I am proudly present a Thai sukiyaki style or we often called “suki”. its very similar to Japanese sukiyaki or Chinese hot pot or shabu shabu. The only major different is we have a tasty spicy dipping source.
Disk scheduling policies with lookahead, A. Thomasian, C. Liu, ACM SIGMETRICS Vol. 30, No. 2, September 2002, pp. 31-40.
Disk scheduling methods that we might already know are concerned with minimizing the seek time, for example, the FCFS and the SSTF methods. However, the summation of both seeks and latency time is more preferable in modern disk. Therefore, the authors introduce some new disk scheduling methods. For example, the SATF policy which takes into account the sum of seek time and latency time is therefore preferable.The authors review the major disk scheduling methods such as FCFS, SSTF, CSCAN, CSCAN-Lai, SATF, SATF, HOL and SATF-RP. They describe the simulation model used to evaluate the relative performance of the disk scheduling methods, and analyze the simulation regarding to those methods. The main contribute is that they extended CSCAN and SATF with look ahead to be able to cope with the dynamic nature of arrivals to the system.
As we might know, we don’t concern a capacity of disk as a major issue like before, and the speed of the seek time became much faster than before. I believe a disk scheduling method is suited for some specific data, it seems to me like there will not be such a method that can optimize all data which is stored in the disk. My question is that they should have a disk scheduling method which acts like the MTLQ (Multi level queue) that we have studied in the early chapter, where we could select right algorithm and move up and down depends on the starvation level. That should be very more interesting.
In my opinion, the read and write speed could improve by increasing speed of motor and some more mechanical stuff rather than using scheduling methods, of course there would be some improvement but only minor, since today we don’t feel that the bottleneck of transferring data is occurs at memory device.
For this paper, I had like to rate the significance of this paper as 3/5(modest), because 20% of the paper review the scheduling methods which most of us already know, the simulation doesn’t show us a significant result of improvement of disk utilization, and this should be the most noticeable deficiency of the paper.
To be able improve applicability, scalability, performance and availability in data storage for large data, the authors have implemented and deployed a distributed storage system which is called Bigtable, and this would be the main motivation of the paper. To manage large data, the system provides a simple data model for dynamic control over data layout and format for clients as describe as following paragraph.
For their contributions, the authors have spent roughly seven person-years on design and implementation. They have introduced an interesting model which a map data structure, the concept of row and column families, and time stamps which form the basic unit of access control and so on. Also the refinements and the performance evaluation which describes in the paper have shown an improvement. Three of the real applications or products have success by using the Bigtable implementation and concepts.
The paper’s single most noticeable deficiency already describes by the authors in the paper which are the following. For example, consideration of the possibility of multiple copies of the same data doesn’t count; a permission to let the user tell us what data belongs in memory and what data should stay on the disk rather than trying to determine this dynamically. Lastly, there are no complex queries to execute or optimize. The Bigtable seems to take to another whole level of manipulating the data, however my question is still concerned about the networking such that it seems to me that the latency plays an important role to be able to retrieve or display the result of queries. In my personal opinion, there is still a bottle neck because it is a distribute servers which require a high-performance network infrastructure to achieve the highest performance.
I would rate the significant of the paper 5/5(breakthrough) because of the Bigtable model system is amazing such that it could adapts to handle some very large data, and it has been used in many popular application that we have been using nowadays, for examples, Google products such as Google earth and Google analytics and etc. The concept of adding a new machine when it needs more performance to perform database operations is spectacularly. I believe that the Bigtable will be very useful in future use, and we will most likely to see the next coming products from such companies take this model to approve their use of database.
Bigtable: A Distributed Storage System for Structured Data, F. Chang, J. Dean, S. Ghemawat, W. Hsieh, D. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. Gruber, Proc. of the 7th Conf. on USENIX Sym. on Operating Systems Design and Implementation, November 2006, pp. 205-218.
Serverless Network File Systems, T. Anderson, M. Dahlin, J. Neefe, D. Patterson, D. Roselli, and R. Wang, Proc. of the 15th ACM Symposium on Operating Systems Principles, December 1995, pp. 109-126.
The authors believe that the traditional central network file system still has a bottle neck, such that all the miss read/write goes through the central server. It is also expensive, such that it requires man to control or operate the server to be able to balance the server loads. Therefore, they have introduced a server less file systems distribute file system server which responsibilities across large numbers of cooperating machines. Ideally, the authors have implemented a prototype serverless network file system called xFS to provide better performance and scalability than traditional file systems.
There are three factors which motivate their work on the implementation of the serverless network file systems: the first one is the opportunity to provided by fast switched LANs, the second one is the expanding demands of users and the last one is the fundamental limitations of central server systems.Taking about their contributions, the authors make two sets of contributions. Firstly, xFs synthesizes a number of recent innovations which provide a basis for serverless file system design. Secondly, they have transformed DASH’s scalable cache consistency approach into a more general, distributed control system that is also fault tolerant. Moreover, they have improved the Zebra to eliminate bottlenecks.
The paper’s single most noticeable deficiency is the limitation of the measurements, such that the workloads are not real workloads, and they are micro benchmarks that provide a better performance in term of parallelism than real workloads. Another limitation of the measurements is that they compare against NFS, hence scalability is limited.
This paper seems very solid and interesting to me, I like many ideas, for example, the idea of taking advantage of the cooperative caching to server client memory. However, I still have a question regarding to the future work and its limitation such that, what would be a real workloads the author most likely to measure on and how much expectation would the author prefer to see according to such workloads.
I would rate this paper 5/5(breakthrough) due to the challenging idea and how the authors implements and their measurements. It improves the old fashion server in term of performance, scalability, and availability. It could also help reduce the cost of hardware.