Time-Saving First: Coflow Scheduling for Datacenter Networks

Borjigin, Wuyunzhaola; Ota, Kaoru; Dong, Mianxiong

doi:10.1109/vtcfall.2017.8288339

Cited by 2 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Faced with this problem, researchers have proposed some online information-agnostic coflow scheduling schemes [ 22 , 23 , 24 ] in which they deduce each coflow’s remaining transmission time and divide coflows into different priority queues according to the known attributes or arrived bytes. In the second multi-stage coflow scheduling type, researchers scheduled dependent coflows from precedence-constrained job stages [ 25 , 26 , 27 , 28 , 29 ]. From the perspective of job-level performance rather than the coflow-level, meeting job deadlines is not equivalent to simply minimizing each CCT.…”

Section: Introductionmentioning

confidence: 99%

“…In their solution, the first level performs the Most Bottleneck First (MBF) algorithm to determine job orders, and the second level performs intra-job and intra-coflow scheduling while considering different coflow dependencies. W. Borjigin et al divided dependent coflows into different stages and presented a heuristic multi-objective time-saving first (MTF) scheduling algorithm to guarantee meeting job deadlines [ 26 ]. S. Zhang formulated the multi-stage inter-coflow scheduling (MICS) problem, partitioned MICS into multiple convex single-stage inter-coflow scheduling (SICS) problems, and designed three online heuristics to balance the fairness and completion times of coflows [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks

Suo,

Ma,

Jiao

et al. 2023

Sensors

View full text Add to dashboard Cite

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.

show abstract