Artificial Intelligence for Mobile Learning Interactive System

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International Journal of Engineering and Management Research Vol. 1, Issue-1, 2011

ISSN No.:

Pages: 17-24

www.ijemr.net

Scope and Analysis of Distributed Real Time Replicated Database Models

& their Performance

Sanjay Kumar Tiwari

1

Research Scholar, Madan Mohan Malviya Engineering College, Gorakhpur, Uttar Pradesh, INDIA.

2

Associate Professor, Madan Mohan Malviya Engineering College, Gorakhpur, Uttar Pradesh, INDIA.

3

Research Scholar, Madan Mohan Malviya Engineering College, Gorakhpur, Uttar Pradesh, INDIA.

[email protected], [email protected], [email protected]

ABSTRACT

The Criteria for transaction consistency adopted for the management persistent replicated objects in a distributed system define the degree of concurrency allowed among operations accessing objects. Several notions of consistency are known from the literature among them are causal consistency causal serializability and serializability. After surveying and analyzing the available database models for replicated database, we propose a new transactional model for Hybrid transaction model to specialize the generalizing algorithm for concurrency control in a transaction system that exhibits a clean separation between policy and mechanism. A consistency criterion selected is manifested as a set of rules forming the policy. The mechanism however remains unchanged regardless of the currently used policy. It implements causally consistent message delivery and uses tokens and quorums of tokens to enforce access operation ordering according to the specified consistency criterion. An example of an application exploiting the advantages of dynamically switching among various consistency criteria concludes.

Keywords— Replicated database, Data Access, Static and Dynamic Replication, Transaction, Hybrid Transaction, Lock Conflict, Distributed Real Time Databases.

I.

INTRODUCTION

Inside the growing world of distributed databases, data replication acting an increasingly important role. Replication intends to increase data availability in the presence of site or communication failures and to decrease retrieval costs by local access if possible. The maintenance of replicated data is therefore closely related to inter site communication and replication management can have significant impact on the overall system performance. Replication management in distributed database systems

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namely the data distribution sub models, and examines the effects which different modeling alternatives have on the computational model complexity. In [9], this is extended to analyze the accuracy and complexity of analytical availability estimations. Hence, we compose an analytical modeling approach, called 2RC (2-dimensional replication model with integrated communication), which focuses on the increasingly close interplay between replication and communication. 2RC represents a balanced model of both the database and the communication part. We use this example to show how to derive meaningful performance values (such as response time, throughput, network traffic, and scalability) and to illustrate the general applicability and expressiveness of analytical performance models for distributed and replicated databases.

II. ANALYSIS OF PERFORMANCE

AVAILABLE MODELS

The mapping between the literature and some of the classified modeling concepts are as-

2.1 Database Site Models

Simulation and analytical performance studies of distributed databases commonly use queuing systems as the underlying models. For details on queuing theory and Kendall's classifying notation of queuing systems (e.g., M/M/1) [10].These studies model a fully replicated database of m local sites by a M/M/m/FCFS queuing system. This means that transactions which arrive according to a Poisson process are served on a FCFS basis by m servers and require an exponentially distributed service time. The read transactions are processed by the m servers in parallel, while write transactions occupy all m servers during their service time. This model shared read and exclusive write operations. This is modeled as an M/M/m system with preemptive service interruptions, where the interruptions correspond to the service periods of an M/M/1 system which represents the arrival and service of updates. Nelson and Iyer [11] assume no preemptive processing of write operations and compare parallel updating with sequential updating of replicas. Major drawbacks of these early models are that intersite communication is neglected, that all sites share a single queue of incoming transactions, and that the extreme case of full replication is assumed. The studies in [12, 13, 14] use networks of M/M/1 queues so that each local database is modeled as an M/M/1 system. However, this still restricts all transactions to having the same exponentially distributed service times. More generally, [15, 16] model the local databases as M/G/1/FCFS and M/G/1/RR queues, respectively, with generally distributed service times. The studies in [17,18] use networks of M/H2/1 systems with 2-phase hyper exponentially distributed service times to assign different exponentially distributed service times to read-only transactions (queries) and updates. Still, such

models do not allow us to evaluate real-world systems with more than two transaction types.

All queuing models of distributed databases consider unlimited waiting rooms, i.e., there is no restriction on the queue lengths. Additionally, queues representing database nodes are usually defined to serve their jobs on a FCFS basis, except for in [16], where the round-robin queue discipline is used. Common to most models is that transactions are assumed to arrive as a Poisson process, i.e., the inter arrival time is exponentially distributed. The widely accepted (and, thus, usually omitted) justification is that Poisson streams have been found to be a good approximation for the arrival of jobs submitted independently by a large number of users [19]. Transactions that access a small or moderate number of data items are expected to occur more frequently than transactions that reference a large number of data objects. This can be expressed if the number of data objects accessed per transaction is assumed to be geometrically distributed. The service time for a transaction can then be assumed to be exponentially distributed, which is the continuous version of the discrete geometric distribution. The simulation model in [20] does not assume queuing of transactions, but assigns the same constant service time to every transaction. Instead, the read and write operations in each transaction have to queue individually for lock requests in M/M/1 systems.

2.2 Communication Models

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database server, but it could also be used for a distributed database system. A quite detailed model of inter database communication is presented in [28], but has not received much attention in subsequent analytical models of distributed databases. The capability of simulations to evaluate more complex system models has rarely been exploited to capture inter database communication details. The simulation model in [29] contains a message queue and a send queue at each database node. All incoming and outgoing messages join the message queue to receive CPU service. The outgoing messages then enter the send queue to receive network service. Again, both studies are restrictive in the database part of their model, e.g., they assume full replication and uniform data access. The simulation in [30] models the database nodes as a detailed queuing network where messages require CPU service. However, they assume a local-area network where the actual message transmission time is negligible.

2.3 Replication Models

Models which assume partial replication, rather than full replication, either consider the fraction of replicated data (how many objects are replicated?) or the degree of replication (to how many sites are objects replicated?), but not both. This distinction leads to the following classification and the development of a new two-dimensional replication model.

2.3.1 Full Replication - All Items to All Sites

Most performance evaluations assume full replication [21, 31, 32] i.e., all data objects are replicated to all sites so that each site holds a complete copy of the distributed database. This is an extreme case of replication and it has been recognized that, for many applications, neither full nor no replication is the optimal configuration [12, 17].

2.3.2 One Dimensional Partial Replication -

All Items to Some Sites

Several studies model partial replication in the way that each data items is replicated to some of the sites [33,30,34,12,13]. Formally, the degree of replication can be denoted by a parameter r 2 f1; 2; . . . ; n g, describing that each logical data item is represented by r physical copies, where n is the number of sites. A value of r. 1 expresses no replication, r. n means full replication, and if r > 1, every data item is replicated. Consequently, either no or all data items are replicated. It is usually assumed that the replicas are distributed evenly across the sites, but it is still undefined which copies are placed on which site, such that different degrees of quality of a replication schema can be modeled. Data which is updated frequently should not be replicated to avoid update propagation overhead. However, data which is updated rarely, but read frequently, should be replicated to increase local

availability and avoid communication delays. This common situation requires selecting appropriate data items for replication, which cannot be modeled with the all-objects-to-some-sites scheme.

2.3.3 One Dimensional Partial Replication -

Some Items to All Sites

Alternatively, the degree of replication r can be defined as r 2 .0; 1. describing the fraction of logical data items that are fully replicated to all sites. A data item is either fully replicated or not replicated at all. A value of r . 0 expresses no replication, r . 1 means full replication. Alonso et al. [27] modeled a client server information system and assumed that 0 to 20 percent of the server data is cached at each workstation. This is comparable to the some-objects-to-all-sites replication scheme with r 2 .0; 0:2.. Alonso [35] considered the some-objects-to-all-sites model to examine the correctness of a replication protocol based on group communication. The some-objects-to-all-sites scheme is orthogonal to the all-objects-to-some-some-objects-to-all-sites approach in the sense that the degree of replication is defined along the fraction of replicated data items as opposed to the number of copies. For r < 1, the selection of data items to replicate is undefined. This undefined replica selection can be used to model the quality of replication. Full replication of some data items and no replication of others is a choice between two extremes and entails considerable update propagation overhead for the former and a severely reduced availability of the latter group of items. Since this situation is not typical in real-world applications, the some-objects-to-all-sites scheme is again a questionable modeling approach.

2.3.4 Two-Dimensional Partial Replication -

Some Items to Some Sites

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the interval [0; 1] as an overall level of replication. This is achieved through dividing by d _ n, yielding .r1 _ r2.=n. Performance models which assume full replication only consider the point .1; n. as the possible replication schema.

2.4 Data Access Models

A database performance model can either assume uniform data access or define a model of no uniform (or skewed) data access. For mathematical tractability, most studies assume uniformly distributed data access, i.e., each data item is accessed with equal probability [21, 22, 37, 24, 15, 32]. No uniform data access is more realistic, but used in very few studies of distributed database systems [38, 16, 35]. These models of no uniform data access are usually adopted from evaluations of centralized databases. They can be classified to be either hot-spot models or locality models. In hot-spot models, certain data groups (hot spots) are more likely to be accessed than others. In locality models, local data is more likely to be accessed than remote data.

2.4.1 Hot-Spot Models

The classical hot-spot model of no uniform data access for centralized database systems is b-c access [39]. The model of no uniform b-c access describes that b percent of the data requests are made to c percent of the data items. More precisely defines that a fraction c of the data items in the database are called regular granules and a lock request is with probability b for a regular granule. Among regular granules, each granule is accessed with equal probability and the same is assumed for no regular granules. Using the notions of object-intensity and operation-intensity, the simulations in [40] and [38] apply the b-c access model to individual transactions such that b percent of the operations of each transaction are directed to c percent of the data items which are access intensive. They set b to 0.8 and c to 0.01, 0.05, and 0.25 . The simulation in [41] uses 0.8-0.2 access. Gray and Reuter argue that, for many applications, 0.99-0.01 access is a realistic model.

2.4.2 Locality Models

Raghuram et al. [42] use a data access model called b-l access which simply describes that b percent of the data request can be satisfied locally. If a transaction requests a local data object, each local data object is accessed with equal probability and the same holds for remote data objects, respectively. Raghuram et al. [42] use a value of b . 0:8, while [16] assume b . 0:5 to express that 50 percent of the primary copies are accessed locally. The analytical model in [35] assumes that locally cached data is f times more likely to be accessed than other data, where f is set to 5 and the fraction of cached data ranges from 0 to 20 percent.

2.5 Transaction Processing Models

Replica control protocols assumed in performance evaluations of distributed databases include ROWA, primary copy with synchronous and asynchronous update propagation, as well as optimistic and quorum based algorithms [6].Concurrency control protocols (distributed two-phase locking, optimistic methods, etc.) to capture lock conflicts and blocking of transactions are usually only modeled to compare concurrency control algorithms [21, 22, 33, 43, 12, 13, 29, 44]. Such models are of considerable complexity. They typically use simulations and simplified modeling assumptions concerning replication and communication.

2.5.1 Transaction Models

Since read-only transactions (queries) are easier to process for a database system than updates a distinction in performance models is recommended unless a worst case analysis is intended. Several studies model two transaction types [35, 17]. Distributed transaction processing is addressed in [45, 46]. Under varying assumptions regarding the data access pattern, the numbers of data objects, and the number of database sites, these studies calculate the average number of data objects referenced per transaction and the average number of remote sites accessed per transaction as performance measures. However, these models of distributed transactions are not used to compute response times or transaction throughput. Performance studies that consider distributed transaction processing in the response time and throughput analysis are typically simulation studies [47, 30]. Moreover, [12 , 48] consider two classes of transactions: Class 1 transactions submitted at site k only access data locally available at site k. Class 2 transactions access local as well as remote data items using distributed two-phase locking with primary copy or a distributed optimistic protocol.

2.5.2 Lock Conflict Models

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the lock conflict probability. Hence, an iterative calculation is used in [21] and [12]. The lock conflict probability also depends on the data access pattern. Tay et al. prove that, under three approximations (A1), (A2), (A3), the performance of locking under b-c access in a database of size D is the same as that for uniform access in a database of size D 1. .b ÿ c.2=c _ .1 ÿ c.: This finding has been named the database reduction approach in [50] or the effective database size paradigm (EDSP) in [51].

III.

HYBRID TRANSACTION MODEL

Every transaction would declare its input-output set, and the transaction schedulers do the computation comparing the new transaction’s need to all the running transaction. If there was a conflict, initiation of the new transaction would we delay until the conflicting transactions had completed. The problem with static allocation was the difficulty of computing the inputs and outputs of a transaction before its runs. A transaction’s behavior and consequently its data inputs and outputs of a transaction before runs. Transaction’s behavior and consequently its data inputs and outputs, is often dependent on the transaction input message. For Example- since a banking transaction potentially updates any record of the ACCOUNT table, static allocation would probably reserve the entire table. The locking system cannot tell which particular records will be accessed until the transaction runs. In this case static allocation would schedule only one such banking transaction at a time. By 1972 this approach had been abandoned for the more dynamic one of locking at the individual actions of the transactions.

In Dynamic allocation scheme, each transaction is viewed as a sequence of actions rather than as an input – output set. The requests of each action are scheduled on demand as they occur. When an action accesses a particular object, the object is dynamically allocated to the transaction. For Example-when your teller runs a banking transaction, only your bank account record and the other specific record accessed by the transactions are allocated to it. Transactions are different accounts can run concurrently and in isolation. The proposed model is discussed below. A common model of a distributed transaction is that there is one process, called as Master, which is executed at the site where the transaction is submitted, and a set of processes, called Cohorts, which executes on behalf of the transaction at these various sites that are accessed by the transaction. In other words, each transaction has a master process that runs at its site of origination. The master process in turn sets up a collection of cohort’s processes to perform the actual processing involved in running the transaction. When cohort finishes executing its portion of a query, it sends an execution complete message to the master. When the master received such a message from each cohort, it starts its execution process. When a transaction is initiated, the set of files and data items that, it will access are chosen by the source. The

master is then loaded at its originating site and initiates the first phase of the protocol by sending PREPARE (to commit) messages in parallel to all the cohorts. Each cohort that is ready to commit, first force-writes a prepared log record to its local stable storage and then sends a YES vote to the master. At this stage, the cohort has entered a prepared state wherein it cannot unilaterally commit or abort the transaction but has to wait for final decision from the master. On other hand, each cohort that decides to abort force-writes an abort log record and sends a NO vote to the master. Since a NO vote acts like a veto, cohort is permitted unilaterally abort the transaction without waiting for a response from the master. After the master receives the votes from all the cohorts, it initiates the second phase of the protocol. If all the votes are YES, it moves to a committing state by force-writing a commit log record and sending COMMIT messages to all the cohorts. Each cohort after receiving a COMMIT message moves to the committing state, force-writes a commit log record, and sends an acknowledgement (ACK) message to the master. If the master receives even one NO vote, it moves to the aborting state by force writing an abort log record and sends ABORT messages to those cohorts that are in the prepared state. These cohorts, after receiving the ABORT message, move to aborting state, force write an abort log record and send an ACK message to the master. Finally, the master, after receiving acknowledgement from all the prepared cohorts, writes an end log record and then forgets and made free the transaction. The statistics are collected in the Sink [52, 53, 54, 55]. The database is modeled as a collection of DB size pages that are uniformly distributed across all the Num sites. At each site, transactions arrive under Poisson stream with rate Arrival Rate and each transaction has an associated firm deadline. The transactions can be managed in many different ways. In most of the earlier work done simply static or dynamic ways with only database size computing [56, 55]. A new concept is introduced to manage the hybrid transactions management with database size for originating site and remote site rather than database size computing parameters, where the values of the parameters are changes or adjust automatically depending on the requirements during the execution the experiment.

IV.

CONCLUSION

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the hybrid transactions in database size for originating site and remote site rather than database size computing parameters. With this approach, the system gives a significant improvement in performance. This approach will keep tracks of timing of the transactions to help them from aborts. This approach will give advance information about the remaining execution time of the transactions. This will help the system to inject extra time to such transactions with the merit of static as well as dynamic ways with the track and does recording of the status of the status of failing transaction so that we can provide an extra slack time to improve the performance of the system. In all the conditions the arrival rate of transaction plays a major role in reducing number of miss percentage and improved performance.

The aim of this model is to increase system performance by dynamically data placement so as to optimize the message traffic in the network. Research in the recent past has comparatively focused very little on using replication for increasing performance but has instead been directed more at improving system availability through replication. However, with the advent of mobile or nomadic computing, research in replication needs to change direction-- the underlying assumption of high speed networks no longer hold true. Wireless networks not only have lower bandwidth but are also very expensive to use. In such an situation, it is essential that data be distributed intelligently to achieve a good system performance in terms of message costs and turnaround time. Besides, with mobility introduced in the system, earlier static schemes for improving performance (e.g., the File Allocation Problem) are no longer applicable. As a first step, we use the distributed dynamic replication scheme, which uses a finite automaton based technique to learn access patterns and static replication scheme both together as Hybrid Replication Model which will be more efficient for Data Replication. This accrued information is then used to predict future access sequences and dynamically reorder-- replicate or delete copies of data in the network, in anticipation of predicted accesses, to reduce network message costs. Being distributed, each node of the network bases its decision to replicate or delete an existing copy solely on local information.

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