Server Cloud Scheduling

TL;DR

This paper introduces the Server Cloud Scheduling problem, optimizing job scheduling on local and cloud resources considering precedence, communication delays, costs, and processing times, with algorithms and complexity results.

Contribution

It presents an FPTAS for makespan minimization and establishes strong hardness results for specific cases of the problem.

Findings

FPTAS developed for general makespan minimization

Proven strong hardness for unit processing times and delays

Effective scheduling strategies balancing cost and performance

Abstract

Consider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the Server Cloud Scheduling problem, in which the jobs have to be processed either on a single local machine or on one of many cloud machines. Both the source and the sink have to be scheduled on the local machine. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server, the other in the cloud. The server can process jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS with respect for the makespan objective for a fairly general case and strong hardness for the case with unit processing times and delays.