Paper Info
Title
Partition with Side Effects.
Abstract
In data centers, many tasks (services, virtual machines or computational jobs) share a single physical machine. We propose a new resource management model for such colocation. Our model uses two parameters of a task -- its size and its type -- to characterize how a task influences the performance of the other tasks allocated on the same machine. As typically a data~center hosts many similar, recurring tasks (e.g.: a webserver, a database, a CPU-intensive computation), the resource manager should be able to construct these types and their performance interactions. Moreover, realistic variants of our model are polynomially-solvable, in contrast to the NP-hard vector packing used previously. In particular, we minimize the total cost in a model in which each task's cost is a function of the total sizes of tasks allocated on the same machine (each type is counted separately). We show that for a linear cost function the problem is strongly NP-hard, but polynomially-solvable in some particular cases. We propose an algorithm polynomial in the number of tasks (but exponential in the number of types and machines), and another algorithm polynomial in the number of tasks and machines (but exponential in the number of types and admissible sizes of tasks). When there is a single type, we give a polynomial time algorithm. We also prove that, even for a single type, the problem becomes NP-hard for convex costs.
Year
DOI
Venue
2015
10.1109/HiPC.2015.52
HiPC
Keywords
Field
DocType
data center, heterogeneity, resource management, scheduling, colocation, co-tenancy, partition, complexity, algorithm
Resource management,Virtual machine,Exponential function,Polynomial,Computer science,Scheduling (computing),Parallel computing,Theoretical computer science,Time complexity,Partition (number theory),Computation,Distributed computing
Conference
Citations 
PageRank 
References 
1
0.35
17
Authors
2
Name
Order
Citations
PageRank
Fanny Pascual19714.48
Krzysztof Rzadca220919.13