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Prototype

Prototype. Storage Management in the Cloud with QoS. Gal Lipetz Netali Alima Chen Ackerman Shimi Malka. Vision Recap. The goal of our project is to provide performance management for enterprise disc arrays taking into account QoS specifications. Prototype goals.

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Prototype

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  1. Prototype Storage Management in the Cloud with QoS Gal Lipetz NetaliAlima Chen Ackerman ShimiMalka

  2. Vision Recap The goal of our project is to provide performance management for enterprise disc arrays taking into account QoS specifications.

  3. Prototype goals • Infer and make conclusions concerning the data observation of disc arrays which we receive data from our client (EMC). The data contains traces of memory movement over different time periods. • Measure performance, and compare different possibilities to manage the data in the disc array . • A tool which will give the ability to set different parameters and simulate different algorithms over memory data. • Analyze different data management algorithms, and be able to compare them in different scenarios (different parameters). • Present graphical diagrams of the results that will prove to us that the algorithms are working and we can begin improving .

  4. Key Issues • Parsing data from the machine – we parsed a large amount of data – traces from the machine. • Simulator – we built an application which generates a trace data file similar to the one created by the real machine • Cache Management Algorithms we implemented: 1- Optimal algorithm 2- FAST algorithm Next & Cost algorithms • Correlation between memory locations • Used to make conclusions about the memory content. • Device Utilization

  5. How We Did It? (cont’d) • Parsing: • The data file is a text file containing all memory movement information in the system (read/write). • Each trace (row) represents all I/O requests to a memory location (extent) in a time slice. • Example : • The memory location (extent) is identified by a LUN ID and an Offset. • The first number in the statistics represents the number of random read miss. • The statistics also include sequential read miss, random and sequential write etc.

  6. How We Did It? (cont’d) • Simulator: • Goal is to create a trace data file which has the same format as that of data file required by program • Uses parameters given by user to generate a data file with desired characteristics • File from output inserted as input to DataBase System • Used to verify the algorithms and test the system.

  7. Extent = Memory Location How We Did It? (cont’d) • Cache Management Algorithms: We implemented different algorithms that decide which extents will be placed in the cache, and calculate the hit ratio for that algorithm. • Optimal Algorithm – • Knows what is coming and prepares in the best way possible. • Predicts the memory locations that will have the largest amount of I/O requests, and places them in the cache. • Used for comparison with the other algorithms so we can see how close we are to the best thing possible. • Next Algorithm – • In every iteration the cache contains the popular memory locations from the previous time unit. • Cost Algorithm – • Based on the Next Algorithm, with the addition of a limit on the number of exchanges in the cache. • For example: If currently the cache contains extents 1,2,3 and the most popular extents of the next time unit are 1,10,9 and there is a limit of 1 exchange then the cache will be changed to 1,10,2.

  8. Outcomes • Hit Ratio • Describing the hit ratio for each algorithm

  9. Outcomes • Device Utilization • Describing each device’s utilization for each time unit

  10. Conclusions • Simulator produces relevant data that is parsed by our system • Correlation between memory locations calculated and content based relations found • Measurement found for the effectiveness of each implemented algorithm • Meaningful Graphs created with relevant data for future decision making

  11. Conclusions Cont. • Speed: • The algorithm engine should give an answer in less than 12 hours • 2GB of data should be parsed in less than 12 hours • The queries implemented should give an answer in less than 2 minutes • Reliability: • 100% of the prototype outcome was as expected

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