94 research outputs found
An Efficient Concurrency Control Technique for Mobile Database Environment
Day by day wireless networking technology and mobile computing devices are becoming more popular for their mobility as well as great functionality Now it is an extremely growing demand to process mobile transactions in mobile databases that allow mobile users to access and operate data anytime and anywhere irrespective of their physical positions Information is shared among multiple clients and can be modified by each client independently However for the assurance of timely access and correct results in concurrent mobile transactions concurrency control techniques CCT happen to be very difficult Due to the properties of Mobile databases e g inadequate bandwidth small processing capability unreliable communication mobility etc existing mobile database CCTs cannot employ effectively With the client-server model applying common classic pessimistic techniques of concurrency control like 2PL in mobile database leads to long duration Blocking and increasing waiting time of transactions Because of high rate of aborting transactions optimistic techniques aren t appropriate in mobile database as well This paper discusses the issues that need to be addressed when designing a CCT technique for Mobile databases analyses the existing scheme of CCT and justify their performance limitations A modified optimistic concurrency control scheme is proposed which is based on the number of data items cached amount of execution time and current load of the database server Experimental results show performance benefits such as increase in average response time and decrease in waiting time of the transaction
A Concurrency Control Method Based on Commitment Ordering in Mobile Databases
Disconnection of mobile clients from server, in an unclear time and for an
unknown duration, due to mobility of mobile clients, is the most important
challenges for concurrency control in mobile database with client-server model.
Applying pessimistic common classic methods of concurrency control (like 2pl)
in mobile database leads to long duration blocking and increasing waiting time
of transactions. Because of high rate of aborting transactions, optimistic
methods aren`t appropriate in mobile database. In this article, OPCOT
concurrency control algorithm is introduced based on optimistic concurrency
control method. Reducing communications between mobile client and server,
decreasing blocking rate and deadlock of transactions, and increasing
concurrency degree are the most important motivation of using optimistic method
as the basis method of OPCOT algorithm. To reduce abortion rate of
transactions, in execution time of transactions` operators a timestamp is
assigned to them. In other to checking commitment ordering property of
scheduler, the assigned timestamp is used in server on time of commitment. In
this article, serializability of OPCOT algorithm scheduler has been proved by
using serializability graph. Results of evaluating simulation show that OPCOT
algorithm decreases abortion rate and waiting time of transactions in compare
to 2pl and optimistic algorithms.Comment: 15 pages, 13 figures, Journal: International Journal of Database
Management Systems (IJDMS
An Efficient Concurrency Control Technique for Mobile Database Environment
Day by day, wireless networking technology and mobile computing devices are becoming more popular for their mobility as well as great functionality. Now it is an extremely growing demand to process mobile transactions in mobile databases that allow mobile users to access and operate data anytime and anywhere, irrespective of their physical positions. Information is shared among multiple clients and can be modified by each client independently. However, for the assurance of timely access and correct results in concurrent mobile transactions, concurrency control techniques (CCT) happen to be very difficult. Due to the properties of Mobile databases e.g. inadequate bandwidth, small processing capability, unreliable communication, mobility etc. existing mobile database CCTs cannot employ effectively. With the client-server model, applying common classic pessimistic techniques of concurrency control (like 2PL) in mobile database leads to long duration Blocking and increasing waiting time of transactions. Because of high rate of aborting transactions, optimistic techniques aren`t appropriate in mobile database as well. This paper discusses the issues that need to be addressed when designing a CCT technique for Mobile databases, analyses the existing scheme of CCT and justify their performance limitations. A modified optimistic concurrency control scheme is proposed which is based on the number of data items cached, amount of execution time and current load of the database server. Experimental results show performance benefits, such as increase in average response time and decrease in waiting time of the transactions
Towards context-aware ubiquitous transaction processing: a model and algorithm
Transaction management for mobile and ubiquitous computing aims at providing mobile users with reliable services in a transparent way anytime anywhere. To make such a vision a reality, transaction processing for the mobile and ubiquitous computing needs to adapt to the runtime environments dynamically. However, most existing mobile transaction models do not consider the context-based transaction management. In this paper, we propose a context-aware transaction model and context-driven coordination algorithms. They are built on an event-context-action mechanism, enabling the transaction processing to adapt well to dynamically changing transaction context. The simulation results have also demonstrated that our model and algorithms can significantly improve the successful commit ratio under unstable context conditions. © 2011 IEEE.published_or_final_versionThe 2011 IEEE International Conference on Communications (ICC), Kyoto, Japan, 5-9 June 2011. In IEEE International Conference on Communications, 2011, p. 1-
Uniparallel Execution and its Uses.
We introduce uniparallelism: a new style of execution that allows
multithreaded applications to benefit from the simplicity of
uniprocessor execution while scaling performance with increasing
processors.
A uniparallel execution consists of a thread-parallel execution, where
each thread runs on its own processor, and an epoch-parallel
execution, where multiple time intervals (epochs) of the program run
concurrently. The epoch-parallel execution runs all threads of a
given epoch on a single processor; this enables the use of techniques
that are effective on a uniprocessor. To scale performance with
increasing cores, a thread-parallel execution runs ahead of the
epoch-parallel execution and generates speculative checkpoints from
which to start future epochs. If these checkpoints match the program
state produced by the epoch-parallel execution at the end of each
epoch, the speculation is committed and output externalized; if they
mismatch, recovery can be safely initiated as no speculative state has
been externalized.
We use uniparallelism to build two novel systems: DoublePlay and
Frost. DoublePlay benefits from the efficiency of logging the
epoch-parallel execution (as threads in an epoch are constrained to a
single processor, only infrequent thread context-switches need to be
logged to recreate the order of shared-memory accesses), allowing it
to outperform all prior systems that guarantee deterministic replay on
commodity multiprocessors.
While traditional methods detect data races by analyzing the events
executed by a program, Frost introduces a new, substantially faster
method called outcome-based race detection to detect the effects of a
data race by comparing the program state of replicas for divergences.
Unlike DoublePlay, which runs a single epoch-parallel execution of the
program, Frost runs multiple epoch-parallel replicas with
complementary schedules, which are a set of thread schedules crafted
to ensure that replicas diverge only if a data race occurs and to make
it very likely that harmful data races cause divergences. Frost
detects divergences by comparing the outputs and memory states of
replicas at the end of each epoch. Upon detecting a divergence, Frost
analyzes the replica outcomes to diagnose the data race bug and
selects an appropriate recovery strategy that masks the failure.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89677/1/kaushikv_1.pd
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