the first core satisfying its requirements.…”

“…To address the task scheduling problem in DE3C, the paper formulates it into a binary nonlinear programming (BNLP) for the SLA satisfaction optimization. In order to solve the problem in polynomial time, a heuristic method is designed based on the idea of least accumulated slack time first (LASTF) ( Wang et al, 2020a ) and earliest deadline first (EDF) ( Benoit, Elghazi & Robert, 2021 ). In brief, the contributions of this paper can be summarized as followings.…”

Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA satisfaction

“…For PSO or GA with binary encoding method, they have similar procedures to IPSO, and thus their time complexities are also O ( ITR ⋅ NP ⋅ T 2 / NC ). Referring to Bays (1977) ; B.V. & Guddeti (2018) ; Benoit, Elghazi & Robert (2021) ; Liu et al. (2019) ; Meng et al.…”

“…Earliest Deadline First ( Benoit, Elghazi & Robert, 2021 ), EDF, iteratively schedules the task with earliest deadline to the first computing core satisfying its deadline, which is a classical heuristic method concerning the deadline constraint.…”