chapter 2 proposed framework with concurrency...

CHAPTER 2

PROPOSED FRAMEWORK WITH CONCURRENCY

CONTROL AND CACHE MANAGEMENT TECHNIQUES

Transactions run in the MH can access and manipulate public data

items. The data items in the database are accessed through a DS which is

connected to a static PDN. However, direct data access from the DS increases

response time and server load. Caching is a process of keeping copies of

remote data items in a permanent storage to improve data availability, access

time and response time. Caching strategy reduces network overhead and load

on DS.

When caches are loaded with duplicated data items that are fetched

from the DS, the cache management strategy should ensure co-ordination of

access to the cached data, preservation of cache consistency and effective

replacement of the cached data. The cache consistency preservation technique

should ensure that the cached copy of the data item is consistent with the

original copy in the database. To avoid access to stale data, IR is generated by

the DS, when the original copy of the data item is updated. IR contains the

identities of the data items that are updated.

Due to constrained memory size, the cached data should be

efficiently replaced to make room for new data items that are to be cached.

The cache replacement policy should be efficient enough to improve cache hit

ratio. When the cached data item is shared between number of concurrent

transactions from different MHs, the concurrent access is made to the cached

data item. Concurrent access to the shared data item must be properly

controlled to preserve data integrity.

The thesis proposes an efficient framework that serializes the

transactions to ensure concurrency control and effectively manages the cache

to facilitate concurrent transaction execution in mobile wireless environment.

2.1 PROPOSED FRAMEWORK FOR CONCURRENT

TRANSACTION EXECUTION

The proposed framework improves concurrent transaction execution

with cache management strategies to support cache consistency preservation

and effective cache replacement when compared with existing AVI approach.

It also supports recovery of failed or interrupted transactions.

2.1.1 Structure of the Framework

The proposed framework is shown in Figure 2.1. In the proposed

framework for concurrent transaction management, transactions are run in

MHs. Transactions that access the shared data item are also called as affiliated

transactions.

Figure 2.1 Framework for Concurrent Transaction Execution

RDR or UDR / Reply

Recovery Status

Log Entry

MOBILE HOSTBASE STATION

MANAGER

RECOVERY

MANAGER

LOCAL LOG

LOG MANAGER

TRANSACTION

BASE STATION CONTROLLER

PERMANENT LOG

Cache is stored in the BS of the mobile data network. In all existing

caching strategies, the cache is loaded in the MH, which allows the affiliated

mobile transactions to manipulate the shared data item independently. It may

lead to increased transaction ‘restart’ rate when the shared data item is

updated. In the proposed caching strategy, independent execution in the MHs

is co-ordinated and ‘restart’ rate is reduced. Cache Manager (CM), a software

upgrade, is loaded in the BS to co-ordinate access to the shared data item. It

also manages cache with consistency preservation scheme and replacement

policy. CM ensures that the data access requests on cached data items are

serviced with improved response time. When the original data item in the DS

is updated by any one of the simultaneous transactions, the replicated cached

data items should be properly invalidated to ensure data integrity. The CM

module invalidates the cached data items based on the proposed PLP method,

in which the life time of the data item is predicted based on the previous life

time and update rate. The CM facilitates effective replacement of the cached

data items based on the degree of data sharing to improve cache hit ratio.

The framework supports recovery of affiliated transactions that have

failed or interrupted. To support recovery, the fixed BS has a Recovery

Manager (RM) module. A Log Manager (LM) module is loaded in the MH

itself to create, maintain and manipulate the log to support RM on recovery of

failed or interrupted transactions. The log is stored in the MH itself. The last

saved log entry which may be RDR, RDR_REPLY, UDR or UDR_REPLY is

sent to the RM on failure recovery. The RM sends the recovery status back to

the MH. The BSC is chosen to be the permanent storage area for log storage,

when the log size exceeds the storage capacity of the MH.

2.1.2 Caching Strategy

The cache is loaded in the BS against caches in the MHs. The CM

module manages cache access and operations on cached data. The CM fetches

data item from the DS and stores it in the cache. All subsequent access

requests for the cached data item will be serviced by the CM itself.

2.1.3 Cache Structure

The CM stores the public data items fetched from the DS in the

cache along with additional control information. Control information of the

data item supports cache management, concurrency control and failure

recovery. The structure of the data object in the cache is represented as < Did,

Vid, PLP, ATC, UC, LUT, LUTS, DTL> where Did is the identity of the data

item, Vid is the value of the data item, PLP is Predicted Life Period of the data

item, ATC is the Affiliated Transaction Count, UC is a flag that confirms the

previous update made, LUT is the transaction that updated the data item

lastly, LUTS is the logical timestamp that is associated with last update

operation and DTL is a Dependent Transaction List.

Every data item in the database is uniquely identified with an

identity and a value is assigned to the data item. PLP of the data item is an

interval, within which its value is assumed to be valid. It is calculated based

on previous update interval and update rate. When more number of

transactions share the data item, they are said to be affiliated transactions and

the number of affiliated transactions that access the data item is identified

with ATC. LUTS indicates the logical timestamp assigned when the data item

is updated lastly. The flag UC is set, when the update operation is confirmed

by the DS. While ATC indicates the number of affiliated transactions, DTL

keeps track of the dependent transactions’ identities. Dependent transactions

of transaction T are those which have read the data item that has been updated

by the transaction T but not yet committed in the DS. Initially when the CM

retrieves the data item from the DS, it sets LUTS to the logical timestamp at

which data is retrieved and ATC of the data item to zero; when subsequent

data access requests are made, ATC is incremented. The data in the cache is

valid as long as it is not updated in the DS.

2.1.4 Algorithms for Cache Data Management

The control information for each data item is preserved in the cache

along with its identity and value to support concurrency control, cache

consistency preservation and cache replacement. The following algorithms

explain how the control information is affected by transaction execution and

its support for concurrency control and cache management.

a. Setting value of Predicted Life Period

PLP of a data item is initially set to optimal life time based on the

nature of the data item. If the shared data item is the available reservation slot

for automobile service in the weekend, PLP is assumed to be few minutes. If

it is available slot during working days, PLP may be set to several minutes

assuming contention for the reservation slot on a holiday is more when

compared to the reservation slot in a working day. It is highly probable that

the shared data item with high contention is updated quickly leading to

reduced valid life time of the data item.

The strategy for manipulating the PLP is given below

Step 1: PLP (Did) = opt_Value

Step 2: if (PLP (Did) expires without any updates)

PLP (Did) = PLP (Did) /2.

if (Total life time elapsed since last update > opt_Value)

PLP (Did) = Total life time elapsed since last update/2+PLP (Did)

else if (UDR (Ti, Did, Vid) and ATC (Did) = =1)

PLP (Did) = update interval (Did)

else if (UDR (Ti, Did, Vid) and ATC (Did) > 1)

PLP (Did) = update interval (Did) -

( update rate*update interval(Did))

If the PLP of the data item expires without any update by the

affiliated transactions, the previous predicted life period is halved and set to

the current PLP of the data item. To avoid PLP exceeding optimal value, it is

reset to a value less than the optimal value. If the total life time elapsed since

last update is more than estimated optimal life time, predicted life time is reset

to a value less than the optimal value. If data item is updated even before the

expiration of the predicted life time, the update rate of the data item is

considered and the life time is reduced in proportion to the update rate. Since

the life time of the data item is predicted based on the previous actual life

time and update rate, it is highly probable that the PLP of the data item equals

the actual life time.

b. Setting Affiliated Transaction Count

Initially when data item is loaded into the cache, the ATC is set to

zero. When a transaction makes read access to the data item, ATC is

incremented once to indicate the count of transactions that access the shared

data item.

Step 1: ATC=0

Step 2: if RDR (Ti, Did)

ATC (Did) = ATC (Did) + 1

c. Setting Dependent Transaction List

Initially DTL is set to NULL. When affiliated transactions access

the shared data item, the dependency between the transactions is identified

and DTL is updated to reflect the status of dependency at any time instance.

The process of manipulating DTL is given below.

Step 1: if (RDR (Ti, Did) and LTS (RDR (Ti, Did) > LUTS (Did)

and UC(Did) = =0)

DTL (LUT) = DTL (LUT) U Ti

Step 2: if (UC (Did) ==1) DTL (Did) =NULL

When a transaction issues RDR (Ti, Did), it becomes dependent

transaction of another transaction Tj, if the data item in the cache is updated

earlier by Tj and not yet committed in the DS. DTL is reset to NULL when

UC flag of the data item is set to indicate update confirmation in the DS.

d. Setting Logical Timestamp Value

Timestamp of any operation is the time instance at which the

operation is performed. In the proposed framework, timestamp ordering is

used to preserve the ordering between simultaneous conflicting access

requests. The major issue in timestamp ordering is the synchronization of the

physical clocks of each MH with that of the CM. So logical timestamp

ordering is used to logically represent the timestamp of transaction operations

rather than physical timestamps. The absence of global physical time makes it

difficult to find out the global timestamp.

Cristian and Berkeley (2001) suggested synchronization algorithms

based on multicasting physical timestamps from a server which is based on

round trip time. It may be suitable for intranet which has limited geographical

coverage. Mills (2001) proposed an algorithm based on Network Time

Protocol (NTP) service. The network of servers located across the internet

might help in synchronization of the physical clocks. The NTP servers

broadcast the timestamp so that physical clocks of clients may be

synchronized with that of the server. Since it was difficult for synchronizing

physical clock, Lamport has defined ‘happened before relation’ to have

logical ordering (numerical ordering) of transactions. Lamport’s logical clock

is a monotonically increasing software counter, whose value need not be

physical clock dependent. The timestamp of an event e at process Pi is

denoted by Li (e). To capture ‘happened before relation’ denoted as ,

processes update their logical timestamps and transmit the logical timestamps

in messages as follows:

LTS 1: Li is incremented before each event is performed at process Pi.

LTS 2: i) When a process Pi sends a message m, it piggybacks the timestamp

value t=Li on m.

ii) On receiving (m, t), a process pj computes Lj= max

(Lj, t) and applies LTS, before timestamping the event receive (m).

Lamport’s logical ordering fails, when two independent events

occur in two different transactions. The ordering between the independent

events may not be properly identified. To overcome that, vector clocks are

proposed by Mattern and Fidge which enable each transaction to hold a vector

that depicts the ordering of the independent events in a set of transactions. It

has large message and storage payload that is proportional to the number of

transactions.

The proposed framework has the CM (coordinator) module present

in the BS. The CM attaches timestamps with the access requests (RDR or

UDR), based on Lamport’s logical ordering. Lamport’s timestamp itself will

properly order the transactions based on their logical timestamps, as the CM

coordinates the access requests.

2.2 LITERATURE SURVEY

The following section explores various concurrency control

techniques to preserve transaction serializability, consistency preservation

techniques to preserve integrity of the shared data item and replacement

policies to effectively replace the cached data item as found in the literature.

2.2.1 Concurrency Control Techniques

Locking techniques lock the shared data item when simultaneous

access is made to them. A binary-lock is held on the data item when the data

is accessed; the scheme is too restrictive for database items, which reduces

transaction throughput. A Shared-lock allows a transaction to apply read-lock

on the data item, while other transactions are allowed to read the data item

simultaneously. A write-lock is exclusively held on the data item preventing

concurrent transactions from accessing the data item. Two Phase locking

(Easwaran et al, 1978) requires clients to communicate continuously with the

DS to obtain locks and detect the conflicts resulting in increased network

load. The altruistic locking protocol (Salem et al, 1994) supports

synchronization of long lived transactions but suffers from cascading aborts.

Speculative locking proposed by Krishna and Masaru (2004) improves the

performance by trading extra resources. Locking techniques limit the degree

of concurrency and lead to deadlocks when conflicting ordering of locking is

done on the shared data items.

In multi version concurrency control proposed by Philip and Nathan

(1983), each data object is allowed to have multiple versions. However, if the

transaction which has created a version aborts, the transaction that has read

the version must also be aborted.

In timestamp ordering technique, each transaction has an ordered

list of actions. Before the transaction performs its action, it is marked with a

timestamp. Based on the timestamp, the transactions are serialized. Real time

distributed optimistic concurrency control protocol with dynamic adjustment

of serialization order was proposed by Kwok et al (2000) and it avoids

unnecessary transaction restarts. An optimistic concurrency control with

dynamic timestamp adjustment protocol proposed by Ho-Jin and Byeong

(2006) requires client side validation.

Salman (2008) proposed concurrency control without locking by

introducing Absolute Validity Interval (AVI). AVI based approach helps in

reducing bandwidth and managing disconnections. AVI is calculated based on

the previous update interval, within which data item is assumed to be valid.

AVI based approach does not consider the dynamicity of the life time of the

data item.

From the literature survey, it is understood that the timestamp based

concurrency control can provide controlled concurrency with improved

transaction throughput. But timestamp ordering is a challenging issue in

mobile wireless environment as synchronizing physical clocks of participating

MHs is difficult to achieve.

2.2.2 Cache Consistency Preservation Techniques

In the literature, three types of cache consistency maintenance

algorithms for wireless mobile computing environment namely stateless,

stateful and hybrid are proposed. In the stateless approach (Barbara and

Imielinski, 1994) the DS is unaware of MHs’ cache content. The MH needs to

check the validity of the cached data items before using them. Even though

stateless approaches employ simple database management at the DS, their

scalability and ability to support disconnectedness of the MH are poor. Jing et

al (1997) proposed Bit Sequences method which increased the size of IR. On

the other hand, stateful approaches proposed by Anurag et al (2001) are

scalable, but have significant overhead due to server database management. In

the hybrid approach (Wang et al, 2004) the server keeps very limited

information about clients’ cache status. It achieves good tradeoff between

scalability and efficiency, as computation overhead involved is less for

database management.

The stateless approach assumes no knowledge of the MHs’ cache

contents. The server simply sends IRs to all MHs periodically. At the MH, a

data object request cannot be serviced until the next IR is received. The

stateless server broadcasts the IR according to different strategies. Stateless

approach is classified into asynchronous and synchronous methods. In

asynchronous method the IR is broadcast immediately, after the data item is

updated. In synchronous method, IR is broadcast periodically. Asynchronous

methods do not provide any latency guarantees for the querying MH; but in

synchronous method, there is a guaranteed latency due to the periodic nature

of synchronous broadcast. Three stateless synchronous methods namely

Broadcasting Timestamps, Amnesic Terminals and Signatures for cache

invalidation were proposed by Barbara and Imielinksi (1994). Two methods

adaptive IR with fixed window and adaptive IR with adjustable window were

proposed by Joe Chun-Hung et al (2000). They avoid bandwidth wastage of

the downlink broadcast channels as in Bit Sequences approach and uplink

bandwidth wastage as in Timestamp approach. They dynamically adjust the

IR size.

In the stateful approach, Mobile Support Station (MSS) maintains

object state for each MH's cache and broadcasts IR that contains only those

cached objects. Anurag et al (2001) proposed an asynchronous stateful

algorithm which uses asynchronous IR to maintain cache consistency. In

Asynchronous Stateful algorithm, each MH maintains its own Home Location

Cache (HLC) to deal with the problem of disconnections. When a MH queries

for a data item, it checks the validity of the data item in its local cache. If the

data item is valid, it satisfies the query from its local cache; otherwise, an

uplink request is made to the server by the MSS on behalf of the MH. When

the data item is received, the MSS adds an entry to the HLC for the requested

data item and forwards the data item to the MH. The data item may not be

cached at the MSS.

Wang et al (2004) proposed a hybrid consistency maintenance

scheme, called Scalable Asynchronous Cache Consistency Scheme (SACCS)

to maintain MH's cache consistency. Unlike the stateful algorithm which

requires the MSS to remember all data objects for each and every MH's cache,

SACCS requires the MSS to identify which data objects in its database might

be valid in MH's caches. This makes the database management of the MSS

much simpler. On the other hand, unlike existing synchronous stateless

approaches, SACCS does not periodically broadcast IR reducing the

frequency of IR through the downlink broadcast channel.

Yuen et al (2000) proposed a cache invalidation scheme, called

Invalidation by Absolute Validity Interval (IAVI). An AVI for each data item

is defined based on its real-time property. A MH can verify the validity of a

cached item by comparing the last update time and its AVI. A cached item is

invalidated if the current time is greater than the last update time by its AVI.

With this self invalidation mechanism, the IAVI scheme uses the IR to inform

the MHs about the change in AVI rather than the update event on the data

item. The AVI of a data item can be derived based on the previous update

interval of the data item. It is assumed that each update is associated with a

timestamp. On every update of the data item, the update timestamp will be

recorded with the updated data item. The timestamp together with the AVI of

the data item can be used to determine its validity. The update interval can be

used to define the validity period of the cached item.

From the literature survey of cache consistency preservation

schemes, it is learnt that in all existing schemes, the MH is assumed to have

cache loaded in it. The frequency, with which IR should be generated and sent

to the MH, differs based on different schemes. The major constraint in the

existing cache invalidation schemes is to deal with disconnected MH, when

IR is sent.

2.2.3 Cache Replacement Policies

Aggarwal et al (1999) proposed three categories of cache

replacement strategies for web caching namely direct extensions of traditional

strategies, key based replacement strategies and function based strategies.

Direct extensions of traditional strategies such as Least Recently Used (LRU),

Least Frequently Used (LFU) and First In First Out (FIFO) do not consider

size of the objects to be cached. Key based policies prioritize cache

replacement factors based on the requirements of the application. The factor

with highest priority is considered as a primary replacement criterion. If two

data objects have same value for the primary replacement factor, the selection

for replacement is based on a secondary key factor. Function based policies

employ a general function of different factors such as transfer time, object

expiration time and entry time in the cache. A pyramidal selection scheme is

also proposed where the objects are categorized based on their size. The

cached data items are replaced based on the least recent usage.

Yin et al (2003) presented a generalized target driven cache

replacement policy for mobile environment under strong consistency model.

They formulated functions for specific targets of minimized query delay and

downlink traffic. Narottam et al (2006) proposed a scheme based on data

profit. It ensured that the data items which are highly associated with each

other are retained in the cache. Jin-Woo et al (2006) designed a cache

replacement policy for ad-hoc environment in which small objects are loaded

in the cache and larger objects are loaded in idle peer nodes. Energy efficient

co-ordinated cache replacement scheme was proposed by Edward Chan et al

(2009) for adhoc networks that reduces energy consumption and access

latency. Hazen et al (2009) proposed a cache replacement policy for location

dependent information services using virtual table method.

All the previous approaches deal with storing cache in the MH and

none of them deal with shared caches in which the degree of data sharing

needs to be considered as an important factor.

2.3 PROPOSED PLP SUPPORTED LOGICAL TIMESTAMP

ORDERING BASED CONCURRENCY CONTROL

TECHNIQUE

When affiliated transactions make simultaneous data access

requests, the CM controls access to the shared data item without reducing the

transaction throughput. Any MH that needs to read the data item makes RDR.

When the RDR is received, the CM attaches the timestamp to the request

based on its logical clock value. If the data item is cached, the CM checks

whether the RDR is made within the valid life time of the data item; if so, the

read access is permitted on the data item. When a MH issues UDR indicating

data update, the CM timestamps the UDR with its logical clock. Since update

request can be issued only after read request, the data item which is to be

updated is present in the cache. The CM updates the data item locally.

When simultaneous access requests are made, CM resolves the

access requests using FIFO principle, based on the logical timestamp assigned

to the requests. If two transactions Ti and Tj send UDR on the same shared

data item, the transaction requests are attached with the logical timestamps Li

and Lj respectively based on their arrival at the CM. The Transaction Ti is

allowed to continue if Li is less than Lj; Else Tj is allowed to continue. The

proposed PLP based concurrency control algorithm is as follows:

Step 1: While (Simultaneous RDR (Ti, Did) requests are received)

if (all RDRs received before PLP (Did) expires)

Allow all Ti to continue with read access

Step 2: Serialize simultaneous RDR/UDRs based on logical

timestamp assigned to them.

if (UDR (Ti, Did, Vid, TSr) and (TSr <LUTS))

Send ‘REDO UPDATE’ with piggybacked updated value.

else Locally update the data item.

If multiple RDRs are received, CM allows all transactions to read

the value, if the read requests are made within valid life time of the data item.

Within PLP interval, the data item is assumed to be valid and all the MHs that

have requested read access to it are allowed to access the data item, increasing

the degree of concurrency. If two transactions Ti and Tj send UDR on the

same shared data item, the transaction requests are attached with the logical

time stamps LTSi and LTSj respectively based on their arrival at the CM. The

Transaction Ti is allowed to continue if LTSi is less than LTSj; Else Tj is

allowed to continue. When a transaction is allowed to update the data item,

the value already read by the transaction is checked for its validity by

comparing the logical timestamp of the read operation with the logical

timestamp of last update operation on the data item. If the already read value

is updated after it has been read, the transaction is intimated to resend update

request with the updated data value. Otherwise, the data item is updated

locally and update request is forwarded to the DS while UC flag is set to zero.

The time interval between two consecutive updates is said to be the

valid life span of the data item and in the proposed PLP method, it is

predicted based on previous update interval and update rate. Within PLP

interval, the data item is assumed to be valid and all the MHs that have

requested read access to the shared data item are allowed to access the data

item, increasing the degree of concurrency. The proposed PLP supported

concurrency control technique serializes the conflicting operations of the

concurrent transactions efficiently based on their logical timestamp. It also

ensures the immediate data availability, after the data is updated in the cache.

2.4 PROPOSED PLP BASED CACHE CONSISTENCY

PRESERVATION TECHNIQUE

When concurrent transactions are executed, the data item in the DS

may be updated. When such update is made to the data in the DS, the CM

should invalidate the replicated copy of the data item in the cache. When one

of the concurrent transactions, serviced by the same CM, issues UDR the

replicated copy in the cache is updated locally. The CM generates the invalid

indication to indicate the update of the data item and broadcasts the invalid

indication to all dependent transactions serviced by the same CM, only if the

ATC ratio is high (>0.6). ATC ratio is the ratio of the affiliated transactions to

the total transactions. High ATC ratio indicates that almost all transactions are

affiliated transactions. The broadcast of invalid indication avoids transaction

processing with invalid stale data. Invalid indication has the updated value

piggybacked on it, to allow transactions to continue with execution.

The CM sends the invalid indication to all connected BSs that have

caches stored in it. When the connected BSs that have CMs receive the

invalid indication, they update the data item locally with UC of the data item

set to zero, if the data item is available. Otherwise, the data item is loaded in

the cache with UC flag set to zero. Invalid indication sent to the connected

CMs, makes provision for the availability of data item in the nearby BSs. It

supports continual transaction execution even on handoff procedure.

The threaded CM also forwards the update request made by the MH

to the DS. The DS after validation does the update and broadcasts the

invalidation confirmation together with updated value. On receiving the

invalidation confirmation, the CM sets the UC flag to indicate the confirmed

update in the DS.

The updated cached copy of the data item is immediately available

in the cache with a change in PLP. This ensures cache consistency with

control of concurrency.

The PLP of the data item is calculated as shown in equation (2.1).

PLP(Did) = UI(Did) - (U(Did) * UI(Did)) (2.1)

where UI is the actual update interval of the data item and U is the update rate

on the data item. Update rate is the ratio of the number of transactions that

have updated the data item to the total number of affiliated transactions during

previous PLP duration and is calculated as shown in equation (2.2).

U(Did) = Total_Updates (Did) /ATC (Did) (2.2)

Since the PLP is based on the update rate, it is highly probable that

the PLP is very close to the actual valid life span of the data item. If the PLP

is optimally estimated, the number of transactions that need to be restarted

will be considerably reduced. Since the predicted life period is based on

update rate, the value of PLP is dynamically changed reflecting the data

update environment. If the data item is not updated, it need not be invalidated

unnecessarily with limited PLP. This is ensured with a change in PLP, when

PLP expires without any update to the data item. Conversely if the frequency

of data item is very high the PLP is reduced to ensure frequent invalidation.

The framework proposes immediate dissemination of invalid

indication to all BSs connected to the current BS. Since cache is maintained

in the fixed host, the complexity in the MH is reduced against the existing

techniques in which MHs store the data item locally. The proposed cache

consistency preservation technique also deals with the MH that has missed IR.

The situation is handled as a failure of the MH and recovery algorithm

ensures consistency preservation.

2.5 PROPOSED ATC/DTL BASED CACHE REPLACEMENT

POLICY

A cache replacement strategy decides which data item is to be

evicted from the cache when no space is available to store additional data

objects. The cache replacement may be based on several factors such as

frequency of access, data retrieval cost from the server and its size. The

effectiveness of a replacement strategy is measured with the cache hit ratio.

In the proposed approach, the data items are organized in the cache

in the order, in which they are loaded into the cache to avoid complexity

increase due to reordering of data items on every loading of data item.

Whenever a read access is made to the data item, ATC is incremented; it

indicates the degree of sharing of the data item. Analysing the previous

approaches, none of them considers the number of transactions that share the

data item. If more number of transactions share the same data item, it is

highly desirable to make the data item available in the cache. Because, if the

highly shared data item is evicted from the cache, it is highly probable that the

cache hit ratio of the affiliated transactions becomes zero. Since the proposed

framework favours the affiliated transactions, proposed cache replacement

policy replaces the data object with the least ATC.

The proposed cache replacement policy, improves data availability

in the cache when the data item is shared between affiliated transactions.

When more than one data item has the same ATC value, it is resolved based

on recent usage of the data item and the count of dependent transactions on

the data item. The recent usage identifies the data item which is accessed in

the recent past. The DTL gives the count of the dependent transactions on the

data item. In the proposed replacement policy, highly shared recent data items

are retained in the cache. The algorithm for cache replacement policy is as

follows:

While (cache is loaded to its maximum capacity) and (new item to be cached)

Repeat for every data item in the cache from the remembered location

if ( ATC(Did) is the least of all data items )

if (Did is not accessed in the recent past and

count of DTL (LUT(Did) is the least)

Replace Did with the new item to be cached

Remember the next location for initiating next search

When the cached data items are to be replaced for the first time, the

data item for replacement is searched from the first location which is the

remembered location. The remembered location helps in identifying the data

item that is not used in the recent past. When a data item is replaced the

location of the data item is marked as remembered location, so that search for

next replacement will start from that location. If the data item is not shared

between affiliated transactions and if it is not recently accessed, it is replaced.

The next immediate cache location is remembered to make sure that the

search for next replacement starts with the remembered location. Since the

data items are loaded in the cache in the same order they are referenced, all

data items are checked for possibility of replacement without omission.

2.6 FUNCTIONALITY OF THE PROPOSED FRAMEWORK

The proposed framework supports concurrent transaction execution

in mobile wireless environment. The CM co-ordinates the access to the shared

data item while preserving the consistency of the cached data item. It ensures

efficient cache replacement policy to favour concurrent affiliated transaction

execution. The functionalities and interactions of the MH, CM and DS to

achieve concurrent transaction execution environment are discussed below.

2.6.1 Functionality of the Mobile Host

The MH executes the transaction. The MH gets connected to the DS

that provides data services. The DS authenticates the valid MHs. When a data

item is needed, the MH generates RDR and sends it to the CM. The MH sends

UDR to the CM when data item is to be updated. The MH makes entry in the

log for all communication done between the MH and the CM/RM/DS along

with the logical timestamp for the operations. The flow of operations

performed by the MH is as follows:

Step 1: Connect to the DS and get authenticated

Step 2: if (Did is needed)

Make log entry for RDR (Ti, Did) and Send RDR to CM

Step 3: if (Did needs to be updated)

Make log entry for UDR (Ti,Did,Vid) and Send UDR to CM

Step 4: if (Requested data item is retrieved from the CM)

Make log entry for the received data item with the logical

timestamp set by the CM

Step 5: if (Update confirmation is received from the CM)

Make entry in the log with the timestamp

Step 6: if (Invalid indication is received)

Continue the operation with the received updated

data item.

Step 7: if (Commit (Ti))

Make log entry and commit Ti.

The functionality of the MH is shown in the Figure 2.2.

Figure 2.2 Functionality of the MH

2.6.2 Functionality of the Database Server

The functionality of the DS is shown in Figure 2.3. The data items

stored in the database can be accessed through a DS. When the DS receives a

connection request, it authenticates valid users. When RDR for a data item is

received, the DS broadcasts the value of the data item. If UDR is forwarded

from the CM, the DS updates the data item in the database and sends the

Connect to Server

Get Authenticated

DataAccess?

Make entry in log and

send RDR/ UDR to CMData

Update?

Reply for

read/updatefrom CM?

Make entry in log with

timestamp received from

Invalid

indication?

Continue operation with

the updated value

Commit?Make entry in log ,

commit and terminate

Terminate

update confirmation message along with the updated value of the data item.

The operations of a DS are as follows:

Step1: While (waiting for connections)

if (connection request) authenticate the valid users

Step2: if (RDR (Ti, Did) from CM)

Send the requested data item to the CM

Step 3:if (UDR(Ti, Did, Vid))

Update the data item in the DS and broadcast update

confirmation with updated value to all CMs

Figure 2.3 Functionality of the DS

YesRead Access

Waiting for Connection

Connection

Request?

Connect Authorized

Clients

Update ?

Send Requested

Data item

Update data & Send

Invalidation Confirmation

with updated value

2.6.3 Functionality of the Cache Manager

The CM when loaded with data items initializes the control

information. When RDR is received from the MH, it is attached with the

logical timestamp of the CM. The CM ensures concurrency control with

logical timestamp ordering of the requests. The RDR is satisfied if the data

item is already in the cache. Otherwise data item is requested from the DS.

The retrieved data item is loaded in the cache. If cache replacement is needed,

the CM replaces a data item with the least ATC and DTL. If UDR is received,

the data item is updated locally and invalid indication is sent to all connected

CMs and dependent MHs. This ensures availability of the data item in the

nearby CMs to support mobility of the MHs. The threaded CM forwards the

requests to the DS and waits for update confirmation. On update confirmation

from the server, the CM sets UC flag and forwards the confirmation message

to the MH.

The functionality of the CM is as follows:

Step 1: Load the CM with the initial data (Did, Vid)

Step 2: For every data item Did loaded in the cache

UC=1; ATC (Did) =0; LUT=NULL; DTL=NULL;

LUTS=0; PLP (Did) =optimal value;

Step 3: While (waiting for data access requests from MH)

if (RDR (Ti, Did))

Timestamp RDR with logical timestamp

if (cache has Did and PLP(Did) is not expired)

Allow transaction to continue with read

access to Did

Forward the RDR to the DS

if (Did is retrieved from DS) and (cache is not

loaded to maximum capacity)

Load Did in the cache and initialize the control

information of the data item.

Send Did to the MH along with logical timestamp.

while (Data item not found for replacement)

Search from remembered location for Did with

the least ATC and DTL

Remember the location for next replacement

if (UDR(Ti, Did, Vid, TSr) and (TSr <LUTS))

Send ‘redo update’ to the MH with the current data

Locally update Did and forward UDR to the DS.

Send invalid indication to all BSs.

Set LUT (Did) =Ti; LUTS (Did) = LTS (UDR);

PLP (Did) = UI - U *UI; UC=0;

if (ATCRatio (Did) is high)

Broadcast invalid indication to all MHs.

if (Invalid indication for a data item from other CMs)

if (data item in already cached)

Update the value of data item and its PLP

Load the data item in the cache

if (Update Confirmation from the DS)

Set UC=1 to indicate confirmed update

Send the update confirmation to the MH.

Update

request?

Request?

cache?

Fetch data from DS

Update Data item

locally & set UC to 0

Update ATC &

Send data to MH

Send invalid indication to all

connected CMs & Forward

UDR to DS

Update

Confirmation?

Waiting for Client Requests

Replace

Update UC

Replace data item with

least ATC and DTL

Send the data item to

the MH with timestamp

The functionality of the CM is shown in Figure 2.4.

Figure 2.4 Functionality of the CM

2.7 PERFORMANCE ANALYSIS

The proposed framework is compared with the existing concurrency

control techniques, cache consistency preservation techniques and cache

replacement policies. The merits of the proposed scheme are analysed.

2.7.1 Comparison of Concurrency Control Techniques

The proposed concurrency control technique is compared with

existing techniques for various parameters and discussed below.

i) Data Accessibility: When transactions are concurrently

executed, data accessibility determines how immediately data

is accessible when it is made available. Since locking

technique locks the data item, the accessibility of data is

limited. Timestamp ordering allows strict serialized data

access but with improved accessibility. AVI and proposed

method make data immediately available. The access to the

data is not blocked in the AVI and proposed PLP method.

ii) Serializability: Serializability of the concurrent transactions

decides ordering of the concurrent transactions such that

conflicting operations are performed without conflicts.

Timestamp ordering strictly preserves the serializability, while

the locking techniques may lead to deadlocks. AVI preserves

serializabilty, but at extra cost of restarting the conflicting

transactions. Proposed method uses logical timestamp ordering

based PLP method to ensure strict serializability.

iii) Transaction Throughput: Locking technique drastically

reduces the degree of concurrency while timestamp ordering

technique improves it. AVI based approach and the proposed

technique have improved support to concurrent transaction

execution. The proposed technique ensures immediate data

availability after the data is updated in the cache when

compared to the AVI based approach.

iv) Probability for Transaction ‘restarts’: The transactions are

aborted and restarted when conflicting operations are not

serialized in concurrent transactions. Though locking

technique handles conflicting operations, deadlocks due to

locking may lead to transaction restarts. AVI based method

leads to transaction abortion as the concurrent transactions

may access stale data. But it is avoided at the cost of placing a

co-ordinator at the BS. The proposed method reduces the

transaction ‘restarts’, as the CM serializes the conflicting

operations with co-ordinated access to the shared data item.

2.7.2 Comparison of Cache Consistency Preservation Protocols

The existing cache consistency protocols are compared with the

proposed PLP based invalidation scheme against various parameters and the

comparison is shown in Table 2.1.

Table 2.1 Comparison of Cache Consistency Preservation Techniques

Parameters

Stateless

Method

Stateful

Method

Hybrid

Methods

Proposed

Cache Location MH MH MH BS

IR Method Synchronous Asynchronous Asynchronous Asynchronous

IR Generation DS DS MSS CM

IR Size Database sizeInvalidated

Invalidated

In the stateless approaches, the IR is broadcast by the DS

periodically. IAVI, SACCS and proposed method generate IR only when

cached data items are invalidated. In the proposed technique, the invalid

indication is broadcast only when ATC ratio is high. So, considerable

reduction in the down link capacity is achieved. In the proposed technique,

the invalid indications are communicated between BSs in the wired network

to ensure effective utilization of wireless channels. The IR size of the stateless

approaches is proportional to the database size. In IAVI, SACCS and

proposed method the IR size is proportional to the invalidated cached data

items. The proposed method reduces the complexity in the MH and the DS.

2.8 ANALYSIS OF PERFORMANCE METRICS

The performance of the proposed concurrency control and cache

consistency preservation technique is analyzed based on various metrics. The

following section shows the merits of the proposed framework against

existing techniques.

2.8.1 Simulation Model

The functionality of the CM is implemented in Java and MHs in

J2ME. The DS is loaded with 200 data items of equal size. As the shared data

item is assumed to be available number of slots, the data item size is

considered to be equal. Access time of the data items of equal size remains

equal. The data access pattern is not uniformly distributed as concurrent

execution environment is considered. The data item together with its control

information occupy maximum of 128 bytes. The simulation is done for

varying cache size (10 % and 30 % of database size) and varying ATC ratio.

2.8.2 Impact of Update rate on PLP

The proposed scheme calculates the PLP of the data item based on

the previous update interval and the update rate. The PLP of the data item for

various update intervals and update rates are tabulated in Table 2.2.

Table 2.2 Impact of Update Rate on PLP

Predicted life period in sUpdate Interval

in s U = 0.2 U=0.4 U=0.6 U=0.8

5 4 3 2 1

4 3.2 2.4 1.6 0.8

3 2.4 1.8 1.2 0.6

2 1.6 1.2 0.8 0.4

1 0.8 0.6 0.4 0.2

From the Table 2.2, it is understood that the PLP of the data item is

reduced in proportion to the update rate of the data item. When 80% of the

affiliated transactions involved in data update, the PLP is reduced to 20% of

the previous update interval. Since the increase in update rate reduces the PLP

of the data item, it is highly probable that the PLP equals the actual valid life

time of the data item.

2.8.3 Impact of PLP on Usage of invalid data

In the AVI approach, the predicted validity period of the data item is

equal to the previous update interval. The SACCS method predicts Time To

Live (TTL) value based on previous update interval and life time. AVI/TTL

values are predicted based only on the update interval irrespective of the

update rate. But the proposed PLP based scheme calculates the PLP, based on

the update rate. The PLP, AVI and TTL values for various update intervals

and update rates are shown in Table 2.3. For TTL calculation the previous life

time of the data item is assumed to be 2 seconds. When the MH accesses the

data item with AVI or TTL, it assumes the validity of the data item until AVI

/ TTL expires. If the update rate increases, the original copy of the data item

is updated even before AVI / TTL expires, while the MH continues execution

with stale data.

Table 2.3 Comparison of PLP and AVI/TTL

PLP in s AVI in s TTL in sUpdate

Interval

in sU=0.4 U=0.6 U=0.8 U=0.4 U=0.6 U=0.8 U=0.4 U=0.6 U=0.8

5 3 2 1 5 5 5 3.5 3.5 3.5

4 2.4 1.6 0.8 4 4 4 3 3 3

3 1.8 1.2 0.6 3 3 3 2.5 2.5 2.5

2 1.2 0.8 0.4 2 2 2 2 2 2

1 0.6 0.4 0.2 1 1 1 1.5 1.5 1.5

The maximum deviation of the predicted life span of the data item

for the proposed PLP based technique and the existing AVI (Salman, 2008) /

TTL (Wang, 2004) based techniques is shown in Table 2.4. The PLP and AVI

/ TTL values calculated for the update interval of five as in Table 2.3, are

taken and compared with the actual time intervals. The difference between the

actual validity and the predicted validity of the data item is tabulated in Table

2.4 for the proposed and existing techniques.

Table 2.4 Accuracy of PLP and AVI/TTL Calculation

Maximum Deviation in PLP/AVI/TTL calculation (sec)

U=0.8 , PLP = 1, AVI=5 and

TTL=3.5 for UI=5

U=0.6 , PLP = 2 AVI=5 and

TTL=3.5 for UI=5

Actual

in secPLP AVI TTL PLP AVI TTL

5 0 0 0 0 0 0

4 0 1 0 0 1 0

3 0 2 0.5 0 2 0.5

2 0 3 1.5 0 3 1.5

1 0 4 2.5 1 4 2.5

If the update rate increases, the actual update interval decreases.

When the update rate changes, the AVI / TTL approaches have the predicted

duration deviated from the actual valid period in which stale data items are

processed. In AVI/TTL approach the predicted validity lifetime deviates

whenever the update rate is changed. The PLP approach ensures no deviation

when update rate is uniform and update rate is not increased more than half of

its previous update rate. Anyhow, deviation is limited to the maximum

duration of 1second when compared to 4 seconds of AVI and 2.5 seconds of

TTL based approach.

2.8.4 Analysis of Stale Hit Probability

Stale Hit Probability is the probability with which the MH accesses

the invalid (stale) data from the cache. The cache data becomes invalid when

the original data is updated in the DS. Since cache is maintained in the MH,

the AVI and SACCS approach assume validity of the data till AVI/TTL

expires. In SACCS approach, the stale hit probability is proportional to the IR

miss probability. The stale hit probability reaches 0.51 when IR miss

probability increases to 0.8 irrespective of the update rate. However the stale

hit probability is reduced to 0.04 at the cost of multiple IR broadcast. But in

the proposed PLP method the stale hit probability is reduced to zero as the

updated data is made immediately available in the cache. In AVI based

approach, stale hit probability is not reported.

2.8.5 Impact of Validity Period on Transaction ‘restarts’

Transactions need to restart when they process with invalid data. In

SACCS approach, when MH is awake the data item is assumed to be valid

before TTL expiration. The probability of transaction ‘restarts’ due to stale

data access, is directly proportional to stale hit probability. For AVI based

approach, the transaction is committed locally and conflict resolution is

postponed till conflicts are identified in the co-ordinator. The co-ordinator

restarts all the transactions that processed with stale data. But in the proposed

approach no invalid data is processed and the conflicts are resolved even

before the transactions reach the commit point.

The proposed PLP based technique reduces the transaction ‘restarts’

to 0 when PLP exactly matches the actual life time. The existing techniques

abort all the transactions except the one that commits first which is

proportional to O(n-1) where n is the number of simultaneous affiliated

transactions.

2.8.6 Analysis of Response Time

Response time refers to the time taken to service the request made

from the MH. In Table 2.5, the response time analysis between direct DS

access and cache access is given.

Table 2.5 Analysis of Response Time

Response Time in msTransaction

Count Direct DS

Access

Proposed Cache

Existing Cache

1 77.8 65.4 63

5 78.6 65.4 63

10 81.9 70 63

15 83.4 73.8 63

20 84.5 76.5 63

25 86.2 78 63

30 87.4 78 63

35 88.2 78 63

Figure 2.5 Analysis of Response Time

From Figure 2.5 it is understood that, as the number of mobile

transactions increases, the response time increases for both direct DS access

and the proposed cache access for data and control frames. Data frames have

data packets and frame control has control information about the packet. The

response time increases, due to the network overhead. When the cache is

loaded in MH, the response time is constant for accessing any cached data

item. When transaction count is less than 5, the response time equals at least

the round trip time which is comparatively more than 63 ms. When

transaction count is increased, the affiliated transaction ratio is increased

resulting in maximum response time of 78.4 ms.

The existing techniques ensure fixed and reduced response time.

Though the proposed technique has increased response time, the reduction in

probability of transaction ‘restarts’ and stale hit probability to zero is

appreciable when compared to the existing techniques which have caches

loaded in the MH itself.

2.8.7 Impact of ATC Ratio on Cache Hit Ratio

Cache hit ratio is defined as the ratio of the number of available

searched data items in the cache to the total number of searched data items.

The impact of ATC ratio on cache hit for varying transaction count (TC) is

tabulated in Table 2.6. ATC ratio is the ratio of the number of affiliated

transactions to the total number of transactions. Figure 2.6 indicates that the

increase in ATC ratio, improves the cache hit ratio.

Table 2.6 Impact of ATC Ratio on Cache Hit Ratio

Cache Hit RatioATC

Ratio TC=5 TC=10 TC=15 TC=30

0.2 0 0.13 0.16 0.17

0.4 0.2 0.33 0.36 0.36

0.6 0.4 0.53 0.56 0.57

0.8 0.6 0.73 0.76 0.8

1 0.8 0.93 0.96 0.97

Figure 2.6 Impact of ATC Ratio on Cache Hit Ratio

2.8.8 Comparison of Cache Hit Ratio

The proposed ATC/ DTL based cache replacement policy is

compared against existing FIFO, LRU and LFU policies. Since the proposed

policy assumes data items of equal size, the proposed system is compared

only against traditional approaches in which data size is equal. The cache hit

ratio is compared between the proposed ATC/DTL policy and existing

policies for different ATC ratio and varying cache size. The comparison is

shown in Table 2.7.

Table 2.7 Analysis of Cache Hit Ratio

Cache Hit Ratio

Cache Size = 10% DB Size Cache Size = 30% DB SizeATC Ratio

FIFO LRU LFUATC/

DTLFIFO LRU LFU

0.2 0 0 0 0.2 0 0 0 0.2

0.4 0 0 0 0.4 0 0 0 0.4

0.6 0 0 0.1 0.6 0 0 0.33 0.66

0.8 0.16 0.16 0.15 0.7 0.2 0.2 0.6 0.8

1.0 0.2 0.2 0.2 0.74 0.3 0.3 0.6 0.84

Figure 2.7 Analysis of Cache Hit Ratio

From Figure 2.7, it is understood that the cache hit ratio of the

proposed ATC/DTL method increases as ATC ratio increases irrespective of

the cache size. It assures maximum of 0.84 cache hit ratio against 0.6 of LFU

policy and 0.3 of LRU and FIFO policies. The cache hit ratio of the proposed

policy is improved by at least 20% when compared to the existing cache

replacement techniques.

Based on the proposed cache consistency preservation technique, a

paper entitled “Performance Analysis of Improved Cache Invalidation

Scheme in Mobile Computing Environment” is published in the International

Journal of Computer Science and Network Security, Vol. 9, No.9, September

2009, pp.153-160.

Based on the proposed caching mechanism, a paper titled

“Improved Caching strategy for Concurrent Transaction Execution in Mobile

Wireless Environment” is published in European Journal of Scientific

research, Vol. 51, Issue 2, 2011, pp. 182-192.

Based on the cache management operations proposed in the thesis, a

paper titled “Cache Management for concurrent Transaction execution in

Mobile Wireless Environment” is published in the Journal of Computer

Science, Vol.7, Issue 3, 2011, pp. 374-378.

Based on the framework proposed in the thesis, a paper entitled

“Framework for Transaction Management in Mobile Computing

Environment” is published in the International Journal of Computer Network

and Internet Research, Vol. 9, Issue 2, December 2009, pp. 19-24.

2.9 CONCLUSION

The proposed PLP based concurrency control technique assures

efficient concurrency control in a highly dynamic transaction execution

environment in which the update rate is uniform and high (>0.8). When 80%

of the affiliated transactions involve in data update, the PLP is reduced to

20% of the previous update interval ensuring prediction of the PLP that equals

the actual valid life time of the data item. The PLP approach ensures no

deviation when update rate is uniform and update rate is not increased more

than half of its previous update rate. Anyhow, deviation is limited to the

maximum duration of 1second when compared to 4seconds of AVI and 2.5

seconds of TTL based approach.

The proposed PLP method reduces the stale hit probability to zero

against 0.51 of SACCS approach, as the updated data is made immediately

available in the cache. The proposed PLP based technique reduces the

transaction ‘restarts’ to 0 when PLP exactly matches the actual life time. The

existing techniques abort all the transactions except the one that commits first.

The cache replacement policy assures efficient replacement that

preserves highly shared data item in the cache resulting in cache hit ratio

greater than 0.74. The cache hit ratio of the proposed policy is improved by at

least 20% when compared to the existing cache replacement techniques. It

also proves that the replacement policy is highly suitable for concurrent

transaction execution environment.

chapter 2 proposed framework with concurrency...

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