BOLAS+: Scalable Lightweight Locality-Aware Scheduling for Hadoop

Gao, Shengli; Xue, Ruini

doi:10.1109/trustcom.2016.0178

Cited by 3 publications

(1 citation statement)

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“…KM algorithm is a combinatorial optimization algorithm that solves the assignment problem in polynomial time which anticipates latest primal‐dual methods 72 . KM algorithm is widely used in weighted bipartite graph model to realize task scheduling, such as References 54,55,73. The KM algorithm 72 is an efficient way to find the maximum weight perfect matching in a weighted bipartite graph and find a good feasible labeling that remains enough edges in graph.…”

Section: A Data Locality Aware Task Schedulermentioning

confidence: 99%

An efficient deadline constrained and data locality aware dynamic scheduling framework for multitenancy clouds

Yang

Grundy

et al. 2020

Concurrency and Computation

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Summary Scheduling and resource allocation in clouds is used to harness the power of the underlying resource pool. Service providers can meet quality of service (QoS) requirements of tenants specified in Service Level Agreements. Improving resource allocation ensures that all tenants will receive fairer access to system resources, which improves overall utilization and throughput. Real‐time applications and services require critical deadlines in order to guarantee QoS. A growing number of data‐intensive applications drive the optimization of scheduling through utilizing data locality in which the scheduler locates a task and ensures the task's relevant data to be on the same server. Choosing suitable scheduling mechanisms for running applications that support multitenancy has consistently been a major challenge. This work proposes a new adaptive Deadline constrained and Data locality aware Dynamic Scheduling Framework “ 3DSF“ that orchestrates different schedulers based on varied requirements. This framework considers tenants' deadline‐based QoS requirements, cloud system's performance and a method of resource allocation to improve resource utilization, system throughput and reduce jobs' completion time. 3DSF contains: (a) a real‐time, preemptive, deadline constrained job scheduler, (b) an optimized data locality aware scheduler, (c) an improved Dominant Resource Fairness greedy resource allocation approach, and (d) an adaptive suite to integrate above‐mentioned schedulers together.

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Section: A Data Locality Aware Task Schedulermentioning

confidence: 99%