Online Job Assignment
Farbod Ekbatani, Yiding Feng, Ian Kash, Rad Niazadeh

TL;DR
This paper introduces an online algorithm called Forward-Looking BALANCE (FLB) for assigning jobs to servers in cloud computing, achieving near-optimal competitive ratios by considering future capacity penalties.
Contribution
The paper presents a new online algorithm with a primal-dual analysis that is asymptotically optimal-competitive for heterogeneous job-server assignment problems.
Findings
FLB achieves an asymptotic competitive ratio of ln(RD)+3lnln(max(R,D))+O(1).
The analysis introduces a novel dual-fitting technique and inductive capacity feasibility proof.
The bound's dependencies on reward and duration ratios are proven to be optimal.
Abstract
Motivated primarily by applications in cloud computing, we study a simple, yet powerful, online allocation problem in which jobs of varying durations arrive over continuous time and must be assigned immediately and irrevocably to one of the available offline servers. Each server has a fixed initial capacity, with assigned jobs occupying one unit for their duration and releasing it upon completion. The algorithm earns a reward for each assignment upon completion. We consider a general heterogeneous setting where both the reward and duration of a job depend on the job-server pair. The objective of the online algorithm is to maximize the total collected reward, and remain competitive against an omniscient benchmark that knows all job arrivals in advance. Our main contribution is the design of a new online algorithm, termed Forward-Looking BALANCE (FLB), and using primal-dual framework to…
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Taxonomy
TopicsOptimization and Search Problems · Scheduling and Optimization Algorithms · Advanced Bandit Algorithms Research
