Scalability issues in Cloud Computing

Capacity scaling

The auto scaling does take advantage of dynamic scaling of easy addition of capacity within the cloud infrastructure. Capacity planning provides an understanding of the traffic patterns alongside their behavior of change periodically alongside the mode of growth is inspired, and the kind of cloud infrastructure does suit and support appropriately the traffic patterns. Capacity planning cannot get avoided through the auto scaling operation. This operation is critically extra large as it goes towards the enabling of combining the infrastructure costs with the benefits realized within the organization upon combining the capacity and demand of the same.

Discussion

After carrying out the research on scalability issues on computer clouding, the following lessons were learned:

Systems of the clients had to be run periodically as they used the internet to be connected to the cloud. A varying bandwidth parameter was determined on the scalability of the cloud as time moved on. It requires considerations before understanding the situation of the connecting terminals well in determining the duration of the connection. Various conditions that were similar to other clients were experienced at the same time hence contributed to the connection issues.

Distribution of network largely relies on the information about the approximate location of the cloud subscribers. Changing of location varies the strengths of the network as far as accessibility of the internet is concerned. Areas with few network boosters and resources have a scarce network. Limitations brought by cloud service providers also contributed to internet access failure. This led to failure in achieving the appropriate scalability.

Use of fiber optic cables, wireless and wired connections were the metrics considered. Similar network shifting limitation was realized through various connections. Physical connections were the only active since shifting from one connection medium to another was restricted to the values of the metrics imposed to serve direct connection to cloud only supported a single type of architecture.

Data are coming from wired connections overlapped with the one from wireless connection hence mixing up the subscribers’ data and information. The configuration of important cloud computing data and information was limited due to the differences in various connections and business models behind the provisioning of data and information connections.

Scalability is the degree to which a system or component is capable of changing or being changed depending on demand, situation or technology. It is calculated as:

Scalability = Uptime / Uptime + Downtime = MTBF / MTBF + MTTR

Where by MTTR Mean time to recover/repair (MTTR). This is the average time taken by cloud services to recover Mean time between failures (MTBF) subtract average time between failures. In case the MTBF is much greater than MTTR then Scalability ≈ 1 – MTTR / MTBF.  Therefore for the system having 0.99 scalabilities it has 1- 0.99 = 0.1 probability of failing

Conclusion

Auto scaling is defined with the capability of enhancing automation with the aim of maintaining the performance of the system and gets the automatic management of the costs of running the cloud architecture. It can be outlined with some welcoming features such that auto scaling does scale out the instances in a seamless and automatic manner when the demand for the same increases. At the same time, it does shed the unrequired cloud instances in an automatic manner thus saving the money at the time when the demand subsides. In the paper, there was an outlined discussion of the various common issues that are associated with auto scaling in consideration of the necessary auto scaling required for the present mechanism. It can get depicted that with auto scaling, more ways have been identified to undertake research at a different level. Based on eth understanding of the study conducted the further works should go further into details about some of the emerging issues that does to do with scalability.

 References

Dougal, R. A., Gao, L., & Liu, S. (2004). Ultracapacitor model with automatic order selection and capacity scaling for dynamic system simulation. Journal of Power Sources, 126(1), 250-257.

Vaze, R., & Heath, R. W. (2007, June). Capacity scaling for MIMO two-way relaying. In Information Theory, 2007. ISIT 2007. IEEE International Symposium on (pp. 1451-1455). IEEE.

Yu, S., Wang, C., Ren, K., & Lou, W. (2010, March). Achieving secure, scalable, and fine-grained data access control in cloud computing. In Infocom, 2010 Proceedings IEEE (pp. 1-9). Ieee.

Lee, J. Y., & Kim, S. D. (2010, November). Software approaches to assuring high scalability in cloud computing. In e-Business Engineering (ICEBE), 2010 IEEE 7th International Conference on (pp. 300-306). IEEE.

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in Write My Essay Today services. If you need a similar paper you can place your order from pay for research paper services.