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Advances, Systems and Applications

Table 1 Job scheduler comparisons

From: CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning

Scheduling method

Locality level

Technique

Advantages

Disadvantages

HybSMRP

DL (Map tasks)

Localization marker, Job priority

Avoids wastage of resources

Does not consider some environmental features for job priority

LARD

CL

Predict cached data location

It is suitable for small files

Poor performance for large dataset

Improved CL, and DL for map tasks

DL, CL (Map tasks)

Weighted bipartite graph, Maximum matching algorithm

Improves data locality rate for Map tasks

Only considers Map tasks

ACL

DL, CL

Delay tasks to launch them on local nodes

Increase tasks locality rate

Latency to schedule tasks

CATS

CL

Buffer cache probe and find tasks with the greatest amount of cached data

Increases cache locality rate

Only considers cache locality

CAVA

VMM, PMML, RMML, VMDL

Memory locality-aware and application’s cache affinity

It is suitable for virtualized clusters

Only focuses on Map tasks