AN ENHANCED SERVICE PROVIDER COMMUNICATION
INTERFACE WITH CLIENT PRIORITIZATION
Case Study on Fast-food Chain Restaurants
Slobodan Lukovic
ALaRI, University of Lugano, Lugano, Switzerland
Nikola Puzovic
Department of Information Engineering, University of Siena, Siena, Italy
Milos Stanisavljevic
Microelectronic Systems Laboratory, EPFL, Lausanne, Switzerland
Keywords:
e-Commerce, client prioritization, mobile devices, workload scheduling.
Abstract:
With the increased dynamics of modern life, the efficiency and reliability of everyday services is emerging to
be a fundamental concern. On the other hand, modern telecommunication technologies, like wireless Internet
access, are penetrating all segments of our life. However, many every day activities and services still do not
fully exploit new technologies. We propose an approach that enables increased deployment of E-commerce
concepts in the fields where their usage was either small or negligible. Moreover, in the scope of the same
concept, we introduce prioritization of clients in services where it was not commonly present to date.
A solution for enhanced communication interface between service provider and customers is developed. As
a case study, the system is designed and optimized for an implementation in a fast-food chain. The proposed
solution is aiming at increasing of quality of service for customers, and at the same time increasing the op-
erational efficiency of the provider. The main idea behind this approach is to enable customers to use their
mobile devices, such as cell phones or PDAs, for browsing offered services or goods, viewing current service
conditions and placing orders. We will detail theoretical concepts underneath and describe the implementation
on both server and client side.
1 INTRODUCTION
Millions of people daily face different kinds of prob-
lems in common situations. These issues range
from unpredictable traffic and parking problems to
big waiting times in restaurants due to inefficient
order/payment service. The rapidly growing urban
population additionally increases pressure on service
providers. We look for a response for these problems
by incorporating e-Commerce concepts into service
provider - client chain.
The basic idea of service order/payment process
automation relies on the rapid increase of number of
portable devices that are able to access Internet, and
on the growth of the popularity of e-payment meth-
ods. It is estimated that inthe next two years all manu-
factured cell-phoneswill be equipped with WiFi mod-
ules which will boost the availability of Internet ser-
vices. Other broadband wireless services like UMTS
and WiMax are becoming more widely available and
affordable and the number of mobile users that will
use these services will increase. Moreover, the num-
ber of online payments is constantly increasing and
this way of payments is expected to be widely ac-
cepted in very near future. Putting all together, inte-
gration of all aforementioned services is logical con-
sequence of technology development and of the evo-
lution of users’ habits.
In this paper we detail, based on case study on
fast food chain, a possible solution for incorpora-
tion and automation of the service scanning, or-
dering, payment and execution that increases effi-
ciency, cutting the costs and bringing many other
benefits for all sides involved in the process. Our
aims are the improving of existing Quality of Ser-
vices (QoS) in fast food restaurants, and at the same
197
Lukovic S., Puzovic N. and Stanisavljevic M. (2008).
AN ENHANCED SERVICE PROVIDER COMMUNICATION INTERFACE WITH CLIENT PRIORITIZATION - Case Study on Fast-food Chain Restaurants.
In Proceedings of the International Conference on e-Business, pages 197-202
DOI: 10.5220/0001912401970202
Copyright
c
SciTePress
time providing greater flexibility for both the ser-
vice provider and consumers. We propose a fully
integrated system that incorporates many different
technological aspects, ranging from Internet brows-
ing and e-payments to service performance evalua-
tion and workflow scheduling. The final result is a
novel communication interface that brings many new
features and benefits to the client, like better service
overview, waiting time prediction (service availabil-
ity overview),prioritizing of clients, multilingual sup-
port, etc. On the other side, it cuts costs for service
providers in terms of staff reducing, better insight in
demand and market overview coupled with market
profiling and targeted marketing. For now no experi-
mental results are provided.
The paper is organized as follows: In Section 2
we summarize current state of technology and differ-
ent types of its usage that are in scope of interest of
this work, in Section 3 we present an overview of the
overall architecture of the system. Algorithms and
techniques used in realization of proposed concepts
are detailed in Section 4. Finally, Section 5 presents
conclusions and future work.
2 STATE OF THE ART
In parallel to development of communication tech-
nologies many different service providers have been
adopting them to facilitate the interaction with clients,
to provide new services or increase operational effi-
ciency. On the other side mobile technologies such
as WiFi, UMTS or WiMax as well as web access
standards and protocols are more and more oriented
toward better support for increased need for mo-
bile Internet availability. In that sense many mo-
bile web standards such as Wireless Markup Lan-
guage (WML), Extended Modeling Language (XML)
or XHTML etc. have been developed. Moreover, the
development of AJAX (AJAX, 2008) has enabled ex-
change of small amounts of data between client and
server, hence increasing the interactivity, speed and
usability without the need to reload the entire contents
of web page. This is especially valuable when de-
vices have scarce computational and communication
capabilities. By having more and more mobile de-
vices online many new concepts got enabled. This es-
pecially concerns social networking (Eagle and Pent-
land, 2005), mobile commerce (Varshney et al., 2000)
or intelligent wireless web (Alesso and Smith, 2001).
The most recent world-wide trend regarding wire-
less Internet access is deploying of WiFi Internet ac-
cess at variety of locations such as airports, hotels,
restaurants and so on (Friedman and Parkes, 2003);
in some cases even free of charge (Smithers, 2007).
At the same time online payment methods are getting
widespread across wide range of activities (Weiner,
2000),(Ghosh and Li, 2007). Coffees and restaurants
have made steps towards exploitation of WiFi at local
service points (Friedman and Parkes, 2003) and some
fast-food chains started using touch-screens deployed
at tables (eTable) for offer browsing and order placing
(VanLeeuwen, 2005).
We propose a novel solution for prioritization of
clients using and enhancing different experiences and
implementing them in fields where until now these
technologies have not been commonly used. In order
to provide better quality of service we model priori-
tized orders execution and service provider capacity
with a well know operating systems task scheduling.
The theoretical concepts adopted for purposes of our
work will be discussed in detail in Section 4.
3 SYSTEM OVERVIEW AND
IMPLEMENTATION
CONCERNS
We envision a system that integrates WLAN access,
priority order scheduling based on demand prediction
and delivery automation that provides user-friendly
interface to the client. For the purpose of the client-
server communication we propose WiFi wireless In-
ternet access technology since it is widely used and
already deployed at many fast food points around the
world. WiFI access represent an optimal compro-
mise between simplicity and efficiency. The cover-
age, throughput and level of security are considerably
greater than in the case of Bluetooth. On the other
side it is very easy to deploy and cheap to use (from
clients’ point of view it is free) in comparison to other
broadband services as UMTS and WiMax. The range
of the WiFi AP also makes it perfectly suitable for use
in a local service point (a restaurant in this case). Nev-
ertheless, the system is conceived in such way that it
can be easily ported to other communication medi-
ums. By using secure communication and already es-
tablished methods of electronic payment, we will also
provide high level of security that is necessary.
Figure 1 shows the overall organization of the sys-
tem. The main components are servers in service
points, central data warehouse and a connection to
e-payment servers. The local servers are responsi-
ble for processing clients’ orders and updating cen-
tral data warehouse. Payments are performed using
secure connection to e-payment servers or by using
in-house e-payment system. In the following subsec-
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198
Internet
e-payment
servers
Central data
warehouse
Local server
DB
Local server
DB
Service Point 1
Service Point n
.
.
.
Figure 1: Proposed implementation of the system.
tions and we will also discuss the system from the
participants’ point of view (both clients’ and service
providers’), we will discuss the technology involved
together with different implementation aspects.
3.1 Client’s View of the System
Client devices are PDAs or smart phones that are ca-
pable of running a web browser. User interface is im-
plemented in HTML, and it is accessible from each
web browser, so there is no need to install a special
purpose application. Figure 2 shows the UML se-
quence diagram of the users’ interaction with the sys-
tem. At the beginning, the client chooses appropri-
ate WiFi Access Point (AP) that will consent him to
access the services provided in the restaurant. Upon
the entry to the network, clients are redirected to the
start page from where they can browse the available
services and current conditions of the system. There
is a possibility to supply the client with additional
information that traditional ordering methods cannot
support (i.e. expected waiting time, multilingual in-
terface etc.). The calculated expected waiting time
coupled with delivery automation brings a possibility
of introducing prioritization in the fast food service.
For more details see Section 4.
From the start page clients will continue to the
menu with products that are available in the restau-
rant. Trough simple web forms clients can make a
choice, and communicate the selection to the server
in the service point when the order is completed. The
system sends back the information about the order
and order number. The payment can be done through
SSL secured connection using credit-card payment,
pay-pal and other methods of e-payment. In this case,
a request for payment is communicated to the pay-
ment gateway (paypal server or bank server in case
of credit card payment) that processes it and gives the
confirmation. Another option is to use vendor pro-
vided vouchers that can be issued in the form of fi-
ServerUser e-payment server
Enter Network
Send initial web page
Send order
Send e-payment request
Payment processed
Order processed
Browse and
select services
Prepare data
Process request
Redirect
Send e-payment credentials
Process
payment
Confirm
order
Figure 2: Interaction between the client and the system.
delity card, and can be used exclusively for payments
inside the system, and they function in form of deposit
or credit.
Once the payment is completed and the delivery is
ready, it is placed in a delivery slot and the client re-
ceives a code associated with the order. The client
types the code using keyboard on the delivery slot
that is labeled with appropriate order number or with
clients name. These slots can be implemented as
parts of rotating table, where each slot has a protec-
tive cover. Once the correct code is inserted, the ser-
vice is considered to be completed. The time between
order placement and code insertion is considered as
service time’. This information is taken and used as
correction factor for statistical processing and for cal-
culation of the expected time of servicing.
3.2 Service Provider’s View of the
System
The system in a service point processes e-Commerce
orders in parallel with traditional ones (Figure 3). Re-
quests from both sides go to the same server and their
execution is scheduled in order of submission. The lo-
cal server that processes orders is responsible for han-
dling client requests and for scheduling the delivery of
orders. Scheduling and delivery are performed taking
into account also the priority of the clients. The al-
gorithm for calculating waiting times is applied each
time a new client enters the restaurant and this infor-
mation is communicated to the client.
The technology used in the service point is rather
simple and cheap, and it involves a server computer,
wireless AP and Internet connection that will be used
for communication with the central server. The algo-
rithms that are used are described in subsequent sec-
tions.
AN ENHANCED SERVICE PROVIDER COMMUNICATION INTERFACE WITH CLIENT PRIORITIZATION - Case
Study on Fast-food Chain Restaurants
199
Figure 3: Overview of the service points’ organization.
4 SERVICE DEMAND AND
SERVICE TIME PREDICTION
MODEL
In this section we present mathematical concepts pro-
posed for calculation of expected average service time
considering prioritized clients. The main contribution
reflects in the exploration of scheduling mechanisms
in order to meet the deadlines - guarantee service. We
briefly expose possible service classification concepts
and compare them. The chosen concept is described
in detail.
4.1 Definition of the Server and Client
We consider the server - service execution unit that
takes the order, executes it and performs the deliv-
ery of the requested service or product. The server
is characterized as multiple parallel process execution
unit with a given total capacity - service power. The
clients could be clasified in three groups with two lev-
els of priority:
1. Traditional clients served at the service line in
a traditional way (worker at the counter table is
serving one client after another from the FIFO -
first in, first out, queue), taking low priority (pri-
ority equal 0).
2. Regular e-Commerce clients, performing orders
through wireless system, taking low priority.
3. Prioritized e-Commerce clients, performing prior-
ity service orders through wireless system, taking
high priority (priority equal 1).
Initially a certain server capacity is given to the prior-
itized clients. This capacity is dynamically adjusted
during service execution in order to guarantee pro-
jected waiting time for prioritized clients and there-
fore quality of service (QoS). We have adopted fol-
lowing naming conventionto describe different issues
related to times spent in different phases of the order
processing process:
Queuing time - time spent in the line/queue while
waiting for the order placing.
Service execution time - time required to execute
the requested service by the server.
Service waiting time - the time that passes from
the moment of performing the order to the mo-
ment in which the order is completed.
Total service time - the time from the moment
the client has entered service point to the moment
when the required services are obtained.
4.2 Classification of Services by
Processing Power Requirements
Almost all orders differ from each other in type of
services requested, its quantity and quality which re-
sults in different service processing power require-
ments. Any order can be further decomposed in set of
’atomic services’. We assume ’atomic service’ to be
the simplest possible single order (i.e. an ice cream
or coffee). In that sense every order can be seen as
a composition of various ’atomic orders’. The most
precise way of modeling the orders would be repre-
senting them in form of number of atomic services
requests. Unfortunately this method would introduce
huge computational overhead and the entire system
would be very complex to implement. For the sake
of the simplicity and better efficiency but without los-
ing generality we classify orders according to number
of atomic services requested into five classes ranging
from small to huge ones. Each of those order types
are assigned certain evaluated average service time.
This evaluation is constantly updated by newly ob-
tained data from the system.
4.3 Service Time Prediction Model and
Scheduling for a Guaranteed
Execution
Here we describe empirical model for run-time calcu-
lation of service expected time. This time is provided
to clients as additional information and this property
of the system is one more original feature that en-
hances existing services. The service time is modeled
as a statistical variable with two components:
1. Non-stochastic component measuring the neces-
sary service time for non-prioritized and priori-
tized services with a given scheduling scheme.
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200
2. Stochastic component that incorporates the addi-
tional service time related to the average num-
ber of prioritized clients that are arriving during
the time slot defined with non-stochastic compo-
nent. The additional service time is added into
the scheduling scheme as a prioritized process and
a total service time is acquired after the probe
scheduling execution. This componentis acquired
by processing empirical data related to number of
prioritized clients and their orders that are dynam-
ically updated every day. During the phase of ini-
tial system deployment these data are unavailable
and therefore a worst case estimation is taken in-
stead of statistical component.
The order execution inside the server is scheduled ac-
cording to non-preemptive task execution scheduling.
We consider this scheme to be the most suitable in
case of fast food restaurants as preemptive service
would cause disorder in execution of currently exe-
cuted services and also would require different work-
ing concept inside the server (i.e. specialization to
atomic tasks execution and work division that could
support this would result in inefficient use of working
power).
In case that prioritized tasks are not schedulable
within dedicated server capacity for prioritized tasks,
the amount of dedicated server capacity is increased
to the minimal value that guarantees schedulability of
prioritized tasks. This has a consequence of increase
in waiting time for non-prioritized clients. However,
QoS for prioritized clients is guaranteed.
Any practical scheduling algorithm in multipro-
cessing server systems presents a trade-off between
performance and computational complexity. How-
ever, in our case scheduling computation time is not
an issue (because it can be in the range of seconds)
and we can explore the more complex scheduling
schemes. Scheduling could be regarded as soft real-
time or even non real-time problem. The Earliest
Deadline First (EDF) algorithm is the most widely
studied scheduling algorithm for real-time systems
(Balarin et al., 1998).
EDF is more efficient than many other schedul-
ing algorithms, including the static Rate-Monotonic
scheduling algorithm. However, when the process-
ing server is overloaded (i.e., the combined require-
ments of pending tasks exceed the capabilities of the
system) EDF performs poorly. Researchers have pro-
posed several adaptive techniques for handling heav-
ily loaded situations, but they require the detection of
the overload condition. Least Laxity First (LLF) al-
gorithm (Ramamritham and Stankovic, 1994) is non-
preemptive and selects the task that has the lowest
laxity (the maximum time that a task can wait before
Subtraction
Laxity
Priority
Server
Capacity
Time
Interface Engine
Total
Service
Time
Execution
Time
Run
Time
Priority
Figure 4: Inference model
being executed; laxity = total service time - service
execution time) among all the ready ones whenever
a processing server becomes idle, and executes it to
completion.
Lee et al. (Lee et al., 1994) presents a fuzzy
scheduling algorithm. Their proposed algorithm uses
task laxity and task criticality as system parameters
and doesn’t consider fairness. Their simulation model
contains small number of tasks on a uni-processing
unit system and they did not consider system over-
loads.
Chen et al. (Chen et al., 2005) proposed a schedul-
ing model and a related algorithm that is suitable
for both uni-processing and multiprocessing servers.
They provide a method to detect work overloading
and try to balance load with task dispatching. We pro-
pose to use model presented in (Hamzeh et al., 2007)
using a fuzzy interface engine. The model we pro-
pose has a slight modification considering that there
are only two levels of priority defined as ”high” and
”low”. As shown in Figure 4, the major factors con-
sidered in used approach to determine the scheduling
are task priority, total service time, service execution
time, and used server capacity time. The notion of
laxity is used in the proposed approach to facilitate
the computation.
In proposed algorithm as shown in Figure 5, a
newly arrived task will be added to the input queue.
This queue contains the remaining tasks from last cy-
cle that has not yet been assigned.
Fuzzy scheduler processes each task separately,
computes its run-time priority and sends it to task dis-
patcher’s priority queue. In a multiprocessing system,
this queue offers tasks to dispatcher by their run-time
priority order (as shown in Figure 6). Dispatcher of-
fers a new task whenever one of the processing units
of the system finishes its current task.
AN ENHANCED SERVICE PROVIDER COMMUNICATION INTERFACE WITH CLIENT PRIORITIZATION - Case
Study on Fast-food Chain Restaurants
201
1. For each task in input queue
(a) Feeds task’s run-time priority using fuzzy infer-
ence engine
2. While a server has a free processing unit
(a) assign the task with highest run-time priority to
the processing unit
3. Loop forever
(a) If a processing unit event occurs endenumerate
i. Go to 2
(b) If scheduling event occurs
i. Update tasks parameters
ii. Go to 1
Figure 5: Proposed algorithm
Task Queue
Task Scheduler
Task Dispatcher
Process Unit
Process Unit
Process Unit
Process Unit
Priority Queue
Figure 6: System view of real-time fuzzy scheduler
5 CONCLUSIONS AND FUTURE
WORK
In this work we have proposed a solution for deploy-
ment of e-Commerce concept in the fast food restau-
rant chain that brings more convenience for both ser-
vice provider and clients. The novelty of the work
lays in possible implementation of client prioritiza-
tion based on a well-known computer science concept
of operating system task scheduling, that will perform
the best under the assumption that we have enough
information considering stochastic component of ser-
vice time. For such a credible statistics we need to
have the insight of a system in a long run. The pro-
posed system brings numerousbenefits to both parties
involved in the service process.
The system will boost the efficiency of the ser-
vice by eliminating ordering waiting time and will cut
costs by decreasing staff needed for order acceptance
and delivery performing. It will also increase the vis-
ibility (if coupled with Internet - browsing and posi-
tioning) of the services, and enables better demand in-
sight that brings more flexibility. Moreover, it will en-
able market profiling and targeted profiling, and gives
the possibility to offer more e-services according to
clients’ need.
There are many benefits for the clients also. They
will achieve precise insight in offer and service con-
dition, the queuing time will be eliminated and they
will have the possibility to get delivery in short time
as prioritized users. They will also have more pay-
ment options, and multilingual interface.
Our future work will focus on collecting and
thoroughly analyzing statistical data. In the early
phase of system deployment, realistic assumptions for
worst case scenario needs to be made. Also, tun-
ing of scheduling algorithm needs to be performed
with a detailed testing with realistic data versus other
scheduling schemes.
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