A NOVEL TRANSFER POLICY MODEL TO ENHANCE THE

SERVICEABILITY OF AN E-BUSINESS

Jason C.T. Lo, Allan K.Y. Wong and Wilfred W.K. Lin

Department of Computing, Hong Kong Polytechnic Univeristy, Hung Hom, Kowloon, Hong Kong S.A.R.

Keywords: SDITPM, primary metrics, secondary metrics, PID, planar integration, vertical integration, pervasive

computing.

Abstract: The novel statistical distribution independent transfer policy model (SDITPM) is proposed to improve the

serviceability of a logical agent server in a pervasive computing environment. Serviceability is defined as

the “chance of obtaining a required service within a defined period”. The SDITPM helps the agent to make

sound migration decision by leveraging different primary metrics from which secondary ones are derived

for the proportional (P), derivative (D), and integral (I) control elements. These elements are timely

combined by planar and vertical integrations to form the final transfer probability that affirms a transfer

policy decision to migrate. Therefore, The SDITPM is basically a PID controller that facilitates the decision

making process.

1 INTRODUCTION

Traditionally migration of logical entities is a way to

improve system dependability (Avizienis, 2004) and

performance. A logical entity such as an application

may migrate for many reasons, for example: a) to

reduce the host’s load and as a result workload is

evened out (i.e. load balancing), and b) to self-

preserve because the host becomes unreliable (e.g.

continued power jitters). In the UNIX perspective

migration of a logical entity is basically process

migration of the following steps: a) the transfer

policy makes the migration decision, b) the location

policy finds the target node, c) the target nodes starts

a new process, d) the old node passes on the

execution states and object code of the suspended

logical entity, and e) the migrant starts running with

the new process in the new node (Coulouris, 2001).

In this era of the Remote Programming Paradigm

(Cockyane, 1997) agent mobility is a necessity.

Agent migration entails all the steps in process

migration but differs by carrying the object class

from which the executable code is generated locally.

This avoids any incompatibility between the pre-

compiled code and the local architecture because the

Internet is naturally heterogeneous. The SDITPM

leverages primary metrics such as the host’s context

switching cycle time, and the length of the agent

server’s queue of requests. The leveraging accuracy

and speed are independent of the distribution of the

parametric values because this is carried out by the

SDITPM’s CA (Convergence Algorithm) component

(Wong, 2001). The overall CA operation is

summarized by the equations (1) and (2).

M is the

distribution mean estimated for the period in which

the F (flush limit) number of data samples are

collected. F=14 is used because it yields the fastest

convergence to

(Wong, 2001). The other

parameters are: a)

1−i

is the feedback of the last

estimated mean to the current estimation cycle, b)

m is the j

sample in the i

estimation cycle,

)1(,3,2,1

−

Fj , and c)

is the first data

sample when CA had first started running.

1);2.(..........);1(..........

≥=

−=

−

∑

imM

160

Lo J., Wong A. and Lin W. (2005).

NOVEL TRANSFER POLICY MODEL TO ENHANCE THE SERVICEABILITY OF AN E-BUSINESS.

In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 162-166

DOI: 10.5220/0001408501620166

 SciTePress

2 THE STATISTICAL

DISTRIBUTION

INDEPENDENT TRANSFER

POLICY MODEL (SDITPM)

The transfer decision making process by the

SDITPM is divided into three main parts: a)

leveraging primary parameters by the PID approach,

b) computing the transfer probability (TP), c)

computing the cost index (CI) and affirming the

migration decision if “

ThresholdTP >

AND

ThresholdCI <<

” is satisfied. It treats a

primary metric (e.g. server’s queue length

Q )

simply as a waveform. It derives from a primary

metric three secondary ones: a) the “current sampled

mean (of the waveform) over the last one sampled”

ratio for proportional (P) control, b) “current

sampled rate of change” for derivative (D) control,

and c) deviation errors for integral (I) control. The

corresponding

},0{ ∆ objective functions compute

the P and D deviation errors, where “0” and

∆

symbolically mark the reference point and the safety

margin about this point respectively. The deviation

error measures how much a secondary metric has

gone beyond the

∆±

safety band. SDITPM

selectively and timely uses the P, I, and D controls

to compute the overall transfer probability so that

the transfer policy can affirm a migration decision.

Every primary metric is uniquely identified by l ,

sll ,...2,1= .Similarly every secondary metric (SM)

is identified by s,

sks ,...2,1=

. For

example,

2,3,

SMSM

= indicates the 2

secondary metric derived from the 3

primary

metric. If the primary metric is the agent server’s

queue length

Q , the current rate of Q changes (i.e.

) can be its 2

secondary metric. P and D

controls together forms the 2-dimensional control

plane shown in Figure 1. The 3

dimension is the

perpendicularly “incidental” I control. The plane has

four control regions: “Migrate”, “Alarm 1 (or A1)”,

“Alarm 2 (or A2)”, and “Inert”. The

∆

safety/tolerance margin on each side (i.e.

∆

in Figure 1) of the “0” reference controls the

region’s effect on the SDITPM operation. The

physical meaning of

},0{ ∆ for the

secondary metric above (i.e.

2,3

SM ) is

ref

∆

, where

ref

"0"

and

∆

=∆

for

the “D or derivative” control. If the D control in the

i cycle (i.e.

D ) is beyond the

∆± region, the

deviation error

(e.g. for

2,3

SM ) assumes

either the “

)(

2,3 DDi

refD ∆+−=

” or the

“

)(

2,3 DDi

refD

∆

−

” value.

∆

and

∆

mark the D and P thresholds (i.e. “Threshold1” and

Threshold2”) respectively as shown in Figure 1. The

generic

dl ,

representation is

dl ,

, with

i cycle

implied. The combined effect by P, D and I elements

for region

is the region’s quantified transfer

probability or

TP . The overall system transfer

probability is

TP , which assumes the

TP value

of the current region of operation. Meanwhile the

TP values of the dormant regions are reset to zero.

The threshold for migration decision in the

“Migrate” or

C region is

Th (Table 1), which is

applicable irrespective to the number of primary

metrics being leveraged. Different suffices have

specific meanings, for example,

for D control

at the

migration decision making cycle by the

transfer policy with the

l primary metric.

Figure 1. High-level view of the SDITPM model

(single primary parameter)

A NOVEL TRANSFER POLICY MODEL TO ENHANCE THE SERVICEABILITY OF AN E-BUSINESS

161

Table 1. Transfer decision matrix for Figure 1

(single control plane,

1=l )

C1 (P control,

positive)

C2(P control,

negative)

C3(D control,

positive)

(

Th ,

and

are

thresholds set

for 3 different

regions)

Migrate for sure

for a single

primary metric;

is set to

infinity;

CCr

ThTP >>

always holds

(PID control

region)

Alarm 1

(A1): for

ThTP <

else migrate

(region

C ,

D+I control

only)

C4 (D control,

negative)

Alarm 2 (A2): for

ThTP <

, else

migrate

(region

C , P+1

control only)

Don’t care

or Inert state

(no action

region

C )

3 EXPERIMENTS

The simulation experiments to verify the SDITPM

are carried out in the Java Aglets mobile agent

environment. This platform is chosen because: a) it

is stable, b) it has rich user experience, c) it supports

agent mobility and d) it is designed for the Internet

and this makes the experimental results scalable to

the real Internet environment. The domain for the

simulated PCI is a part of the PolyU Intranet

annexed by the PI technique (Wong, 2000). Within

the PI the agents migrate freely, and the driver(s),

the agent(s), the CA entity, and the Monitor (Figure

2) are all aglets (agile applets). The driver and the

agent server interact in a client/server relationship.

From the TOW (table of waveforms in Figure 2) the

driver(s) picks a waveform or trace, which embeds

an unknown pattern, to simulate a primary metric. In

Figure 2 two primary metrics are leveraged. The

migration behavior of the agent is recorded in a real-

time fashion by the Visual visualization tool (Wong,

2000). The CA exists as an API so that an agent can

invoke it for computing any waveform means

quickly and accurately, for example, the mean

queuing time

Queuing

Mean . These mean values by

the CA, which is invoked by an agent, are the

“interior” ones in the SDITPM context.

The Monitor that gathers the PI/PCI domain

statistics also invokes its own CA to calculate

different mean values on the fly. In contrast, these

are the “exterior mean values”.

Figure 2. Setup for the SDITPM experiments

The interior and exterior mean values contribute

to the

Threshold computation for evaluating the

“

ThresholdTP > AND

ThresholdCI << ” condition for a possible

transfer policy migration. Many experiments were

conducted with the Java SDITPM prototype

leveraging different simulated primary metrics. The

preliminary results indicate that the SDITPM is

indeed responsive for W&W applications. Figure 3

shows the changes of the three primary metrics

being leverage by SDITPM in the experiment:

context switching (CS) cycle time, queuing time

(Queuing), and agent’s service time (CPU). These

metrics represent a stack of three (

3=N ) control

planes and therefore incidental integration is

required for the

computation. Figure 4 shows

the regional changes in SDIPM over time.

In this particular experiment one threshold is

assumed for all the control regions for simplicity as

shown in Figure 4. The rectangular pulse in Figure 4

is not a part of the SDITPM behaviour but explains

what happens with respect to time. At the rising

edge “a” SDITPM makes the decision to migrate

and the agent server moves to another PCI/PI node.

This decision is based on the transfer probability

TP of region R1 or

for PID control;

TP exceeds the given threshold. The agent

migrates at the rising edges “b”, “c”, and “d”. The

contributing factor for the subsequent migrations is

also

. It shows inside the rectangular pulse

width how the dominance of one control region is

taken over by another. If the agent had not migrated,

it would have seen these changes. For example,

inside the pulse width between “a” and “b” rising

edges the

TP and

TP transfer probability

distributions for the R1 and R2 (

C for “D+I”

ICETE 2005 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

162

control) regions respectively overlap in the period

“from 17 to 25”. The dominance of

TP for PID

control wanes as time progresses, and at the time

point “26” it is taken over by region R3 or

C that

administers “P+I” control only.

Figure 3. Changes of three primary metrics over

time

Figure 5 shows the distribution of the agent’s

migration times from one node to another captured

by Monitor. In fact, this is the migration cost

distribution (MCD) for the “whole life” of the PCI

for the duration of the experiment. The CA indicates

the following:

Life

MCD

Mean is 214 .69 ms, the mode is

140 ms (typical migration time), and standard

deviation is 139.5ms. The migration decisions for

the rising edges a, b, c and d in Figure 4 is triggered

by the “true state” of: “

ThresholdTP >

AND

ThresholdCI <<

”, where

CI in this

case was pre-planned to take the

Life

MCD

Mean

(i.e.

214 .69 ms) for testing and demonstration purposes.

Figure 4. Changes in SDITPM’s four control regions

over time with respect to Figure 3

Figure 5. Migration time distribution captured by the

Monitor

4 CONCLUSION

The novel statistical distribution independent

transfer policy model (SDITPM) is proposed in this

paper to improve the serviceability of a logical

server agent in a pervasive W&W environment.

Serviceability is the “chance of obtaining a required

service within a defined period”. The preliminary

experimental results indicate that the SDITPM can

indeed helps agents to make responsive transfer

decisions to migrate by leveraging different primary

metrics. As a result the agent server’s serviceability

is improved through mobility. From the leveraged

primary metrics the secondary ones are derived for

A NOVEL TRANSFER POLICY MODEL TO ENHANCE THE SERVICEABILITY OF AN E-BUSINESS

163

the proportional (P), derivative (D), and integral (I)

control elements. These elements are timely

combined by planar and vertical integrations to form

the final transfer probability that effectively affirms

a migration decision. The SDITPM is basically a

PID controller that facilitates the transfer policy

decision making process of an agent server in a

pervasive W&W environment.

ACKNOWLEDGEMENT

The authors thank the Hong Kong PolyU for funding

the research with HJZ91 and “01901531R” support.

REFERENCES

A. Avizienis, J.-C. Laprie, B. Randell and C. Landwehr,

Basic Concepts and Taxonomy of Dependable and

Secure Computing, IEEE Transactions on Dependable

and Secure Computing, 1(1), January-March 2004, 11-

G. Coulouris et al, Distributed Systems – Concepts and

Design, 3

Edition, Pearson, 2001

W.T. Cockyane and M. Zyda, Mobile Agents, Manning,

1997

Allan K.Y. Wong, Wilfred W.K. Lin and Tharam S.

Dillon, Local Compilation: A Novel Paradigm for

Multilanguage-Based and Reliable Distributed

Computing over the Internet, Special Issue: Mobile &

Wireless Communications & Information Processing,

in the Journal of Simulation, 75(1), July 2000

Allan K.Y. Wong and Joseph H.C. Wong, A Convergence

Algorithm for Enhancing the Performance of

Distributed Applications Running on Sizeable

Networks, The International Journal of Computer

Systems, Science & Engineering, vol. 16, no. 4, July

2001, 229-236

ICETE 2005 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

164