1. Introduction
Today, with the rapid development of internet technology, cloud computing processing
capability has gradually matured, and its understanding has deepened. It continuously
improves task-scheduling algorithms, work efficiency, and people's life quality [1,2]. The maturity of cloud computing capabilities means that more high-quality resources
can be scheduled through a unified cloud platform, thereby optimizing the resource
structure. The advent of edge computing has brought about new demands for scheduling
computations on cloud computing platforms. Consequently, it has become a pressing
issue for cloud computing platforms to discover an accurate and high-quality scheduling
algorithm that can meet these demands effectively [3,4].
With the continuous promotion and application of cloud computing technology, the user
scale of cloud computing platforms continues to expand. The massive user data and
parallel user tasks bring great challenges to the cloud computing backend, requiring
optimization of task scheduling and allocation in cloud computing and finding efficient
scheduling optimization solutions for cloud computing platform tasks [5,6]. However, resource scheduling algorithms are difficult to adapt to the huge computational
workload of cloud platforms and have poor learning ability [7]. Therefore, heuristic algorithms have received significant attention from researchers,
and their powerful learning and adaptation capabilities are exactly what cloud computing
platforms require.
This study explores an improved task scheduling method of cloud computing platforms
for customer-oriented online training applications based on an ant colony optimization
(ACO) algorithm. The parameters of other ACO algorithms are set based on past experience,
so they are prone to fall into local optima and have low robustness. Therefore, based
on the classic ant colony algorithm, a virtual machine evaluation factor and pheromone
correction coefficient can be used to determine the optimal value of the algorithm
parameters and effectively improve the task execution efficiency of the algorithm.
The study utilized an improved ant colony algorithm to optimize and schedule task
resources on cloud computing platforms, effectively achieving a relative balance of
system load and reducing system energy consumption. On the basis of considering overall
coordination, the optimization of task scheduling on cloud computing platforms was
effectively improved.
2. Related Work
To promote a customer online training system to connect with the intelligent era,
the study of an intelligent cloud-computing scheduling strategy has become a focal
issue nowadays. Wu et al. believe that cloud computing systems are dynamic and diverse,
and their task and resource scheduling is very challenging. They introduced a novel
cloud-computing task-scheduling algorithm that leveraged particle swarm optimization
to model the resource scheduling problem in cloud computing systems. They formulated
an objective function of the task execution time and adopted the particle swarm optimization
algorithm to simulate the outcomes. Their proposed method had high utilization rates
of cloud computing resources, leading to a significant reduction in cloud computing
task time [8].
Smith believes that online training will be one of the most important learning methods
in the future. When people are faced with a threat from the environment and viruses,
they have to stay at home to study and work. Therefore, online training could become
an inevitable way of learning in the future. In addition, online training is also
the latest form of online education evolution and is the future of online education.
Online training covers the dual attributes of education and training, which can meet
people's learning and practical needs [9].
Majumder et al. proposed that online training is an important technical means of education
and training in imaging medicine. They believe that practice is an extremely important
part of medical imaging education and training and plays an irreplaceable role in
improving clinical experience. The emergence of online training platforms helped them
solve the difficulty of opening practical teaching after the COVID-19 pandemic and
provided great help for medical students. Therefore, actively promoting online training
platforms has become an inevitable trend of future development [10].
To solve the problem of cloud computing resource transmission, Lu et al. Proposed
mobile edge computing (MEC). For the problem of executing task scheduling in MEC servers,
they proposed a task scheduling-based queuing algorithm (TSBQ) that took into account
data transfer latency and server load and implemented a reasonable task allocation
policy. Experimental results showed that the MS-CE architecture outperformed other
architectures, and TSBQ was more efficient than Corral and Greedy [11]. Oliveros upgraded the online training system of a university and developed an interactive
online training system. With the help of this system, students and teachers had good
interaction. At the same time, schools and teachers could correctly help students
learn with the help of the system. This research provides a good reference for the
extension of an online training system [12].
As an excellent metaheuristic algorithm, the ant colony algorithm is widely used in
cloud computing. Many researchers use the ant colony algorithm to solve combinatorial
optimization problems in cloud computing. Ge et al. proposed a multidimensional quality
of service (QoS) cloud-computing task-scheduling algorithm based on an improved ant
colony algorithm while considering the QoS needs of users and the load balancing of
cloud platforms. They defined a QoS model composed of task completion time and execution
cost and defined a cloud platform load balancing constraint function.
They also addressed the shortcomings of slow convergence speed and easily falling
into local optima of the ant colony algorithm. The pheromone update method and expectation
heuristic function were improved, and the pheromone strength was dynamically changed.
The simulation results showed that the proposed algorithm was superior to the ACO
algorithm and max-min ant system (MMAS) algorithm in terms of user satisfaction and
cloud platform load [13].
Dahan proposed a quality-aware ant colony optimization algorithm to solve the problem
of cloud service composition in cloud computing. The ant colony algorithm was used
to optimize and perceive the service quality attributes of different suppliers, thereby
achieving cloud service composition to meet the complex and personalized cloud-service
needs of users. The study conducted algorithm-comparison experiments using 25 real
datasets, and the results showed that the ACO algorithm was the most competitive [14].
Liu et al. proposed an integrated model based on extreme learning machines and ACO
algorithms for virtual machine integration in cloud computing environments. The model
utilized extreme learning machines to predict host states and then migrated virtual
machines on overloaded hosts. The model combined ACO algorithms for local search and
group migration plan optimization to avoid excessive virtual machine integration.
The research results indicated that the integrated model based on extreme learning
machines and ACO algorithms can effectively reduce model energy consumption and improve
transfer speed [15].
Karmakar et al. introduced ACO algorithms to the optimization problem of virtual machine
placement to find the best solution for virtual machine integration. They explored
the application of ACO algorithms in solving multi-objective problems. They utilized
this approach to optimize the placement problem of virtual machines to satisfy the
dual requirements of service providers and users' service quality while also enhancing
the quality of virtual machine services and reducing operational costs for suppliers.
The study compared the ACO algorithm with multi-objective and single-objective problem-solving
algorithms, and the results showed that the ACO algorithm had the best solution performance
and convergence [16].
The optimization and convergence of intelligent task scheduling methods on cloud computing
platforms still need to be improved. At present, cloud computing platforms have poor
performance in task scheduling, poor load performance, and communication delays in
parallel multitasking processing, The ability and efficiency to solve high task volume
problems are not high. The ant colony algorithm performs well in solving cloud computing
optimization problems. Therefore, an improved ant colony algorithm was used for adaptive
optimization research to improve the task processing ability and efficiency of cloud-computing
task-scheduling algorithms. This study proposes using virtual machine evaluation factors
and pheromone correction coefficients to remedy the shortcomings of ACO in solving
optimization problems and optimize cloud-computing task scheduling.
3. Task Scheduling Method based on Ant Colony Algorithm
A cloud platform for a customer-oriented online practical training system was established
based on a general customer-oriented cloud platform, and its task scheduling model
follows the cloud platform rules and uses cloud computing for task scheduling [17]. The core processing power of cloud-computing task scheduling comes from the scheduling
algorithm. Through the deployment of the scheduling algorithm, the limited resources
of the cloud platform are reasonably allocated to each running task and the tasks
that will be run in the future. The structure of cloud-computing task scheduling is
shown in Fig. 1.
Fig. 1. Cloud-computing task-scheduling structure.
Cloud scheduling is a comprehensive scheduling method that combines static scheduling
and dynamic scheduling. Static scheduling is mainly for task scheduling for consumption
minimization optimization [18]. Dynamic scheduling focuses on resource queuing and load balancing issues for resource
provisioning. In addition, cloud-computing task scheduling requires high heterogeneity,
scalability, and dynamic adaptability of the scheduling system [19]. The goal of task scheduling is to ensure optimal span, guaranteed quality of service,
and load balance, and preferably to maintain a certain level of affordability. The
choice of scheduling algorithm for cloud computing is extremely demanding and is an
NP-complete problem.
The study used the ant colony algorithm for cloud computing scheduling using the traversal
city shortest path problem as an entry point. Assuming that $b_{i}(t)$ is the number
of ants presenting in the region $i$ at the moment $t$ , $\tau _{ij}(t)$ is the concentration
of pheromones left on the path ($i,j$) at the moment t. The number of cities is denoted
by $n$, and the total number of ants by is denoted $m$ ($m=\sum _{i=1}^{n}b_{i}(t)$).
$\chi $ is used to denote the set of information concentrations of any two cities
in the city set $C$ at the moment $t$, and $\chi $ =$\left\{\tau _{ij}(t)\left| c_{i},c_{i}\subset
C\right.\right\}$. The initial settings of the relevant parameters and the road-energy
pheromone are made before the start of the algorithm. $\eta _{ij}$ represents a heuristic
factor of the path ($i,j$), which is calculated as shown in Eq. (1).
In Eq. (1), $d_{ij}$ denotes the distance from node $i$ to $j$. The path length is inversely
proportional to the heuristic factor. When the path is longer, the heuristic factor
is smaller, indicating that it is less attractive to the ants. During the travel of
ants, the selection of paths is closely related to the concentration of pheromones
on path $\tau _{ij}(t)$. ``Tabu'' is used to refer to a taboo table to prevent the
ants from passing through duplicate paths, and ``$allowed$''refers to the next target
city of the ants, which divides the taboo table (i.e., $allowed$=$\left\{C-tabu\right\}$).
Then, the state transfer probability $P_{ij}$ of an ant moving from node $i$ to node
$j$ is calculated as shown in Eq. (2).
In Eq. (2), $\alpha $ is the influence coefficient of the pheromone, which indicates the degree
of guidance of the pheromone on the path, and $\beta $ is the influence coefficient
of the heuristic factor, representing its influence on the path selection. The ant
forms a complete path after traversing the nodes following the selection method in
Eq. (2). Every time it passes through a node, the ant updates the local pheromone, as shown
in Eqs. (3) and (4).
After traversing all nodes, the routes generated by the iterations are updated globally
with pheromones as shown in Eqs. (5) and (6).
$\rho $ in Eqs. (3) to (6) denotes the pheromone volatility factor, $\Delta \tau _{ij}(t)$ denotes the pheromone
increment on the path ($i,j$) at each cycle, and $\Delta \tau _{ij}^{k}(t)$ denotes
the pheromone increment generated by the ant $k$ when it passes through the path ($i,j$).
When applying ACO to practical problems, there are three common models: perimeter,
volume, and density. The method of the ant perimeter model is shown in Eq. (7).
In Eq. (7), $Q$ denotes the intensity constant of the pheromone, $T^{k}\left(t\right)$ denotes
the allocation scheme of the ant $k$, at the completion of the cycle, and $L_{k}$
denotes the length of the path passed by ant $k$ . The method of the ant volume model
is shown in Eq. (8).
The method of the anthropomorphic model is shown in Eq. (9).
The three algorithmic models are compared in detail in Table 1.
Table 1. Comparison of three ant colony algorithm models.
The research on task scheduling tends to consider the whole situation, so it is more
appropriate to choose the ant-week model for global information updates. The running
process of the ant colony algorithm is shown in Fig. 2. The ant colony algorithm has strong stability because the ants are simple in foraging
and they communicate with each other using only pheromones without interference from
other factors. By distributing the ant colony to different nodes, the path can be
found spontaneously. However, the ant colony algorithm also has problems such as slow
solution speed in the initial stage, easily falling into local optimal solutions,
computationally tedious selection of nodes, and large dependence on initial parameters.
In the beginning stage, the pheromone concentration on the paths is low, and the selection
of paths relies almost entirely on heuristic factors. This may lead to more ants choosing
longer paths, and the pheromones released during crawling may mislead subsequent ants,
leading to an ineffective search.
Fig. 2. Flow chart of ant colony algorithm.
In the early stage of ant search, ants may also rotate in place due to the few nodes
recorded in the taboo table, which may also reduce search efficiency. The accumulation
of key pheromones in the search process takes some time, so the search efficiency
of the ant colony algorithm in the initial stage is relatively low. The possible shortest
path is easily ignored by ants because the pheromone concentration on it is too low
or even 0. For the paths with high accumulated pheromone concentration, even if they
are not the shortest paths, the excessive pheromone accumulated on the paths affects
the path selection of ants and leads to locally optimal solutions because the pheromone
cannot be volatilized in time. When ants select the next node, the ant colony algorithm
needs to calculate the probability of state transfer between all unselected nodes.
When the problem size is large, the number of paths recorded in the forbidden table
is very large, and the final path plan of each ant grows exponentially.
3.2 The Improvement of Ant Colony Algorithm and Cloud Platform Optimization Adaptation
Research
During the crawling process, the probability of ants choosing path $\left(i,j\right)$
is related to the heuristic factor and the concentration of path retention information.
If $tabu$ is used to represent the taboo table, and only the cities that the ants
have already walked through are included, it can prevent ants from repeatedly selecting
paths. If allowed is used to represent cities outside the taboo table, the state transition
probability formula for ants transitioning from node $i$ to node $j$ can be represented
by Eq. (10).
In Eq. (10), the state transfer probability $P_{ij}$ has the characteristics of stochasticity
combined with determinism. According to its dynamic and static characteristics, the
optimization of the heuristic factor and the pheromone concentration on the path of
the ant colony algorithm can effectively improve the performance of the algorithm.
Combined with the operation resource scheduling, in addition to the efficiency of
the algorithm, the load of the virtual machine should be taken into account. The computational
capacity of the virtual machine is denoted by $E_{ij}$ , which is calculated as shown
in Eq. (11).
In the operation process, the heuristic factor is influenced by the computing power
of the VM $E_{ij}$ in addition to the direct impact of the performance of the VM itself,
which is required to add the VM evaluation factor $\sigma _{i}$ to evaluate the operation
status of the VM $VM_{i}$. $\sigma _{i}$ is calculated as shown in Eq. (12).
In Eq. (12), $E_{i}$ represents the overall ability of the virtual machine $VM_{i}$ to perform
tasks in the task set $Task_{i}$. The virtual machine evaluation factor $\sigma _{i}$
represents the overall stability of the execution, and a smaller value indicates better
stability. The optimized path heuristic factor $\eta _{ij}$ is calculated as shown
in Eq. (13).
In the ant colony algorithm, ants are influenced not only by heuristic factors when
searching for a path, but also by the concentration of remaining pheromones on the
path. However, since the ant's search path process is spontaneous, it is highly stochastic.
In an environment where the remaining pheromone concentration on the path is low in
the initial stage, ants may choose longer paths and also leave pheromones behind,
which may mislead ants, so all ants will choose longer paths. This makes the ant colony
algorithm too slow and inefficient in an early stage. In order to minimize the pheromone
interference with the ants on long paths during the search process, the global pheromone
update rule in the ant cycle model of the classical ant colony algorithm was improved,
and a pheromone correction factor $w$ ($0<w<1$) is introduced to solve this problem.
The pheromone update method was optimized using the correction coefficients, and the
results are shown in Eq. (14).
In Eq. (14), $w$ represents the correction factor of the pheromone, $L_{k}$ represents the total
path length currently traversed by an ant, $L_{\min }$ represents the length of the
shortest path in the traversal, and $L_{\max }$ represents the length of the longest
path in the traversal. If the ideal route reached by the ant during the search is
shorter than the shortest route found by all ants so far, the pheromone concentration
on the path is enhanced according to the coefficient, and more ants are attracted.
During a given search, if an ant discovers an ideal path that is longer than the shortest
foraging path that it has previously found, but it is still shorter than the longest
path, the pheromone on that path will slightly decrease. The reduction amount is based
on a coefficient subtracted by the ratio of 1 to the small proportion, which helps
minimize other ants mistakenly selecting a longer route.
On the other hand, if the ideal path discovered by this ant during the search is longer
than the longest path found by any of the ants so far, then the pheromone on the worst
path will be significantly decreased. The reduction amount is determined by the corresponding
coefficient, which helps reduce the likelihood of following ants incorrectly choosing
the worst path. The flow of the optimized algorithm is shown in Fig. 3.
Fig. 3. Flow chart of optimized ant colony algorithm.
As shown in Fig. 3, after the environment parameters are initialized, the ants begin to search and continuously
perform resource node searches according to the optimized state transition probability
method. As ants continue to move in new nodes, they constantly modify the tabu list.
Once an ant acquires the ideal route, it compares the feasible routes of all other
ants and records the best and worst routes. The global pheromone is then updated accordingly.
This process is repeated until the optimal route is achieved. The optimized ant colony
algorithm still needs a suitable initial setting to avoid problems such as falling
into local optimum solutions or wasting resources. The optimal solution is obtained
for the information correction coefficient $w$ , and the updated image of the pheromone
is drawn according to the ant-optimized shortest path, as shown in Fig. 4(a).
Fig. 4. Information correction coefficient correlation function image.
The iterative difference between the optimal solution and the worst solution can be
obtained from Fig. 4 and Eq. (14), as shown in Eq. (15).
The iterative difference between the optimal solution and the general solution is
shown in Eq. (16).
To achieve maximum optimization, the difference between the optimal solution and the
worst solution should be widened as much as possible. Conversely, to reduce interference,
the gap between the optimal solution and the general solution should be minimized,
as illustrated in Fig. 4(b). The difference derivation between Eqs. (15) and (16) is performed, and the result is shown in Eq. (17).
According to the calculation result, the maximum value of the difference is obtained
by making$1-2w$ equal to 0, (i.e.,$w=0.5$) and setting the pheromone correction factor
to 0.5.
4. Experiment and Analysis
The performance of the optimized ant colony cloud scheduling algorithm (OACC) proposed
in the study was simulated and experimentally analyzed using the platform CloudSim
4.0 in the Windows 10 operating system. The virtual machine parameters and task parameters
are shown in Table 2. The pheromone correction parameter w was introduced into the optimized ant colony
algorithm. To improve the accuracy of the experiment, OACC needed to be simulated
with the default parameter settings to determine the optimal value of the parameters.
The experimental results are shown in Fig. 5.
Table 2. Comparison of three ant colony algorithm models.
Virtual machine parameters
|
Task parameters
|
Parameter
|
Value
|
Parameter
|
Value
|
mips
|
120-480
|
length
|
1200-4800
|
cost
|
0.04-0.4
|
output
|
22-44
|
bw
|
40-400
|
size
|
180-1800
|
n
|
25
|
m
|
40-280
|
I/O
|
12 GB
|
\
|
\
|
Fig. 5. Simulation results of determining parameters by control variable method.
From Fig. 5, it can be seen that the parameter value with the shortest completion time was selected
as the optimal parameter value based on the control variable method. In the control
variables of the pheromone weighting factor, the shortest completion time was optimal
with $\alpha =1.4$. In the control variables of the pheromone weighting factor, the
shortest completion time is optimal with $\beta =2.6$. In the control variables of
the pheromone volatility factor $\rho $ , the shortest completion time is optimal
with $\rho =0.59$. In the control variables of the pheromone correction factor, the
shortest completion time was optimal when assigned a value of 0.59. In the control
variable of the pheromone correction factor, the shortest completion time was optimal
with $w=0.52$.
After setting parameters, comparative experiments were carried out on the simulation
platform, and the OACC proposed in this research was compared with the discrete firefly
algorithm (DFA), improved group search optimization (IGSO), and improved differential
evolution (MODE). Experiments were conducted on the four algorithms in the same experimental
environment, and the average of the results of 10 experiments was recorded. The comparison
results are shown in Fig. 6.
Fig. 6. Comparison of simulation results of different algorithms performing different tasks.
As shown in Fig. 6, when the number of tasks was small, there was not much difference in the performance
of each algorithm. As the number of tasks increased, there was a significant gap between
the OACC, DFA, IGSO, and MODE algorithms. When the number of tasks was 300, the makespan
value of OACC was 340, that of DFA was 350, that of MODE was 380, and that of IGSO
was 409. The overall performance of OACC was 20.3% higher than that of IGSO, which
met the expectations and reflected the effectiveness of the optimization algorithm.
Fig. 7. Load unbalance comparison of four algorithms executing different tasks.
The degree of load imbalance (DI) of the four algorithms executing different task
volumes is shown in Fig. 7. Fig. 7, it can be seen that OACC and MODE maintain low and stable DI values under different
task count tests. However, IGSO and DFA performed poorly. This result indicates that
the OACC proposed in this study outperformed IGSO in system load balancing, and the
research results are in line with expectations. The good performance of MODE indicates
that the virtual machine evaluation factor improved load balancing in the cloud computing
system. The OACC algorithm was applied to cloud computing task instances, and the
overall satisfaction of customer service was compared using the overall utility function
F. The overall utility comparison of the OACC proposed in this study with AOC, TAOC,
and LB-AACO is shown in Fig. 8.
Fig. 8. Load unbalance comparison of four algorithms executing different tasks.
As can be seen from Fig. 8, OACC performed the best for all different task quantities, followed by LB-AACO.
The other two algorithms performed relatively poorly. At a task quantity of 300, the
overall utility of OACC was rated as 146, which is 31.5% higher than ACO, 18.7% higher
than TACO, and 8.1% higher than LB-AACO. The results of this study indicated that
the overall utility of OACC was better than that of similar algorithms, and it can
perform well for cloud-computing task scheduling.
5. Conclusion
This research focused on the problem of task scheduling methods for cloud computing
platforms in customer-oriented online training systems. Based on the optimization
of the ant colony algorithm, an optimized ant colony cloud-computing task-scheduling
algorithm was proposed. The research results indicated that when the number of tasks
was 300, the makespan value of OACC was 340, that of DFA was 350, that of MODE was
380, and that of IGSO was 409. The overall performance of OACC was 20.3% higher than
that of IGSO. OACC maintained low and stable DI values under different task count
tests. At a task volume of 300, the overall utility evaluation of OACC was 146, which
was 31.5% higher than ACO, 18.7% higher than TACO, and 8.1% higher than LB-AACO.
The experimental results met expectations, indicating that the OACC cloud-computing
task-scheduling algorithm proposed in this study had high task processing ability
and efficiency and was capable of scheduling tasks on cloud computing platforms for
customer-oriented online training systems. However, there are also some shortcomings
in this research, such as the cloud computing scheduling tasks in the experiment not
being comprehensive enough. It is expected that in future research, more experiments
with different types of tasks can be conducted to promote the method to become more
comprehensive and accurate.
Funding
The research is supported by the Jilin Provincial Education Department 2022 Vocational
Education and Adult Education Teaching Reform Research Project. Key project: design
and research of thin customer online training system based on 5G and cloud computing
virtualization technology (No. 2022ZCZ011).
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Hongtao Wang obtained a Bachelor's degree in Computer Science and Technology from
Changchun University in 2006. Currently, he serves as a lecturer at the School of
Electronic and Communication Engineering of Jilin Electronic Information Vocational
and Technical College. In 2010, he obtained the positions of H3C Network Engineer
and Zhanbo Network Engineer, and in 2012, he obtained the positions of Cisco Network
Engineer. He guided students to win multiple first prizes in vocational skills competitions
in areas such as computer networks, information security, and cloud computing technology.
He has published over 10 articles on computer networks and cloud computing technology
in the journal. His areas of interest include computer networks, cloud computing,
and information security.