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CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
A SIMPLE MESSAGE-OPTIMAL ALGORITHM FOR RANDOM SAMPLING FROM A
DISTRIBUTED STREAM
ABSTRACT
We present a simple, message-optimal algorithm for maintaining a random
sample from a large data stream whose input elements are distributed across
multiple sites that communicate via a central coordinator. At any point in time,
the set of elements held by the coordinator represent a uniform random
sample from the set of all the elements observed so far. When compared with
prior work, our algorithms asymptotically improve the total number of
messages sent in the system. We present a matching lower bound, showing
that our protocol sends the optimal number of messages up to a constant
factor with large probability. We also consider the important case when the
distribution of elements across different sites is non-uniform, and show that
for such inputs, our algorithm significantly outperforms prior solutions.
CONCLUSION
We presented a simple message-optimal algorithm for maintaining a uniform
random sample, with or without replacement, from a distributed stream. Our
theoretical results and experiments show that this algorithm outperforms
previous methods with respect to message complexity under a variety of
scenarios, especially when the distribution of elements across sites is skewed.
CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
The correctness of our algorithm is simple to establish. Though our analysis of
its message complexity is based on a synchronous model as described in
Section 2 the algorithm works in the absence of synchronous communication,
in the following sense. Consider an asynchronous distributed system where the
message delays are not predictable. Consider the state of the system at some
instant in time, and suppose that we stopped adding further elements to the
local streams
REFERENCES
[1] G. Cormode, S. Muthukrishnan, and K. Yi, “Algorithms for distributed
functional monitoring,” in Proc. 19th Annu. ACM-SIAM Symp. Discr. Algorithm,
2008, pp. 1076–1085.
[2] D. E. Knuth, The Art of Computer Programming, Volume II: Seminumerical
Algorithms, 2nd ed. Reading, MA, USA: Addison-Wesley, 1981.
*3+ J. S. Vitter, “Random sampling with a reservoir,” ACM Trans. Math. Softw.,
vol. 11, no. 1, pp. 37–57, 1985.
[4] L. Huang, X. Nguyen, M. N. Garofalakis, J. M. Hellerstein, M. I. Jordan, A. D.
Joseph, and N. Taft, “Communication-efficient online detection of network-
wide anomalies,” in Proc. IEEE INFOCOM, 2007, pp. 134–142.
*5+ G. Cormode and M. N. Garofalakis, “Sketching streams through the net:
Distributed approximate query tracking,” in Proc. 31st Int. Conf. Very Large
Databases, 2005, pp. 13–24.
CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
*6+ B. Babcock and C. Olston, “Distributed top-k monitoring,” in Proc. SIGMOD
Conf., 2003, pp. 28–39.
[7] R. Keralapura, G. Cormode, and J. Ramamirtham, “Communication-efficient
distributed monitoring of thresholded counts,” in Proc. ACM SIGMOD Int. Conf.
Manage. Data, 2006, pp. 289–300.
[8] A. Manjhi, V. Shkapenyuk, K. Dhamdhere, and C. Olston, “Finding (recently)
frequent items in distributed data streams,” in Proc. 21st Int. Conf. Data Eng.,
2005, pp. 767–778.
[9] K. Yi and Q. Zhang, “Optimal tracking of distributed heavy hitters and
quantiles,” in Proc. 28th ACM SIGMOD-SIGACT-SIGART Symp. Principles Distrib.
Comput., 2009, pp. 167–174.
[10] S. Agarwal, B. Mozafari, A. Panda, H. Milner, S. Madden, and I. Stoica,
“BlinkDB: Queries with bounded errors and bounded response times on very
large data,” in Proc. 8th Eurosys Conf., 2013, pp. 29–42.

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A SIMPLE MESSAGE-OPTIMAL ALGORITHM FOR RANDOM SAMPLING FROM A DISTRIBUTED STREAM

  • 1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com A SIMPLE MESSAGE-OPTIMAL ALGORITHM FOR RANDOM SAMPLING FROM A DISTRIBUTED STREAM ABSTRACT We present a simple, message-optimal algorithm for maintaining a random sample from a large data stream whose input elements are distributed across multiple sites that communicate via a central coordinator. At any point in time, the set of elements held by the coordinator represent a uniform random sample from the set of all the elements observed so far. When compared with prior work, our algorithms asymptotically improve the total number of messages sent in the system. We present a matching lower bound, showing that our protocol sends the optimal number of messages up to a constant factor with large probability. We also consider the important case when the distribution of elements across different sites is non-uniform, and show that for such inputs, our algorithm significantly outperforms prior solutions. CONCLUSION We presented a simple message-optimal algorithm for maintaining a uniform random sample, with or without replacement, from a distributed stream. Our theoretical results and experiments show that this algorithm outperforms previous methods with respect to message complexity under a variety of scenarios, especially when the distribution of elements across sites is skewed.
  • 2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com The correctness of our algorithm is simple to establish. Though our analysis of its message complexity is based on a synchronous model as described in Section 2 the algorithm works in the absence of synchronous communication, in the following sense. Consider an asynchronous distributed system where the message delays are not predictable. Consider the state of the system at some instant in time, and suppose that we stopped adding further elements to the local streams REFERENCES [1] G. Cormode, S. Muthukrishnan, and K. Yi, “Algorithms for distributed functional monitoring,” in Proc. 19th Annu. ACM-SIAM Symp. Discr. Algorithm, 2008, pp. 1076–1085. [2] D. E. Knuth, The Art of Computer Programming, Volume II: Seminumerical Algorithms, 2nd ed. Reading, MA, USA: Addison-Wesley, 1981. *3+ J. S. Vitter, “Random sampling with a reservoir,” ACM Trans. Math. Softw., vol. 11, no. 1, pp. 37–57, 1985. [4] L. Huang, X. Nguyen, M. N. Garofalakis, J. M. Hellerstein, M. I. Jordan, A. D. Joseph, and N. Taft, “Communication-efficient online detection of network- wide anomalies,” in Proc. IEEE INFOCOM, 2007, pp. 134–142. *5+ G. Cormode and M. N. Garofalakis, “Sketching streams through the net: Distributed approximate query tracking,” in Proc. 31st Int. Conf. Very Large Databases, 2005, pp. 13–24.
  • 3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com *6+ B. Babcock and C. Olston, “Distributed top-k monitoring,” in Proc. SIGMOD Conf., 2003, pp. 28–39. [7] R. Keralapura, G. Cormode, and J. Ramamirtham, “Communication-efficient distributed monitoring of thresholded counts,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2006, pp. 289–300. [8] A. Manjhi, V. Shkapenyuk, K. Dhamdhere, and C. Olston, “Finding (recently) frequent items in distributed data streams,” in Proc. 21st Int. Conf. Data Eng., 2005, pp. 767–778. [9] K. Yi and Q. Zhang, “Optimal tracking of distributed heavy hitters and quantiles,” in Proc. 28th ACM SIGMOD-SIGACT-SIGART Symp. Principles Distrib. Comput., 2009, pp. 167–174. [10] S. Agarwal, B. Mozafari, A. Panda, H. Milner, S. Madden, and I. Stoica, “BlinkDB: Queries with bounded errors and bounded response times on very large data,” in Proc. 8th Eurosys Conf., 2013, pp. 29–42.