Data Replication and Power Consumption in Data Grids Susan V. Vrbsky, Ming Lei, Karl Smith and Jeff Byrd Department of Computer Science The University of Alabama IEEE 2010 Cloud Computing Technology and Science March 16, 2011 Taikyoung Kim SNU IDB Lab.

Outline  Introduction  Data Replication  Performance Results  Conclusion and Future Work 2

Introduction  Data grid features –Millions of files are generated and thousands of clients access the files –Need to manage an extremely large number of data sets  Present systems support scalability, but extremely energy inefficient –Power and cooling of the data center are inefficient –The power demanded by data centers is predicted to double from 2006 to 2011  Storing, managing and moving massive amounts of data are also a significant bottleneck 3

Introduction  Our approach –Save energy through the use of efficient CPU usage –Consider strategies to minimize disk storage and data transmission  We propose to minimize the amount of data stored by utilizing smart replication strategies –Consider replicating the data only when necessary  Goal –Design data aware strategies for data-intensive computing  Shorter running times  Decreased amount of data transmitted  Smaller storage space –Reduce power needed 4

Outline  Introduction  Data Replication –Data Grid Architecture –Sliding Window Strategy  Performance Results  Conclusion and Future Work 5

Data Replication  Utilize data replication –High probability to access data which is not in the local site –Remote data file access can be a very expensive operation  Network bandwidth, network congestion –It reduces the access time and avoids remote file access  limit size of the storage –To decrease the amount of energy needed to store the data  Use of smart data replication to reduce the cost of accessing and storing data 6

Data Replication Data Grid Architecture  We consider only single-tier grids –Expect the strategies developed for single-tier grids can be used within the multi-tier structure  It is common for a job in a data grid to list all the files needed to complete its task –We utilize this aspect in designing a data replication scheme 7

Data Replication Sliding Window Strategy  SWIN [Sliding Window replica scheme] –Consider the file access times in the future and local site Storage Element size –Build a “sliding window” that is a set of distinct files which will be used immediately in the future  Includes all the files the current job will access and the distinct files from the next arriving jobs  The sum of the files in the sliding window will be at most the size of the local Storage Element –Slides forward on more file each time the system finishes processing one file  Keep changing in this way 8

Data Replication Sliding Window Strategy  Q= : a set of jobs  FAS(J i )= : file accessing sequence (f in ≠f im )  G_FAS= : global file accessing sequence  POS(f x,G_FAS): return the first position of f x in G_FAS  Sliding Window rules 1.The sum of the sizes of all the files in the sliding window ≤ Size(SE) 2.No duplicated files exist in the sliding window 3.Any files in the sliding window will not be in a position before the POS(f K,G_FAS) 4.Any files not in the sliding window will be in a position after POS(f m,G_FAS) 9

Outline  Introduction  Data Replication  Performance Results –Performance Environment –Number of Nodes Powered On –File Availability  Conclusion and Future Work 10

Performance Results  Evaluate the performance of SWIN replica strategy using Sage- built at the University of Alabama  Sage nodes –Intel D201GLY2 mainboard with 1.2 GHz Celeron CPU  On-board 10/100 Megabit LAN –1 Gb 533 MHz RAM –80 Gb SATA 3 hard drive  Energy usage rates –Booting and peak : 430 Watts –Idle : 335 Watts (Cooling fans turned on) 315 Watts (Cooling fans turned off) 11

Performance Results Performance Environment  The client nodes are responsible for –Processing the request –Maintaining replica copies –Notifying the server when a job is completed  Default experiment parameters  Metric –Total running time –Average number of watts required to process a job  Sampled every 1 minute 12 (400MB)

Performance Results Number of Nodes Powered On  The power consumed is affected by whether or not all of the nodes are powered on –Regardless of whether they are being used in the computation of the jobs 13 LFU -Least Frequently Used LRU -Least Recently Used MRU -Most Recently Used

Performance Results Number of Client Nodes  Measured the total running time for 100 jobs with all nodes powered on 14

Performance Results Number of Client Nodes  While LRU requires the most watts, it has a shorter running time overall than LFU and MRU –Does not require the highest number of watts  The jobs with only 1 or 2 client nodes take longer to run than those utilizing 8 client nodes  The watts required for computation is a smaller percentage of the total watts 15

Performance Results File Availability  The files are only available at the server –(a) The jobs are able to run in a shorter amount of time as clients increase –(b) The bottleneck increases as the number of client nodes increases  Assume all file requests must go through the resource broker at the server  The amount of power consumed is not always strictly related to the running time of the jobs  Lastly, have shown that the window size can be decreased without increasing the running time or power consumed 16

Outline  Introduction  Data Replication  Performance Results  Conclusion and Future Work 17

Conclusion and Future Work  Propose the smart strategies for replication files –One way to minimize the energy consumed in data grid  SWIN strategy –Minimize the amount of data transmitted and storage needed –Performs better than existing strategies, such as LRU, MRU and LFU –Particularly beneficial in power saving when resource contention is high –Decrease running time and watts required  Smaller storage can be used to lower the amount of power  Future work –Study the performance of SWIN when the files are of different sizes –Explore more efficient implementations for transferring files –Design and test additional replica schemes by utilizing the CPU –Consider ways to schedule the jobs 18

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