=Paper=
{{Paper
|id=Vol-2588/paper4
|storemode=property
|title= Transaction Planning Methods in Hyperconverged Architecture Systems
|pdfUrl=https://ceur-ws.org/Vol-2588/paper4.pdf
|volume=Vol-2588
|authors=Serhii Bulba,Nina Kuchuk,Anna Semenova,Zhengbing Hu
|dblpUrl=https://dblp.org/rec/conf/cmigin/BulbaKSH19
}}
== Transaction Planning Methods in Hyperconverged Architecture Systems ==
https://ceur-ws.org/Vol-2588/paper4.pdf
Transaction Planning Methods in Hyperconverged
Architecture Systems
Serhii Bulba 1 [0000-0003-0358-7516], Nina Kuchuk 1 [0000-0002-0784-1465],
Anna Semenova 1 [0000-0001-7192-5156] and Zhengbing Hu 2 [0000-0002-6140-3351]
1
National Technical University «KhPI», Kyrpychova str., 2, Kharkіv, Ukraine
nina_kuchuk@ukr.net
2
Central China Normal University, Wuhan, China
hzb@mail.ccnu.edu.cn
Abstract. The analysis of the features of the functioning of systems with
hyperconverged architecture is carried out. Transaction efficiency in such
systems is reduced compared to decentralized systems. The purpose of the
research: to develop a method for planning transactions in systems with a
hypeconvergent architecture, which will take into account the specifics of the
functioning of such systems. The development of the method takes into account
the centralized management of the transaction package and the distribution of
various resources. Existing methods for determining the sequence of
transactions in systems with hyperconverged architecture are considered. The
methods that were considered were based on the greedy, clustering, and ant
algorithms. For each method, its features and functioning scheme are
determined. Analysis of existing methods showed the advantages of the greedy
algorithm with a small system load. It is also proved that with the growth of
information volumes and the number of simultaneously executed transactions,
each of the methods considered is less effective than with decentralized
management. Therefore, a method for planning the execution of transactions
through the sharing of these optimization algorithms is proposed. This allowed
to reduce the execution time of the optimal transaction plan in comparison with
existing methods. The experiments showed that the effectiveness of the
proposed method increases with increasing amount of information. Which is
processed by a transaction package of a hyperconverged system.
Keywords: hyperconverged architecture, transaction, greedy, ICT, cluster.
1 Introduction
1.1 Motivation
Today systems with hyperconverged architecture (HCA) are actively developing.
Such systems significantly reduce maintenance costs through centralized manage-
ment. In addition, the extension Software and Hardware becomes cheaper due to the
block hardware and software structure. However, systems with hyperconverged archi-
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attrib-
ution 4.0 International (CC BY 4.0) CMiGIN-2019: International Workshop on Conflict Management in
Global Information Networks.
tecture outperform decentralized systems in transaction efficiency. Therefore, new
methods of working with transactions that take into account the features HCA. Partic-
ularly it is necessary to develop effective methods for planning the execution of trans-
actions in HCA.
1.2 Analysis of related works
System analysis with HCA much attention is being paid. So in [1] hyperconverged
platform features analysis conducted. The issues of reducing maintenance costs in
systems with HCA are worked out in detail in [2, 3]. The use of HCA in data centers
in the analysis and deployment of Big Data is discussed in [4]. Authors in [5] ana-
lyzed the possibility of combining HCA with local cloud components. In [6] attention
is paid to the features of centralized architectures when working with databases. Data
protection methods in HCA systems are addressed in [7, 8].
Transaction planning in various systems in order to reduce the transit time of in-
formation flows was considered by many authors, for example [9- 16]. So in [9, 10]
considered the use of greedy algorithms. Clustering algorithms are considered in [11-
13]. It is shown that with an increase in the volume of information flows, they surpass
greedy in efficiency. However, all the considered algorithms do not take into account
the specifics HCA. Similar problems arise when applying ant algorithms described in
[14-17].
Considering the aforesaid, there is a need to improve the efficiency of transaction
planning in systems with HCA. Therefore, new planning methods that take into ac-
count the features HCA.
1.3 Goals and structure
One of the important tasks in developing systems with HCA is to increase the effi-
ciency of transaction package execution. The goal of this chapter is to describe appro-
priate approaches intended for such problem solution. A mechanism for solving this
problem is proposed. Initially, various transaction planning methods are analyzed.
Then, existing methods for planning the execution of transactions in systems with
HCA are considered. Based on their analysis, a more effective, combined method is
proposed.
The material is structured as follows. Sections 2 and 3 describe methods based on the
greedy and clustering algorithm. Section 4 describes the ant algorithm and proposes a
combined method. Section 5 is devoted to the results of a study of the effectiveness of
the proposed method.
2 Planning the РCS transaction package using greedy
algorithms
Greedy algorithms are most effective when planning without taking into account
deadlines for completing transactions. The greedy algorithm makes the best decision
based on the data currently available. The essence of the greedy algorithm lies in the
locally optimal choice at each step. To implement the algorithm, you must define the
following elements of the problem:
a set of options from which to choose;
a selection function with which the best option is found;
a usability function that determines the usability of the obtained set;
a purpose function that assesses the value of the solution and is not usually
explicitly expressed;
A decoupling function that indicates that a final solution has been found. The main
disadvantage of greedy algorithms – high probability that solutions found will not be
optimal. The main advantage is the speed of finding a solution. It was therefore
advisable to use them in finding a rational solution.
If you use greedy algorithms to plan for the use of hyperconversion resources, you
should [18]:
1) consider a set of tasks at each stage of resource allocation, which may belong to
different transactions;
2) an existing set can contain only tasks, which can be executed at the moment; this
is the criterion for choosing a task (CCT);
3) select one task from a set (by task selection criteria), after that, the selected task
will receive the resource according to the second resource assignment criterion (CRA,
Fig. 1).
Tasks PCS resources
CCT CRA
Fig. 1. Planning tasks with two criteria
In this case, the resource assignment decision is made before the tasks are started.
A static plan is generated.
Note the following:
1) to select a task, you need to use criteria based on taking into account the
deadline for completing the task (and not the deadline);
2) the resource assignment criterion allows you to determine the type of resource
for the task. The task completion time is calculated taking into account the current
resource load.
The principle of choosing set tasks StPar, intended for distribution is such:
1) the set StPar formed from tasks that have no outstanding predecessors;
2) completed tasks are removed from the set StPar.
To replenish the set StPar there are following methods:
1) the method PlanT: condition tasks added to the set StPar only if the conditions
are met:
а) the task is not distributed and is not contained in the set;
b) all predecessors have been distributed for the task;
2) the method ExecutT: the set StPar is formed by adding tasks in which all parent
tasks have been completed.
The developed distribution method has the following form:GreedPlan (CCT, CRA,
PlanT|ExecutT).
A large flow chart using the GreedPlan method using greedy is shown in Fig. 2.
Tasks
PlanT|ExecutT
DS11 CCT
CRA PCS resources
PlanT|ExecutT
DS12 CCT
CRA
PlanT|ExecutT
CRA
DS13 DS14
CCT
CRA
PlanT|ExecutT
DS15 CCT
Fig. 2. Large scheme of the method GreedPlan
3 Planning of HCS transaction package execution using task
clustering methods
When distributed based on task clustering, many tasks are divided into subsets
depending on resource requirements. With vertical clustering, each transaction is
divided into groups of tasks, taking into account the end of previous tasks (Fig. 3).
With horizontal clustering, it becomes possible to enter different tasks into the same
cluster. In horizontal clustering, accounting for the end of previous tasks is also
necessary. An example is given on Fig. 4 [19].
Clusters
DS11
cl1
DS12
DS13 DS14 cl2
DS15
cl3
Fig. 3. Vertical clustering
Clusters
DS11 cl1
DS22
DS 12
cl2
DS23 DS24
DS 13 DS 14
DS 15 DS25 cl3
Fig. 4. Horizontal clustering
The task distribution process using the presented clustering methods is divided into
stages. Each stage is assigned a set of tasks to be distributed. Adding a task to the set
of StPar tasks is done only using the ExecutT method. The clustering based
distribution step is to place a subset of the tasks of one or more transactions. The
number of stages depends on the way tasks are grouped. Also, when distributing tasks
into clusters, it is necessary to take into account the sequence of tasks in each
transaction.
During clustering set clusters are created. It can be characterized by such
parameters as the number of clusters and the cluster parameter. The cluster parameters
can be the minimum or maximum values of the following values: the time of
completion or start of the task, when calculating the task, and others. During
clustering, these parameters may not be known. If during the distribution of the task
the clusterization parameters are unknown, then these types of clusters arise:
1) consolidation of all transaction tasks in one cluster;
2) task based clustering.
The first approach does not allow even a minimal influence on the distribution
process. In this case, there is no way to influence the beginning or completion of a
particular transaction. With this approach, the number of clusters is the same as the
number of transactions. Such a distribution is always correct. It makes it possible to
take into account the priority of transactions. But this approach does not allow for a
fair distribution of resources when executing a transaction package.
Consider a few cases.
1. The number of clusters and clustering parameters specified. In this case, static
clustering occurs. With such clustering, it is necessary to take into account the correct
distribution after the clustering process.
2. The number of clusters is specified. Clustering parameters are assign during
clustering. In this case, each task is assigned to a specific cluster depending on the
distribution criterion. For the correct distribution of tasks in the cluster, it becomes
necessary to monitor the correctness of the distribution. Therefore, it is necessary to
consider tasks depending on their predecessors or using methods PlanT or ExecutT.
3. The number of clusters changes dynamically, and the clustering parameters are
set during clustering. There are such clustering ways:
a) federated clustering: – each first tasks distributed in a separate cluster. After
that, the parameters for clustering and the criteria for the end of clustering are assign;
b) dynamic clustering – one stage of clustering in this case includes the selection of
the task for which the cluster will be determined; assigning tasks to an existing cluster
or creating a new cluster depends on the cluster parameter.
After choosing the method of clustering, there is a need for a means of finding the
correct order of distribution of clusters on the resources of the hyperconverged
system. You can use the PlanT or ExecutT methods. In this case, clusters are ordered
once before the distribution of resources. The search for the next cluster is carried out
before each stage of resource allocation. Thus, the number and method of mutual
arrangement of the three main stages of distribution using the clustering approach can
vary. This is influenced by a specific algorithm [20].
So, we can offer a method for planning the execution of transaction packages of a
hyperconverged system based on a clustering approach. This method is ClustPlan.
Imagine a large flowchart using the method ClustPlan on Fig. 5.
4 Planning the execution of a HCS transaction package using
an ant algorithm
4.1 Direct use of the ant algorithm
Ant algorithms can be used to solve not only static, but also dynamic problems.
The basis of the behavior of ants is self-organization. This is a large number of
dynamic mechanisms. These mechanisms ensure that the system achieves a global
goal as a result of the low-level interaction of its elements. The principal feature of
this interaction is the use by the elements of the system of only local information.
This excludes any centralized management. Self-organization is the result of the
interaction of four components: randomness, multiplicity, positive feedback, negative
feedback.
In order to have an idea of the operation of the ant algorithm, we present its
simplified version on Fig. 6.
Clustering options РCS resources
Tasks 1. Number of clusters
2. Clustering options (min/max)
DS11 3. Calculation time
DS12 cl1
DS13 DS14
cl2
DS15 PlanT PlanT
DS21 cl3
DS22 DS23 DS24
cl4
DS25
Fig. 5. Large scheme of the method ClustPlan functioning
Path of ant 1
Obstacle
Ant 1
Ant 2
Food
Anthill
Path of ant 2
Fig. 6. Simplified scheme of ant algorithm
The main stages of the ant algorithm work include:
1) representation of the problem in the form of a set of components and transitions
on which ants can build solutions;
2) determination of the value of heuristic metrics "pheromones;
3) determination of the heuristic of ant behavior when constructing a solution;
4) the implementation of effective local search;
5) the choice is necessary algorithm;
6) parameter settings of the ant colony algorithm.
The determining parameters in the construction of the algorithm are:
1) the number of ants;
2) the balance between study and use;
3) combination with greedy heuristics or local search;
4) the moment when the pheromone is updated.
The efficiency of ant algorithms grows with the growth of the dimension of
computational problems. Therefore, with a large number of simultaneously executed
transactions, such algorithms will be more efficient than previously considered ones.
In our case, we consider ants to be an existing transaction task. By counting
resources of a certain type on which a task can be calculated. Pheromone is a block of
statistics. It contains the values of the criteria for completing a task. The performance
criteria include the minimum or maximum time to complete the task, resource loads,
values of the criteria for the efficiency of resource use, communication time between
resources.
At a certain time interval, a method (greedy ant) is shown. It allows you to find the
best option for allocating tasks to resources with a specific group of criteria. This
approach allows you to implement a resource search. It will be best for the
distribution of tasks depending on the selected criterion.The general scheme of the
distribution using the ant algorithm is shown in Fig. 7.
4.2 Cluster distribution with the introduction of the ant algorithm
When allocating HCS resources using clustering, it becomes possible to improve
the method of constructing a distribution plan using additional conditions. These
conditions include the resource allocation method based on the ant algorithm. This
will allow more flexibility expanding the capabilities of the clustering approach.
When upgrading, a complication of the clustering algorithm occurs. This leads to an
increase in the number of iterations when finding the optimal plan. So the time to
build it will increase. But this approach can facilitate the operation of the ant
algorithm.
In this case, a certain cluster is considered an ant. Which was formed using the
clustering parameter. The method of distribution of resources based on the ant
algorithm can be used with both vertical and horizontal clustering after their complete
improvement. But for each type of clustering, it is necessary to use different initial
parameters of attractiveness. The weathering parameters of statistical attractiveness
need to be used depending on the distribution method. The general scheme of the ant
algorithm based on the clustering approach is shown in Fig. 8.
Пакет КЗ Множина доступних ресурсів
1
3 2
4 5 6
Блок розподілу ресурсів
(БРР)
7 8 жадібний алгоритм
Блок статистики (БС) кластеризація
Значение феромона
9 Мурашиний алгоритм БС
1 Мурашиний/кластер
2 Жадібний мураха
3 ...
...
1 min час виконання
...
2 max час виконання
z
3 min навантаження
4 max навантаження
5 час комунікації
6 утилізація ресурсів
7 інше
Fig. 7. Using the “greedy ant” method
Transaction
package DS P
DS m
1
3 2
4 5 6 Psi
7 8
Statistics centralized management
9 methods
pheromone value
greedy
1
clustering
2
ant algorithms
3
ant/clustering
...
Greedy ant
...
...
z
1 min completion time
2 max completion time
3 min load
4 max load
5 communication time
6 resource utilization
7 other
Fig. 8. Allocation of resources by ant algorithm based on clustering approach
The sequence of actions when using the cluster allocation method using the ant
algorithm is shown in Fig. 9.
Clusters
PCS resources
Statistics
PCS resource
methods for determining the
sequence of transactions
- Greedy
- Ant
- Clustering
- Ant/clustering
Fig. 9. Scheme of cluster distribution method using ant algorithm
5 Discussion of results
Experiments for small amounts of information. They showed that the execution
speed of a HCS transaction package using a greedy algorithm is 22%, and that of an
ant is almost 4% longer than a cluster one. This is facilitated by the ability to quickly
group the tasks of a transaction package by the parameters considered. The ant
algorithm has lost its effectiveness due to the fact that each task is transmitted with a
large delay through communication. Complex statistics are also needed.
But the execution speed of a HCS transaction package with a large amount of data
using the cluster-ant algorithm is dominated by the greedy algorithm by almost 25%,
the ant algorithm by 13%, and the cluster algorithm by 11%. This is due to a decrease
in the complexity of the ant algorithm through the distribution of clusters rather than
tasks. That is, the time to transfer large amounts of data has been reduced. The results
of this experiment is shown in Fig. 10.
6 Conclusions
The analysis of the features of the functioning of systems with hyperconverged
architecture is carried out. The methods that were considered were based on the
greedy, clustering, and ant algorithms. Therefore, a method for planning the execution
of transactions through the sharing of these optimization algorithms is proposed. This
allowed to reduce the execution time of the optimal transaction plan in comparison
with existing methods. The experiments showed that the effectiveness of the proposed
method increases with increasing amount of information. Which is processed by a
transaction package of a hyperconverged system.
Fig. 10. HCS transaction execution time dependence on the distribution algorithm
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