DeepAI AI Chat
Log In Sign Up

Stochastic Non-preemptive Co-flow Scheduling with Time-Indexed Relaxation

02/11/2018
by   Ruijiu Mao, et al.
0

Co-flows model a modern scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic co-flow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of co-flow tasks. This paper gives an approximation algorithm for stochastic non-preemptive co-flow scheduling. The proposed approach uses a time-indexed linear relaxation, and uses its solution to come up with a feasible schedule. This algorithm is shown to achieve a competitive ratio of (2m+1)(1+√(mΔ))(1+m√(Δ))(3+Δ)/2 for zero-release times, and (2m+1)(1+√(mΔ))(1+m√(Δ))(2+Δ) for general release times, where Δ represents the upper bound of squared coefficient of variation of processing times, and m is the number of servers.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/03/2018

Generalizing the Kawaguchi-Kyan bound to stochastic parallel machine scheduling

Minimizing the sum of weighted completion times on m identical parallel ...
08/14/2022

An Improved Greedy Algorithm for Stochastic Online Scheduling on Unrelated Machines

Most practical scheduling applications involve some uncertainty about th...
07/02/2018

On Non-Preemptive VM Scheduling in the Cloud

We study the problem of scheduling VMs (Virtual Machines) in a distribut...
07/18/2018

HyLine: a Simple and Practical Flow Scheduling for Commodity Datacenters

Today's datacenter networks (DCNs) have been built upon multipath topolo...
03/01/2022

An Adaptable and Agnostic Flow Scheduling Approach for Data Center Networks

Cloud applications have reshaped the model of services and infrastructur...
11/29/2019

Algorithms for flows over time with scheduling costs

Flows over time have received substantial attention from both an optimiz...