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BENCHMARKING INTERACTIVE SOCIAL
NETWORKING ACTIONS
SUMITA BARAHMAND
DEPARTMENT OF
COMPUTER SCIENCE
ADVISER: PROFESSOR SHAHRAM GHANDEHARIZADEH
SPRING 2014
Object Description
| Title | Benchmarking interactive social networking actions |
| Author | Barahmand, Sumita |
| Author email | barahman@usc.edu;sumita.barahmand@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2014-02-24 |
| Date submitted | 2014-04-29 |
| Date approved | 2014-04-29 |
| Restricted until | 2014-04-29 |
| Date published | 2014-04-29 |
| Advisor (committee chair) | Ghandeharizadeh, Shahram |
| Advisor (committee member) |
Govindan, Ramesh Medvidović, Nenad Medvidovic, Nenad Krishnamachari, Bhaskar |
| Abstract | Social networking sites such as Google+, Facebook, Twitter and LinkedIn, are cloud service providers for person to person communications. There are different approaches to building these sites ranging from SQL to NoSQL and NewSQL, Cache Augmented SQL, graph databases and others. Some provide a tabular representation of data while others offer alternative models that scale out. Some may sacrifice strict ACID (Atomicity, Consistency, Isolation, Durability) properties and opt for BASE (Basically Available, Soft‐state, Eventual consistency) to enhance performance. Independent of a qualitative discussion of these approaches and their merits, a key question is how do these systems compare with one another quantitatively? This dissertation investigates the viability of a benchmark to address this question. ❧ Our primary contribution is the design and implementation of a novel benchmark for interactive social networking actions named BG (http://bgbenchmark.org). BG’s design decisions are as follows: First, it rates the performance of a system for processing interactive social networking actions by computing two values: Socialites and Social Action Rating (SoAR) using a pre‐specified Service Level Agreement, SLA. An example SLA may require 95% of issued requests to observe a response time faster than 100 milliseconds. Second, BG elevates the amount of unpredictable data produced by a solution to a first class metric, including it as a key component of the SLA (similar to the average response time) and quantifying it as a part of the benchmarking process. It also computes the freshness confidence to characterize the behavior of a weak consistency technique. Third, BG’s generated workload is characterized by reads and writes of a very small amount of data from big data. Fourth, BG is a modular, extensible framework that is agnostic to its underlying data store. Fifth, BG employs a logical partitioning of data to scale both vertically and horizontally to thousands of nodes. This is essential for evaluating scalable installations consisting of thousands of nodes. Finally, BG includes a visualization tool to empower an evaluator to monitor an in‐progress benchmark and identify bottlenecks. ❧ BG’s possible use cases are diverse. One may use BG to compare and contrast various data stores with one another, characterize tradeoffs associated with alternative physical representations of data, or quantify the behavior of a data store in the presence of various failures (either CP or AP of the CAP theorem) among the others. This dissertation demonstrates use of BG in two contexts. First, to rate an industrial strength relational database management system and a document store, quantifying their performance tradeoffs. This analysis includes the use of a middle tier cache (memcached) and its impact on the performance of each system. Second, to gain insight into alternative design decisions for implementing a social action by characterizing their behavior with different social graphs and system loads. BG’s proposed framework is quite novel and opens several new research directions that benefit the systems research community. |
| Keyword | benchmarks; social networks; data store; data consistency; data freshness; SoAR; socialite; unpredictable data; scalability; performance evaluation |
| Language | English |
| Format (imt) | application/pdf |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Barahmand, Sumita |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@dots.usc.edu |
| Filename | etd-BarahmandS-2457.pdf |
| Archival file | uscthesesreloadpub_Volume8/etd-BarahmandS-2457.pdf |
Description
| Title | Page 1 |
| Repository email | cisadmin@dots.usc.edu |
| Full text | BENCHMARKING INTERACTIVE SOCIAL NETWORKING ACTIONS SUMITA BARAHMAND DEPARTMENT OF COMPUTER SCIENCE ADVISER: PROFESSOR SHAHRAM GHANDEHARIZADEH SPRING 2014 |
