EE-(m,k)-Firm: A Method to Dynamic Service Level Management in
Enterprise Environment
Bechir Alaya
1,2
1
IResCoMath-Lab, University of Gabes, FSG, Erriadh street 6072 Zrig Gabes, Tunisia
2
Higher Institute of Technological Studies, University of Gabes, Tunisia
Keywords: Enterprise Management, Operations Analytics, QoS, (m,k)-Firm Model.
Abstract: Due to enterprises environment specificities, the operations management is actually a challenging problem.
In this paper, we choose to using a compromise between the available resources and the quality of service
(QoS) granularity. We join this compromise to a guaranteed technique in order to reach an intelligent loss of
Sub-operations according to the importance of each operation. The resulting approach permits the increase of
availability, performance, reliability and system dependability. The aim of our contribution is to ensure the
client satisfaction by increasing the QoS while dealing with the enterprises environment characteristics. The
effectiveness of what we propose is measured through a simulation study.
1 INTRODUCTION
In the last few years, Enterprise Performance
Management (EPM) is became an integrated business
intelligence solution that gives companies a
comprehensive view of their enterprise. Many of the
challenges that companies face today, which greatly
influence the enterprise performance and its quality
of service (QoS).
These challenges are specified by completely
uncontrollable variables, that show the current
operation of the system and its performances
compared to the optimal performance required by
managers. These variables considerably show the
current operation of the system and its performances.
Enterprise modeling remains always a challenge,
despite the significant advances in modeling
technology. The modeling for different points of the
company is necessary. Such a modeling is part of the
answer to the need for integrating the production
functions and specially the maintenance and QoS
guarantee. The policy that we propose can be
generalized and therefore applied to all the enterprise
functions.
Due to the similarities between data management
in RTDBS (Hamdaoui and Ramanathan 1995)
(Decker, 2014) and in enterprises (Barnes et al.,
2015), we propose to adapt some results obtained on
the management of QoS in the RTDBS to manage the
performance of companies. However, we present a
model based on (m, k) -firm model (Davide et al.,
2012) (Wang et al., 2004) (Cho et al., 2010)
(Goossens, 2008) studded in RTDBS to take into
account the congestion of systems workload in firms.
Our main objectives were to design a model that
meets the performance requirements of customers
and managers and provide QoS guarantees and
robustness when customer requests grow rapidly or
when company resources are congested.
This paper is organized as follows. Section 2
describes some work related to the management of
service quality; Section 3 shows the (m, k) -frim
model; Section 4 explains the different characteristics
of EE- (m, k) -firm model; Section 5 describes a
method for automating the processing of EE- (m, k) -
firm constraints; Section 7 analyzes the simulations
results and finely, Section 8 presents the Conclusion
and some remarks.
2 RELATED WORK
The QoS management in enterprise environment (Li
et al., 2006) (Partha et al., 2014) (Arboleda et al.,
2016) is a typical problem. Recently, several studies
have been based on this topic. In (Arboleda et al.,
2015), the authors proposed that to reach a superior
performance, it is necessary to suggest (i) the
114
Alaya, B.
EE-(m,k)-Firm: A Method to Dynamic Service Level Management in Enterprise Environment.
DOI: 10.5220/0006322401140122
In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 114-122
ISBN: 978-989-758-247-9
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
adoption of appropriate strategic behaviors to client,
to competitor and to technology and (ii) the targeting
of the appropriate market segments, notably early
adopters, innovators, early majority, laggards and late
majority. In their proposition, the strategic behavior
of corporate performance relationship is subject to the
company's strategy by examining this relationship on
high technology markets and considering further
contribution of the appropriate target market
selection. This approach provides useful orientation
to business managers to the steps that they should take
to augment their performances.
The authors of (Barnes et al., 2015) accentuated the
effect of interpersonal factors on company's
performance through the relationship quality and the
intervening roles of intercompany trust. The authors
justified that trust plays an instrumental role in
enhancing the components of the inter-firm
relationship quality. They showed that inter firm
relationship quality is positively related to superior
financial performance, and most of the associations
between each of the interpersonal factors and inter
firm trust were moderated by the importer's size and
foreign supplier's origin as well as the length of the
relationship and which party initiated the relationship.
In (Slater et al., 2007), the authors proposed a
technique to improve the risk analysis in Enterprise
Resource Planning (ERP) (Li et al., 2006) (Mehrjerdi,
2010). They aimed to obtain a more structured
systematic model of the different relationships
between the risk factors/effects associated with ERP
projects and attain a better understanding. The major
objectives of their work were to (i) allow a
collaborative approach to risk analysis, (ii) help the
administrators in treating and controlling project risk
and (iii) help the administrators to comprise the links
between the development of a relevant risk analysis
strategy and the evaluation of a global risk index for
each factor used.
Demand Response Management (DRM) which is a
key component of the future smart gridDemand
Response Management (DRM) was the subject of
(Chai and Chen, 2014). In fact, the author studies
DRM with different public service companies.
Depending on the requirement of enterprise,
several types of information systems have been
improved for various goals (Krell et al., 2016). A
study in (Al-Mamary et al., 2014) attempted to
demonstrate the role of each type of information
systems in firms' organizations. According to
O’Brien & Marakas (Brien and Marakas, 2010), the
applications of information systems that are
implemented in today’s business world can be
classified in several different ways. In enterprises
world, there are varieties of information systems such
as, Office Automation Systems (OAS), Expert
System (ES), Transaction Processing Systems (TPS),
Management Information Systems (MIS), Executive
Information Systems (EIS), Decision Support System
(DSS), etc. Each type of information system has a
specific objective in management operations and in
organizational hierarchy (Alam et al., 2015).
Other researches were proposed in (Kadiri et al.,
2016) (Atkinson et al., 2015) to present customized
views of enterprise systems to various stakeholders
according to their competencies and requirements.
For a better QoS, they were interested in developing
and improving the services and languages offered by
such tools on a continuous basis. They discuss the
weaknesses and strengths of different approaches
(Nikolow et al., 2013) interested in language
development and proposed a modeling framework
more able to support the main extension scenarios
currently found in practice.
3 ENTERPRISE AND QoS
MANAGEMENT
3.1 (m,k)-Firm Model
The recurrence of tasks in real-time systems allow to
ignore some invocations (or jobs) using (m,k)-firm
constraints. These constraints specify that in a
window of k invocations, at least m tasks (0 m k)
must respect these deadlines (West and Zhang, 2004)
(Hamdaoui and Ramanathan 1995) (Cho et al., 2010).
Otherwise, for k tasks, m tasks are required and (k-m)
tasks are optional. In (Bernat, 1998), Bernat showed
through an example why it is best to use two
parameters to define this type of constraints.
Furthermore, it has been shown that the concept of
(m,k)-firm constraints is appropriate for specification
(management) of QoS of real-time application (Wang
et al., 2002). To effectively manage the tasks under
(m,k)-firm constraints, new scheduling algorithms
have been proposed (Hamdaoui and Ramanathan
1995) (Ramanathan, 1999). They are divided into two
main groups units, (1) Dynamic algorithms
(Hamdaoui and Ramanathan 1995) and (2) Static
algorithms (Dixon and Verma, 2013). Briefly, the
static algorithms provide a deterministic vision of the
system, while the dynamic algorithms rather provide
a probabilistic vision. The dynamic algorithms take
into account any system modification.
EE-(m,k)-Firm: A Method to Dynamic Service Level Management in Enterprise Environment
115
3.2 (m,k)–Firm Constraints
Application
The enterprises management aims to meet the client
requests by integrating many constraints: the costs,
products quality, deadlines, customer demand,
necessary personnel, infrastructure, supply of raw
materials, etc.
The enterprise management must take into account
four main types of constraints (Dixon and Verma,
2013).
The main objective in enterprise environment is
both quantitative and qualitative. The QoS
degradation implies the degradation of system
performance in such a way that the system continues
to function but with a disequilibrated level of QoS. In
an overload situation, the production and QoS
degradation is inevitable since clients' demands will
always be dropped or delayed although many clients'
demands can tolerate some delay if they arrive with a
permitted mode. Moreover, the effect on QoS in
enterprise environment depends on how and when the
degradation is present.
The proposed method can be described as follows:
a task in process of industrial production is
constrained by (m,k)-firm requirements if at least m
task instances within a range of k consecutive tasks
respect their intended deadlines. If more than (km)
deadline of tasks fail in k consecutive tasks at that
moment, we can mention that the tasks will fall in a
dynamic failure state. Consequently, the QoS
constraints will not be satisfactory for the customers.
For each enterprise branch, the values of m and k vary
according to the criticality of tasks and system load.
In practice, the values indicated by the industrial
systems are not all of the same importance.
3.3 Tasks Management and Adaptation
The tasks of an enterprise system are decomposed
into several classes according to their tolerance to
tasks loss.
We consider three classes of tasks in the industrial
environment: critical task, hard non-critical task and
optional task. With this technique, which we called
(m,k)-firm in Enterprise Environment (EE-(m,k)-
firm), we can realize a compromise between the
available resources and the QoS granularity in the
same type of task.
In this work, we focused on the adaptation of the
number of tasks to the system load state. We assumed
that measures of the system capacity were available
on the one hand and that we had a significant number
of client demands on the other hand.
We also assumed a system situation in a production
enterprise, whose actual performance is N, was
overloaded. We supposed that Optimal QoS (Opt-
QoS) was the quality of the client demand
necessitating M tasks. In order to be coherent with the
system performance, it was requisite to throw (MN)
tasks. Consequently, we had to reduce the quality of
the client demand and if necessary, we could remove
some tasks. However, the removal without applying
a control method would be arbitrary.
The removed tasks are lost from the system,
causing QoS degradation, notably if some critical
tasks (Goossens, 2008) are removed. In this work, we
adapted the EE-(m,k)-firm constraints, that serve to
discard some tasks but intelligently.
The three classes of tasks were proposed to adjust
the QoS requested by the clients based on real system
capacity. We proposed that constraints for each task
category were fixed as follows: EE-(mc,kc)-firm for
critical tasks, EE-(mh,kh)-firm for hard non-critical
tasks and EE-(mo,ko)-firm for optional tasks.
Notably, mc tasks must be executed among kc tasks.
The system capacity was calculated using the
formula: mc + mh + mo, where mc and kc present the
constraints of critical tasks .The constraints of
different task classes are organized as follows:
mc>mh>mo. In the enterprise environment, we
usually propose that mc=kc, given that these types of
tasks are critical and that it is not recommended to
lose them.
With the application of our EE-(m,k)-firm policy,
we suppose that the required capacity necessary to
respond to an enterprise transaction is M. With:
M=kc + kh + ko,
N=mc + mh + mo.
We proposed how to equilibrate the QoS at the
tasks level in a production enterprise according to the
available system capacity. We began by calculating
the required capacity by all the current clients. Then,
we calculated the rate that presents the ratio between
the available system capacity (N) and the required
capacity.
=
=
k
i
i
DR
N
Rate
1
(1
)
Given that:
k present the number of available tasks in the
system.
DRi present the demanded resource by task i.
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
116
4 INTELLIGENT TASKS LOSS
In order to guarantee an intelligent loss of tasks
according to their importance, we defined a method
which describes how a client demand is composed of
k tasks. These tasks follow a well-defined
organization when the system resources are not
available to respond to all necessary tasks to have the
requested QoS.
Given the differentiation between tasks, we
already proposed that the critical tasks are two
categories. In one part, the mandatory and critical
tasks (C1) without which the client demand will not
be realizable. In the other part, the critical tasks (C2)
without which, the client demand can be realizable,
but the QoS will be extremely poor.
Similarly to the hard and non-critical tasks, we
proposed a classification into two categories, namely
H1 and H2. Finally, for optional tasks that usually
reflect the QoS degree we have two classes (O1, O2)
according their importance for QoS level.
Consequently, The k tasks of a client demand
based on EE-(m,k)-firm constraints are represented
by a succession of k elements from
{C1,C2,H1,H2,O1,O2} as described previously.
In enterprise environment, it is difficult to have an
approach that guarantees an optimal QoS for all
clients arriving to the system. Using this intelligent
loss specification, each client can show these EE-
(m,k)-firm constraint according to requested QoS and
the available system resources. A minimum QoS is
guaranteed if at least all critical and mandatory tasks
are executed. Notably, if some optional tasks are
missed by the system, the degradation will be only to
EE-(mo, ko)-firm constraints, but not to EE-(mc, kc)-
firm constraints.
These constraints are extremely appropriate in
order to extract all requirement of a client demand. In
all cases, a client demand is represented by a
succession of critical tasks, hard tasks and optional
tasks.
The loss of tasks in a type of critical tasks and/or
in a type of hard tasks necessary for a client demand
will cause some degradation in the following tasks
until a new demand occurs, although the optional
tasks loss has no effect.
4.1 Dynamicity of EE-(m,k)–Firm
Constraints
To improve performance, availability, reliability, and
system dependability, we applied a method of
dynamic treatment of tasks. The objective was to
automate the treatment of EE-(m,k)-firm constraints.
The need of responding to critical and hard tasks
is most crucial when it comes to sensitive systems
where an error can be humanly costly other than
financially. This is the case, for example, for nuclear
reactors, chemical factory, aircraft systems and many
others. Following to importance of client demand and
the importance of meeting their QoS requirements,
we associated a dynamic analysis modules to EE-
(m,k)-firm constraints, to optimize the gap between
the rate of received QoS and the rate of desired QoS.
In some situations, the system stops the operation of
any other tasks (critical, hard or optional) to respond
to tasks that are humanly more critical.
A deviation detecting module between the
provided QoS by the system and the desired QoS by
the client has become necessary. Then, according to
task type, a consultation of gap impact must be
carried out. Finally, the system decides the necessary
values of EE-(m,k)-firm constraints.
4.2 Detection and Localization Gap
The detection procedure aims to determine the
occurrence of a gap between the values m and k of
each task type that has a specific EE-(m,k)-firm
constraints fixed by the system. Indeed, because the
properties of different tasks according to their types,
the difference between MC and KC is more important
than the difference between MH and KH and also
between MO and KO. However, this detection
procedure will be applied to all possible types of
constraints. Generally, for proper operation of an
enterprise, these differences are usually of zero mean,
which represents an optimum QoS to clients.
A means to auto-observe the gap between
different EE-(m,k)-firm constraints is to estimate the
needed values for each constraints type (KC, KH and
KO). The estimated values of MC, MH and MO are
then respectively subtracted from maximum
constraints KC, KH and KO to form the gaps E(C),
E(H) and E(O) as follows:
O
M-
O
K =E(O)
H
M-
H
K =E(H)
C
M-
C
K =E(C)
Given that KC> MC, KH> MH and KO> MO.
At production times in an enterprise, the gap E(·)
will significantly deviate according to the increase of
system load, it will be equal to zero except when the
system operates normally. In real applications, the
differences are not exactly a zero value for the
systems absence that accurately reflects the actual
state of resources. Besides, the assigned
measurements that aim to reflect the available
resources are often marked by measurement noises.
The optimal QoS for clients varies according to
values of measurement noises. With the proposed
treatment of EE- (m,k)-firm constraints (see figure 1),
EE-(m,k)-Firm: A Method to Dynamic Service Level Management in Enterprise Environment
117
Figure 1: Treatment structure.
and depending on the criticality of supplied products
(chemical, nuclear ...), the values of KC and KH must
be accurately measured. The gaps will then be written
as:
O
M-
mO
K =E(O)
H
M-
mH
K =E(H)
C
M-
mC
K =E(C)
Where Km (·) is the value measured by the system
and which is composed by the real value K(·) and the
various types of noises relating to the calculation
uncertainties .
To guarantee the application of EE-(m,k)-firm
constraints, we propose a comparison method of each
gap E (.) at an optimal predefined threshold for each
type of task: Threshold ε for critical tasks, ε' for hard
tasks and ε'' for optional tasks, respectively. At every
crossing of threshold, an alert is sent to the system for
a new QoS management, we will then have:
1 =Alert > E(C)
0 =Alert E(C)
ε
ε
1 =Alert ' > E(H)
0 =Alert ' E(H)
ε
ε
1 =Alert '' > E(O)
0 =Alert '' E(O)
ε
ε
After detecting the presence of a gap between M
and K, it is necessary to locate the task type affected
by this gap. This is nominated by the gap localization.
At the realization, we proceed at a structuring of
all generated gaps during the system function.
Generally, we constructed a first set of gaps Ei (·) that
depend on the tasks types. From these basic gaps, we
form two types of gaps: hard gap and soft gap.
In case of hard gap, after receiving an alert, the
system immediately acts even by an intelligent
violation of allocated resources to other clients’
demands. This gives a dynamicity of resources
allocation and EE-(m,k)-firm constraints. However,
in case of soft gap, the system does not immediately
act, but waits for the availability of resources to
respond to this task type. During system function, the
EE-(m,k)-firm constraints dynamically vary
according to priority of client demands, system load
and gap type.
We will have a decrease in optional tasks number
and an increase in critical tasks number. For hard
tasks, the number varies depending on the decrease
and increase of critical and optional tasks.
5 SIMULATIONS AND RESULTS
We now detail the implementation of the EE-(m,k)-
firm policy. Four types of decisions should be taken
by our policy. We first describe the necessary data
structures, and then we consider each of these
decisions separately.
5.1 Description of Data Structures
Table.1 shows the data structure for each client
demand. In a table noted table of demands in which
each line contains the tasks number of a demand, and
the class of popularity (EE-(m,k)-firm constraints),
indeed, three classes are present.
The first refers to the C tasks (Critical) which are
the most requested tasks by the system. The second
regroups tasks of average importance H (Hard). The
third contains optional L tasks (Low), i.e. least
required by the system. Tasks table (table 2) records
various information about the demanded tasks. Note
that, the demands may not have the same number of
Table 1: Demands table.
Demand-id
Requested
tasks
EE-(m,k)-firm
constraints
C H L
Demand_1 14 4 4 6
Demand_2 7 3 2 2
Demand_3 9 4 3 2
Demand_4 4 3 1 0
tasks. Each entry in the units table (table 3)
corresponds to a unit and maintains several counters
that keep track of free and served resources.
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
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First, for load balancing on the units, the choice
of the tasks will be on a lightly loaded unit that is
selected for execution.
Table 2: Tasks table.
Number
of task on
execution
Number
of unit
tasks
Unit-id
Task_1
60 4
U
1
U
3
U
2
U
4
Task _2
70 2
U
2
U
3
Task _3
50 3
U
3
U
1
U
4
Task _4
20 4
U
2
U
3
U
1
U
4
This is achieved by traversing the entrance of the
tasks table to find all the units that contain the type of
requested task (including tasks in progress) and then
looking into the set of the corresponding units to find
the least loaded unit. Whereas unit Ui has completed
execution of a task Tj, the data structures must be
updated, to indicate a resource liberation on Ui. This
is done by resetting the counter in the entry of tasks
table.
Table 3: Units table.
Id-
Unit
Total
resource
(Rs)
Free
Rs
Served
Rs
Rs in free
admission
U1 1000 100 900 10
U2 3500 700 2800 5
U3 2000 300 1700 7
U4 1500 200 1300 20
5.2 Unit and Task-type Selection
After having taken the decision to get the process of
responding to a client demand, the EE-(m,k) -firm
policy must select the tasks using the different
constraints.
The execution begins when the system completes
the selection of different tasks types of a demand.
Note that the EE-(m,k)-firm policy does not change
the task type simply because a resource is released by
this task or because it has caused a unit overload.
This avoids the problem of changing the task type
which slightly affects the QoS requested by the client.
The estimated profit of Pti to execute a task i of a
demand is a measure of future load that can be
reduced from the current unit. This is calculated as
follow:
Pt
=
1
t
1
t
+1
n


w

(2
)
Where W represents the weighting factor. The
motivation for using this formula is to change the task
type where the advantage in terms of load is expected
to be higher in the future.
The load that can be changed in the near future
(execution task time) is given by the load on the
previous task. However, the load on the previous
tasks represents the load that can be changed
gradually in the future. To further improve the
performance of the immediate load transfer, the profit
to execute a task was calculated by weighting
exponentially.
Algorithm 1.
Ti = number of tasks of i type
Pti = Execution profit of task i
Si = unit number that can execute the
task i
Sth = threshold of tasks number
Pi = Popularity of i task
Pmin = min (Pi)
Pmax = max (Pi)
Pmoy = (Σi = 1..N Pi) / N
Class L = [Pmin (Pmin + Pmoy) / 2]
Class H =[(Pmin+Pmoy)/2,
(Pmax+Pmoy)/2]
Class C = [( max + Pmoy) / 2, max]
V (V1 ... ..Vj ...... .VN)
For (j = 1 to N)
If (Vi H Class)
For (i = 2 to tasks number)
If (Si> Sth)
R = round ((Si-sth)/quota)
If (Pti superior to all benefit of
another tasks)then
Execute the task i in the first
selected unit
The load on the precedent tasks can be found from
entries matching tasks in the tasks table. Also, if there
are ti tasks of the current demand i, creating a
modification of task type result (1/(ri -1)/(ri + 1)) of
profit in terms of load movement. The current number
of tasks ri will be also available in the current task
entry.
5.3 Simulation Results
First, we discuss the impact of the tasks number on
the system response time (access delay) with ''fixed
EE-(m,k)-firm constraints'' and ''dynamic EE-(m,k)-
firm constraints''. Figure 1 describes the
representative results for different values of demands,
such as 10, 20, 30, 40 and 50.
Figure 2 shows that the response time for all
curves decreases with the increase of tasks number.
The time considerably decreases between a low tasks
EE-(m,k)-Firm: A Method to Dynamic Service Level Management in Enterprise Environment
119
number and an important tasks number. Indeed, the
load balancing between different tasks types is
significantly reduced. This is due to dynamics of tasks
treatment that affects several factors. In particular, the
access delay, in case of a unit overload improved with
a dynamic EE-(m,k)-firm constraints, since it
depends on tasks criticality that will be dynamically
treated by the system. Thus, the system, which will be
able to answer, has several tasks with these dynamic
treatments, leading to improve management of the
storage space and the QoS. This means that the EE-
(m,k)-firm policy brings several benefits other than
reducing the access delay.
Figure 2: Access delay.
At the tasks execution, the system begins the tasks
dispersion between the necessary units. The second
application of EE-(m,k)-firm policy results in the
correct application of dynamicity of the policy
showing that mC and kC have the highest priority.
The graph in figure 2 shows the behavior we
expected. We can equally notice from the same figure
that EE-(m,k)-firm policy, with fixed or dynamic
constraints in all loads requirement, gives a shorter
response time.
But, we note that when increasing the tasks number,
the difference between results decreases.
Consequently, we can predict that if the number of
tasks attains a certain threshold, there will be no
difference between the different algorithms.
Figure 3: Reject rate.
Figure 3 shows that our policy with a dynamic
treatment of EE-(m,k)-firm constraints significantly
reduces the rejection rate. The difference between the
curves, using fixed and dynamic constraints, shows
the improvement of tasks acceptance rate. The gap
between sub-curves of EE-(m,k)-firm policy with a
dynamic constraints m and k on different numbers of
demands, shows the effectiveness of this dynamicity
on the rejection rate. The ratio between the decrease
of rejection rate with the increase of demands number
shows that when the tasks number increases, the
curves of our policy will be confused. We can
conclude from these comparisons that our proposed
policy achieved the desired results, even with a large
tasks number.
The served tasks rate present the ratio among the
number of received and executed tasks and all
requested tasks.
We describe the case of little workload arriving to
the case of the best workload. With dynamic
constraints of EE-(m,k)-firm policy and in case of
weighty system workload, our policy substantially
affects the rate of served tasks. Consequently, at
different workloads, EE-(m,k)-firm policy is
powerful to overcome the system congestion
problems (figure 4). From this study, we notice that
the EE-(m,k)-firm policy provides satisfactory
results.
Outstandingly, EE-(m,k)-firm policy leads to an
important number of served tasks in the case of high
workload, up to 98% with dynamic constraints and
about 49% with fixed constraints.
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
120
Figure 4: Served tasks rate.
6 CONCLUSIONS
The main purpose of this study was to present a novel
policy of a specific treatment technique of tasks in
enterprises environment. We proposed the EE-(m,k)-
firm policy to indicate the necessary tasks and
calculate their ranking, using a compromise between
the available resources and the QoS granularity in the
same task type. Based on an in-depth review of the
relevant literature, three categories of tasks are
possible in enterprise environment, namely critical
tasks, hard tasks and optional tasks.
Afterwards, a guaranteed technique was applied
to losses tasks intelligently according to importance
of each task. A dynamicity of EE-(m,k)-firm
constraints is then used to attain an increase of
availability, performance, reliability and system
dependability.
The results obtained from the proposed policy
reveal that a ‘‘lack of awareness regarding the
benefits of dynamic treatment of tasks in an
intelligent and dynamic manner in enterprise
environment is the most important reason behind the
implementation of EE-(m,k)-firm policy. This type of
policy can be extremely valuable for companies that
wish to focus their efforts and resources to guarantee
a satisfactory QoS for end-users and challenges
toward the successful implementation of tasks
management.
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