International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

line decor
  
line decor

 Volume 7, Issue 8 (August 2020), Pages: 53-64

----------------------------------------------

 Review Paper

 Title: A secure operating system for data centers: A survey

 Author(s): Sikandar Ejaz 1, *, Muhammad Javed Iqbal 1, Hafsa Bibi 1, Shahbaz Pervez 2, Kawther A. Al-Dhlan 3, Seyed Ebrahim Hosseini 2

 Affiliation(s):

 1Computer Science Department, University of Engineering and Technology, Taxila, Pakistan
 2Department of Information Technology, Abacus Institute of Studies, Christchurch, New Zealand
 3Computer Science and Information Department, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-0232-2356

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.08.007

 Abstract:

Data centers are now evolving source of computational hardware which have high potential to bring extraordinary computing capacity to use applications with resource sharing, fault tolerance, security, and scalability. To deliver the user with efficient computational power, with the support of data sharing, resource sharing and abstraction, an operating system-like software stack is needed for cloud computing hardware platforms. Existing distributed operating systems are not scalable to handle thousands of machines in clouds. As a result, current cloud computing environments are more complex at the user side. This paper surveys the existing data center functional platforms and discusses their worth and cost, to emphasis on development of a long-term mechanism with lasting impacts for present and future data center software infrastructure demands by considering all these factors which will help the organizations to select the best operating system for datacenter as per their particular needs and priorities. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Data centers, Operating system, Resource sharing, Security, Scalability, Application development, Cluster, Cloud computing

 Article History: Received 11 March 2019, Received in revised form 25 April 2020, Accepted 2 May 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The authors declare that they have no conflict of interest.

 Citation:

 Ejaz S, Iqbal MJ, and Bibi H et al. (2020). A secure operating system for data centers: A survey. International Journal of Advanced and Applied Sciences, 7(8): 53-64

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1

----------------------------------------------

 References (30)

  1. Aviram A, Weng SC, Hu S, and Ford B (2012). Efficient system-enforced deterministic parallelism. Communications of the ACM, 55(5): 111-119. https://doi.org/10.1145/2160718.2160742   [Google Scholar]
  2. Bahl P, Han RY, Li LE, and Satyanarayanan M (2012). Advancing the state of mobile cloud computing. In the 3rd ACM Workshop on Mobile Cloud Computing and Services, ACM, Low Wood Bay, Lake District, UK: 21-28. https://doi.org/10.1145/2307849.2307856   [Google Scholar]
  3. Barroso LA, Clidaras J, and Hölzle U (2013). The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture, 8(3): 1-154. https://doi.org/10.2200/S00516ED2V01Y201306CAC024   [Google Scholar]
  4. Belay A, Prekas G, Klimovic A, Grossman S, Kozyrakis C, and Bugnion E (2014). {IX}: A protected dataplane operating system for high throughput and low latency. In the 11th {USENIX} Symposium on Operating Systems Design and Implementation, USENIX Association, Broomfield, USA: 49-65.   [Google Scholar]
  5. Burrows M (2006). The Chubby lock service for loosely-coupled distributed systems. In the 7th Symposium on Operating Systems Design and Implementation, USENIX Association, Seattle, USA: 335-350.   [Google Scholar]
  6. Comminiello D, Michele S, Simone S, Raffaele P, and Aurelio U (2016). Smart innovation, systems and technologies. Springer Science and Business Media, Berlin, Germany.   [Google Scholar]
  7. Dean J and Ghemawat S (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1): 107-113. https://doi.org/10.1145/1327452.1327492   [Google Scholar]
  8. Fetterly YYMID, Budiu M, Erlingsson Ú, and Currey PKGJ (2009). DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. In the 8th USENIX Symposium on Operating Systems Design and Implementation, USENIX Association.   [Google Scholar]
  9. Fonseca R, Porter G, Katz RH, Shenker S, and Stoica I (2007). X-trace: A pervasive network tracing framework. In the 4th USENIX Conference on Networked Systems Design and Implementation, USENIX Association, Cambridge, USA: 20-20.   [Google Scholar]
  10. Greenberg A, Hamilton J, Maltz DA, and Patel P (2008). The cost of a cloud: research problems in data center networks. ACM SIGCOMM Computer Communication Review, 39(1): 68-73. https://doi.org/10.1145/1496091.1496103   [Google Scholar]
  11. Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz RH, and Stoica I (2011). Mesos: A platform for fine-grained resource sharing in the data center. In the 8th USENIX conference on Networked systems design and implementation: 295-308.   [Google Scholar]
  12. Isard M, Budiu M, Yu Y, Birrell A, and Fetterly D (2007). Dryad: Distributed data-parallel programs from sequential building blocks. In the ACM SIGOPS Operating Systems Review, ACM, Lisbon, Portugal, 41(3): 59-72. https://doi.org/10.1145/1272998.1273005   [Google Scholar]
  13. Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, and Goldberg A (2009). Quincy: Fair scheduling for distributed computing clusters. In the ACM SIGOPS 22nd Symposium on Operating Systems Principles, ACM, Big Sky, USA: 261-276. https://doi.org/10.1145/1629575.1629601   [Google Scholar]
  14. Jadeja Y and Modi K (2012). Cloud computing-concepts, architecture and challenges. In the International Conference on Computing, Electronics and Electrical Technologies, IEEE, Kumaracoil, India: 877-880. https://doi.org/10.1109/ICCEET.2012.6203873   [Google Scholar]
  15. Lee KH, Lee YJ, Choi H, Chung YD, and Moon B (2012). Parallel data processing with MapReduce: A survey. ACM SIGMOD Record, 40(4): 11-20. https://doi.org/10.1145/2094114.2094118   [Google Scholar]
  16. Leokhin Y and Panfilov P (2015). A study of cloud/IX operating system for the ARM-based data center server platform. Procedia Engineering, 100: 1696-1705. https://doi.org/10.1016/j.proeng.2015.01.545   [Google Scholar]
  17. Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, and Czajkowski G (2010). Pregel: A system for large-scale graph processing. In the 2010 ACM SIGMOD International Conference on Management of Data, ACM, Indianapolis, USA: 135-146. https://doi.org/10.1145/1807167.1807184   [Google Scholar]
  18. Mullender SJ, Van Rossum G, Tanenbaum AS, Van Renesse R, and Van Staveren H (1990). Amoeba: A distributed operating system for the 1990s. Computer, 23(5): 44-53. https://doi.org/10.1109/2.53354   [Google Scholar]
  19. Patterson DA (2008). The data center is the computer. Communications of the ACM, 51(1): 105-105. https://doi.org/10.1145/1327452.1327491   [Google Scholar]
  20. Sakr S, Liu A, and Fayoumi AG (2013). The family of mapreduce and large-scale data processing systems. ACM Computing Surveys, 46: 1. https://doi.org/10.1145/2522968.2522979   [Google Scholar]
  21. Shvachko K, Kuang H, Radia S, and Chansler R (2010). The hadoop distributed file system. In the Conference of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), IEEE, Washington, USA: 1-10. https://doi.org/10.1109/MSST.2010.5496972   [Google Scholar]
  22. Tanenbaum AS, Van Renesse R, Van Staveren H, Sharp GJ, and Mullender SJ (1990). Experiences with the amoeba distributed operating system. Communications of the ACM, 33(12): 46-63. https://doi.org/10.1145/96267.96281   [Google Scholar]
  23. Van Renesse R, Van Staveren H, and Tanenbaum AS (1989). The performance of the Amoeba distributed operating system. Software: Practice and Experience, 19(3): 223-234. https://doi.org/10.1002/spe.4380190303   [Google Scholar]
  24. Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, and Chen D (2013). G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Generation Computer Systems, 29(3): 739-750. https://doi.org/10.1016/j.future.2012.09.001   [Google Scholar]
  25. Wang M, Li B, Zhao Y, and Pu G (2014). Formalizing google file system. In the IEEE 20th Pacific Rim International Symposium on Dependable Computing, IEEE, Singapore, Singapore: 190-191. https://doi.org/10.1109/PRDC.2014.32   [Google Scholar]
  26. Zaharia M, Borthakur D, Sen Sarma J, Elmeleegy K, Shenker S, and Stoica I (2010a). Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In the 5th European Conference on Computer Systems, ACM, Paris, France: 265-278. https://doi.org/10.1145/1755913.1755940   [Google Scholar]
  27. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, and Stoica I (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In the 9th USENIX Conference on Networked Systems Design and Implementation, USENIX Association, San Jose, USA.   [Google Scholar]
  28. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, and Stoica I (2010b). Spark: Cluster computing with working sets. HotCloud, 10(10-10): 95. Available online at: https://bit.ly/2zSQieG
  29. Zaharia M, Hindman B, Konwinski A, Ghodsi A, Joseph AD, Katz RH, and Stoica I (2011). The datacenter needs an operating system. In HotCloud. Available online at: https://bit.ly/3eD0UNz
  30. Zhang Q, Cheng L, and Boutaba R (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1): 7-18. https://doi.org/10.1007/s13174-010-0007-6   [Google Scholar]​​​​​​