An Analysis of Real-Time Distributed System under Different Priority Policies
A real time distributed computing has
heterogeneously networked computers to solve a single problem. So
coordination of activities among computers is a complex task and
deadlines make more complex. The performances depend on many
factors such as traffic workloads, database system architecture,
underlying processors, disks speeds, etc. Simulation study have been
performed to analyze the performance under different transaction
scheduling: different workloads, arrival rate, priority policies,
altering slack factors and Preemptive Policy. The performance metric
of the experiments is missed percent that is the percentage of
transaction that the system is unable to complete. The throughput of
the system is depends on the arrival rate of transaction. The
performance can be enhanced with altering the slack factor value.
Working on slack value for the transaction can helps to avoid some
of transactions from killing or aborts. Under the Preemptive Policy,
many extra executions of new transactions can be carried out.
[1] Silberschatz, Korth, Sudarshan-2002, Database system concept,4th (I.E),
McGraow-Hill Pub. 698-709,903
[2] Gray. J,978 "Notes on Database Operating Systems", Operating
Systems: An Advanced Course, Lecture notes in Computer Science, 60
[3] Mohan, C, Lindsay B and Obermark R, 1986,Transaction Management
in the R* Distributed Database Management Systems, ACM TODS,
11(4)
[4] Lampson B and Lomet D, 1993, A new Presumes Commit Optimization
for Two phase Commit, Pro.of 19th VLDB Conf
[5] Oszu M, Valduriez P, 1991, Principles of Distributed Database Systems,
Prentice-Hall,1991
[6] Kohler W, 1981,survey of Techniques for Synchronization and
Recovery in Decentralized Computer System, ACM Computing Surveys,
13(2)
[7] Ramamritham,Son S. H, and DiPippo L, 2004, Real-Time Databases and
Data Services, Real-Time Systems J., vol.28,179-216
[8] Robert A and Garcia-Molina H, 1992, Scheduling Real-Time
Transactions, ACM Trans. on Database Systems, 17(3)
[9] Davidson S., Lee I and Wolfe V.,1989, A protocol for Times Atomic
Commitment, Proc. of 9th Intl. Conf. On Distributed Computing System
[10] Levy E., Korth H and Silberschatz A.1991, An optimistic commit
protocol for distributed transaction management, Pro.of ACM SIGMOD
Conf.
[11] Haritsa J., Carey M, Livney M,-92, Data Access Scheduling in Firm
Real time Database Systems, Real Time systems J, 4(3)
[12] Han Q, 2003, Addressing timeliness /accuracy/ cost tradeoffs in
information collection for dynamic environments, IEEE Real-Time
System Symposium,Cancun, Mexico
[13] Haritsa J., Ramesh G. Kriti.R, S. Seshadri, 1996, "Commit processing in
Distributed Real-Time Database Systems", Tech. Report-TR-96-01, Pro.
Pro. Of 17th IEEE Real-Time Systems Symposium, USA
[14] Haritsa J., Carey M and Livney M, 1990,"Dynamic Real-Time
Optimistic Concurrency Control", Proc. of 11th IEEE Real-Time
Systems Symp.
[15] Haritsa J. 1991, "Transaction Scheduling in Firm Real-Time Database
Systems", Ph.D. Thesis, Computer Science Dept. Univ. of Wisconsin,
Madison
[16] Xiong M. and Ramamritham K., 2004, Deriving Deadlines and Periods
for Real-Time Update Transactions, IEEE Trans. on Computers, vol.
53,(5)
[17] Kang W, Son, S., Stankovic J, and Amirijoo M, 2007, I/O Aware
Deadline Miss Ratio Management in Real-Time Embedded Databases,
IEEE RTSS
[18] Gustavsson S and Andler S, 2005, Decentralized and continuous
consistency management in distributed real-time databases with multiple
writers of replicated data, Workshop on parallel and distributed realtime
systems, Denver, CO
[19] Xiong M, Han S., and Lam K, 2005, A Deferrable Scheduling for Real-
Time Transactions Maintaining Data Freshness, IEEE Real-Time
Systems Symposium, FL
[20] Jan Lindstrom, 2006, "Relaxed Correctness for Firm Real-Time
Databases," rtcsa,pp.82-86, 12th IEEE International Conference on
Embedded and Real-Time Computing Systems and Applications
(RTCSA'06)
[21] Idoudi, N. Duvallet, C. Sadeg, B. Bouaziz, R. Gargouri, F,2008,
Structural Model of Real-Time Databases: An Illustration, 11th IEEE
International Symposium on Object-Oriented Real-Time Distributed
Computing (ISORC 2008)
[22] Minutesmansoftware, GPSS world, North Carolina, U. S. A. 2001(4E).
[GPSS-Book]
[1] Silberschatz, Korth, Sudarshan-2002, Database system concept,4th (I.E),
McGraow-Hill Pub. 698-709,903
[2] Gray. J,978 "Notes on Database Operating Systems", Operating
Systems: An Advanced Course, Lecture notes in Computer Science, 60
[3] Mohan, C, Lindsay B and Obermark R, 1986,Transaction Management
in the R* Distributed Database Management Systems, ACM TODS,
11(4)
[4] Lampson B and Lomet D, 1993, A new Presumes Commit Optimization
for Two phase Commit, Pro.of 19th VLDB Conf
[5] Oszu M, Valduriez P, 1991, Principles of Distributed Database Systems,
Prentice-Hall,1991
[6] Kohler W, 1981,survey of Techniques for Synchronization and
Recovery in Decentralized Computer System, ACM Computing Surveys,
13(2)
[7] Ramamritham,Son S. H, and DiPippo L, 2004, Real-Time Databases and
Data Services, Real-Time Systems J., vol.28,179-216
[8] Robert A and Garcia-Molina H, 1992, Scheduling Real-Time
Transactions, ACM Trans. on Database Systems, 17(3)
[9] Davidson S., Lee I and Wolfe V.,1989, A protocol for Times Atomic
Commitment, Proc. of 9th Intl. Conf. On Distributed Computing System
[10] Levy E., Korth H and Silberschatz A.1991, An optimistic commit
protocol for distributed transaction management, Pro.of ACM SIGMOD
Conf.
[11] Haritsa J., Carey M, Livney M,-92, Data Access Scheduling in Firm
Real time Database Systems, Real Time systems J, 4(3)
[12] Han Q, 2003, Addressing timeliness /accuracy/ cost tradeoffs in
information collection for dynamic environments, IEEE Real-Time
System Symposium,Cancun, Mexico
[13] Haritsa J., Ramesh G. Kriti.R, S. Seshadri, 1996, "Commit processing in
Distributed Real-Time Database Systems", Tech. Report-TR-96-01, Pro.
Pro. Of 17th IEEE Real-Time Systems Symposium, USA
[14] Haritsa J., Carey M and Livney M, 1990,"Dynamic Real-Time
Optimistic Concurrency Control", Proc. of 11th IEEE Real-Time
Systems Symp.
[15] Haritsa J. 1991, "Transaction Scheduling in Firm Real-Time Database
Systems", Ph.D. Thesis, Computer Science Dept. Univ. of Wisconsin,
Madison
[16] Xiong M. and Ramamritham K., 2004, Deriving Deadlines and Periods
for Real-Time Update Transactions, IEEE Trans. on Computers, vol.
53,(5)
[17] Kang W, Son, S., Stankovic J, and Amirijoo M, 2007, I/O Aware
Deadline Miss Ratio Management in Real-Time Embedded Databases,
IEEE RTSS
[18] Gustavsson S and Andler S, 2005, Decentralized and continuous
consistency management in distributed real-time databases with multiple
writers of replicated data, Workshop on parallel and distributed realtime
systems, Denver, CO
[19] Xiong M, Han S., and Lam K, 2005, A Deferrable Scheduling for Real-
Time Transactions Maintaining Data Freshness, IEEE Real-Time
Systems Symposium, FL
[20] Jan Lindstrom, 2006, "Relaxed Correctness for Firm Real-Time
Databases," rtcsa,pp.82-86, 12th IEEE International Conference on
Embedded and Real-Time Computing Systems and Applications
(RTCSA'06)
[21] Idoudi, N. Duvallet, C. Sadeg, B. Bouaziz, R. Gargouri, F,2008,
Structural Model of Real-Time Databases: An Illustration, 11th IEEE
International Symposium on Object-Oriented Real-Time Distributed
Computing (ISORC 2008)
[22] Minutesmansoftware, GPSS world, North Carolina, U. S. A. 2001(4E).
[GPSS-Book]
@article{"International Journal of Information, Control and Computer Sciences:51577", author = "Y. Jayanta Singh and Suresh C. Mehrotra", title = "An Analysis of Real-Time Distributed System under Different Priority Policies", abstract = "A real time distributed computing has
heterogeneously networked computers to solve a single problem. So
coordination of activities among computers is a complex task and
deadlines make more complex. The performances depend on many
factors such as traffic workloads, database system architecture,
underlying processors, disks speeds, etc. Simulation study have been
performed to analyze the performance under different transaction
scheduling: different workloads, arrival rate, priority policies,
altering slack factors and Preemptive Policy. The performance metric
of the experiments is missed percent that is the percentage of
transaction that the system is unable to complete. The throughput of
the system is depends on the arrival rate of transaction. The
performance can be enhanced with altering the slack factor value.
Working on slack value for the transaction can helps to avoid some
of transactions from killing or aborts. Under the Preemptive Policy,
many extra executions of new transactions can be carried out.", keywords = "Real distributed systems, slack factors, transaction
scheduling, priority policies.", volume = "3", number = "8", pages = "1922-6", }
