Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

01/13/2019
by   Ali Yekkehkhany, et al.
0

Dynamic affinity load balancing of multi-type tasks on multi-skilled servers, when the service rate of each task type on each of the servers is known and can possibly be different from each other, is an open problem for over three decades. The goal is to do task assignment on servers in a real time manner so that the system becomes stable, which means that the queue lengths do not diverge to infinity in steady state (throughput optimality), and the mean task completion time is minimized (delay optimality). The fluid model planning, Max-Weight, and c-μ-rule algorithms have theoretical guarantees on optimality in some aspects for the affinity problem, but they consider a complicated queueing structure and either require the task arrival rates, the service rates of tasks on servers, or both. In many cases that are discussed in the introduction section, both task arrival rates and service rates of different task types on different servers are unknown. In this work, the Blind GB-PANDAS algorithm is proposed which is completely blind to task arrival rates and service rates. Blind GB-PANDAS uses an exploration-exploitation approach for load balancing. We prove that Blind GB-PANDAS is throughput optimal under arbitrary and unknown distributions for service times of different task types on different servers and unknown task arrival rates. Blind GB-PANDAS desires to route an incoming task to the server with the minimum weighted-workload, but since the service rates are unknown, such routing of incoming tasks is not guaranteed which makes the throughput optimality analysis more complicated than the case where service rates are known. Our extensive experimental results reveal that Blind GB-PANDAS significantly outperforms existing methods in terms of mean task completion time at high loads.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

09/23/2017

GB-PANDAS: Throughput and heavy-traffic optimality analysis for affinity scheduling

Dynamic affinity scheduling has been an open problem for nearly three de...
04/30/2020

A Lower Bound on the stability region of Redundancy-d with FIFO service discipline

Redundancy-d (R(d)) is a load balancing method used to route incoming jo...
07/18/2017

Asymptotically Optimal Load Balancing Topologies

We consider a system of N servers inter-connected by some underlying gra...
05/09/2017

Affinity Scheduling and the Applications on Data Center Scheduling with Data Locality

MapReduce framework is the de facto standard in Hadoop. Considering the ...
08/21/2018

Heavy-traffic Delay Optimality in Pull-based Load Balancing Systems: Necessary and Sufficient Conditions

In this paper, we consider a load balancing system under a general pull-...
03/31/2019

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

MapReduce framework is the de facto in big data and its applications whe...
04/15/2019

Consistent Dynamic CDN Server Assignment for Online Video Streaming with Optimality Guarantees

Server assignment plays an essential part in Content Delivery Network (C...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.