Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Conserving disk energy in network servers
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Energy conservation techniques for disk array-based servers
Proceedings of the 18th annual international conference on Supercomputing
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Ensemble-level Power Management for Dense Blade Servers
Proceedings of the 33rd annual international symposium on Computer Architecture
Exploiting redundancy to conserve energy in storage systems
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
On evaluating request-distribution schemes for saving energy in server clusters
ISPASS '03 Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
PARAID: A gear-shifting power-aware RAID
ACM Transactions on Storage (TOS)
Intra-disk Parallelism: An Idea Whose Time Has Come
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Analyzing the energy efficiency of a database server
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On energy management, load balancing and replication
ACM SIGMOD Record
FAWNdamentally power-efficient clusters
HotOS'09 Proceedings of the 12th conference on Hot topics in operating systems
Delivering energy proportionality with non energy-proportional systems: optimizing the ensemble
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Wimpy node clusters: what about non-wimpy workloads?
Proceedings of the Sixth International Workshop on Data Management on New Hardware
A case for micro-cellstores: energy-efficient data management on recycled smartphones
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Energy proportionality and performance in data parallel computing clusters
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
A survey on energy-efficient data management
ACM SIGMOD Record
Trojan data layouts: right shoes for a running elephant
Proceedings of the 2nd ACM Symposium on Cloud Computing
Energy efficient scheduling of MapReduce workloads on heterogeneous clusters
Green Computing Middleware on Proceedings of the 2nd International Workshop
Parallel data processing with MapReduce: a survey
ACM SIGMOD Record
DreamWeaver: architectural support for deep sleep
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Energy efficiency for large-scale MapReduce workloads with significant interactive analysis
Proceedings of the 7th ACM european conference on Computer Systems
GreenHadoop: leveraging green energy in data-processing frameworks
Proceedings of the 7th ACM european conference on Computer Systems
Only aggressive elephants are fast elephants
Proceedings of the VLDB Endowment
Towards energy-efficient database cluster design
Proceedings of the VLDB Endowment
Interactive analytical processing in big data systems: a cross-industry study of MapReduce workloads
Proceedings of the VLDB Endowment
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Driver input selection for main-memory multi-way joins
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Investigating hybrid SSD FTL schemes for Hadoop workloads
Proceedings of the ACM International Conference on Computing Frontiers
Energy efficiency for MapReduce workloads: an in-depth study
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Exploiting Redundancies and Deferred Writes to Conserve Energy in Erasure-Coded Storage Clusters
ACM Transactions on Storage (TOS)
Boosting energy efficiency with mirrored data block replication policy and energy scheduler
ACM SIGOPS Operating Systems Review
Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions
Journal of Grid Computing
Journal of Parallel and Distributed Computing
Journal of High Speed Networks
MapReduce framework energy adaptation via temperature awareness
Cluster Computing
Hi-index | 0.00 |
The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming "ubiquitous", the focus of this paper is on developing a framework for systematically considering various MapReduce node power down strategies, and their impact on the overall energy consumption and workload response time. We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently proposed technique called "Covering Set" (CS) that keeps only a small fraction of the nodes powered up during periods of low utilization. At the other extreme is a technique that we propose in this paper, called the All-In Strategy (AIS). AIS uses all the nodes in the cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show that AIS is often the right energy saving strategy.