115 research outputs found
Experimental Analysis of Algorithms for Coflow Scheduling
Modern data centers face new scheduling challenges in optimizing job-level
performance objectives, where a significant challenge is the scheduling of
highly parallel data flows with a common performance goal (e.g., the shuffle
operations in MapReduce applications). Chowdhury and Stoica introduced the
coflow abstraction to capture these parallel communication patterns, and
Chowdhury et al. proposed effective heuristics to schedule coflows efficiently.
In our previous paper, we considered the strongly NP-hard problem of minimizing
the total weighted completion time of coflows with release dates, and developed
the first polynomial-time scheduling algorithms with O(1)-approximation ratios.
In this paper, we carry out a comprehensive experimental analysis on a
Facebook trace and extensive simulated instances to evaluate the practical
performance of several algorithms for coflow scheduling, including the
approximation algorithms developed in our previous paper. Our experiments
suggest that simple algorithms provide effective approximations of the optimal,
and that the performance of our approximation algorithms is relatively robust,
near optimal, and always among the best compared with the other algorithms, in
both the offline and online settings.Comment: 29 pages, 8 figures, 11 table
Weighted Scheduling of Time-Sensitive Coflows
Datacenter networks commonly facilitate the transmission of data in
distributed computing frameworks through coflows, which are collections of
parallel flows associated with a common task. Most of the existing research has
concentrated on scheduling coflows to minimize the time required for their
completion, i.e., to optimize the average dispatch rate of coflows in the
network fabric. Nevertheless, modern applications often produce coflows that
are specifically intended for online services and mission-crucial computational
tasks, necessitating adherence to specific deadlines for their completion. In
this paper, we introduce \wdcoflow,~ a new algorithm to maximize the weighted
number of coflows that complete before their deadline. By combining a dynamic
programming algorithm along with parallel inequalities, our heuristic solution
performs at once coflow admission control and coflow prioritization, imposing a
-order on the set of coflows. With extensive simulation, we demonstrate
the effectiveness of our algorithm in improving up to more coflows
that meet their deadline in comparison the best SoA solution, namely
. Furthermore, when weights are used to differentiate
coflow classes, \wdcoflow~ is able to improve the admission per class up to
, while increasing the average weighted coflow admission rate.Comment: Submitted to IEEE Transactions on Cloud Computing. Parts of this work
have been presented at IFIP Networking 202
Integrality Gap of Time-Indexed Linear Programming Relaxation for Coflow Scheduling
Coflow is a set of related parallel data flows in a network. The goal of the coflow scheduling is to process all the demands of the given coflows while minimizing the weighted completion time. It is known that the coflow scheduling problem admits several polynomial-time 5-approximation algorithms that compute solutions by rounding linear programming (LP) relaxations of the problem. In this paper, we investigate the time-indexed LP relaxation for coflow scheduling. We show that the integrality gap of the time-indexed LP relaxation is at most 4. We also show that yet another polynomial-time 5-approximation algorithm can be obtained by rounding the solutions to the time-indexed LP relaxation
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