TY - JOUR

T1 - Assignment problems of different-sized inputs in MapReduce

AU - Afrati, Foto

AU - Dolev, Shlomi

AU - Korach, Ephraim

AU - Sharma, Shantanu

AU - Ullman, Jeffrey D.

N1 - Funding Information:
This work of F. Afrati is supported by the project Handling Uncertainty in Data Intensive Applications, cofinanced by the European Union (European Social Fund) and Greek national funds, through the Operational Program "Education and Lifelong Learning," under the program THALES. This work of S. Dolev is partially supported by Rita Altura Trust Chair in Computer Sciences, Lynne and William Frankel Center for Computer Sciences, Israel Science Foundation (grant number 428/11), Cabarnit Cyber Security MAGNET Consortium, and Ministry of Science and Technology, Infrastructure Research in the Field of Advanced Computing and Cyber Security.
Publisher Copyright:
© 2016 ACM.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs participating in the computation of this output. Reducers have a capacity that limits the sets of inputs they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which each input of a list, X, is required to meet each input of another list, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problems is NP-hard, and present a bin-packing-based approximation algorithm for finding a near optimal mapping schema.

AB - A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs participating in the computation of this output. Reducers have a capacity that limits the sets of inputs they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which each input of a list, X, is required to meet each input of another list, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problems is NP-hard, and present a bin-packing-based approximation algorithm for finding a near optimal mapping schema.

UR - http://www.scopus.com/inward/record.url?scp=85006998093&partnerID=8YFLogxK

U2 - 10.1145/2987376

DO - 10.1145/2987376

M3 - Article

AN - SCOPUS:85006998093

VL - 11

JO - ACM Transactions on Knowledge Discovery from Data

JF - ACM Transactions on Knowledge Discovery from Data

SN - 1556-4681

IS - 2

M1 - 18

ER -