The article discusses the numerical-analytical and simulation models of a high-load monitoring system. The examples of modeling are presented and the technical problem of choosing the hardware configuration of the simulated monitoring system is solved, which makes it possible to reduce the time spent by the task in the system by more than 2 times.
Automated monitoring systems are an
important part of any information system.
Monitoring is a continuous process of
observing and registering object parameters,
processing them, and comparing them with
threshold values. This monitoring system must
cope with the increasing workload. Zabbix is
the most popular and easily scalable for load
adaptation free monitoring system for
information systems [
Most often, the authors consider the
stationary mode of operation of such systems
in the context of queuing systems, however, it
is the non-stationary mode of operation that is
of greatest interest. The current state of the
issue of non-stationary queuing systems is
considered in more detail in [
In [
CEUR Workshop Proceedings (CEUR-WS.org)
monitoring system under various loads. The
proposed model uses an improved recursive
algorithm for generating a list of system states
and a matrix of coefficients of an ODE system
without constructing a graph of states and
transitions of the non-stationary queueing
system and deriving the general equation of
the ODE system as in [
As it already known, the monitoring system under consideration allows you to distribute the load and scale it through the use of proxy servers. Each proxy server collects data from a separate set of devices, and then sends the data to the main server that processes it.
Figure 1 shows a general diagram of the interaction of system components. Such a system can also be considered as a queuing system. The queuing system representation is shown in Figure 2. distribution at time moment 0, as we can see, this is the initial state of the system and its probability is equal to one, while the probabilities of other states are equal to zero.
As can be seen from Figure 5, the probability distribution of states at time moment 5 allows us to say that at this time moment the final states have the highest probabilities, which indicate that new requests can arrive in the system with the least probability, and those that have already arrived will be processed with a higher probability.
Figure 6 allows us to say that at time moment 18 the absorbing state has the highest probability, while the probability of the system being in other states is negligible. shifted from 18 to 16, which, as expected, indicates a slightly higher throughput of such a system compared to the system in which there are 2 proxies.
Simulation modeling was carried out using
the software package [
Additionally, the model allows tracing the full path through the system state graph.
Consider a model with the following initial inputs: 1. Amount of proxy servers – 2; 2. Amount of incoming tasks – 1000; 3. Intensity of tasks arriving – 3; 4. Intensity of processing tasks for proxy – 1; 5. Intensity of processing tasks for main server – 2.
The arrival intensity of tasks higher than the service rates allows one to simulate the non-stationary operation of the system.
The path of such a model through the state graph was 3001 states. Figure 8 shows the number of tasks in queues to proxy servers and to the main server, as well as the number of tasks they have already served. Values are listed for each condition passed by the system. As we can see, the queue to proxy servers is constantly increasing until all requests has arrive to the system, after which the queue to proxy begins to decreasing rapidly. At the same time, the queue to the main server and the gap in the number of requests served by proxy servers and the main server is minimal.
In Figure 9, we can see that the residence time of each new request in the system increases up to 200 conventional units of time, which is obviously due to the low throughput of proxy servers. task in the system is still close to 200. From this we can conclude that the growth of the queue to the proxy in this configuration is insignificant, but it is impossible to reduce their number or performance, since this will reduce the queue to the main server, but increase the queue to the proxy. Thus, the changes are leveled and the total time spent by the order in the system will not change.
Let's simulate the behavior of the system once again with an increase in the intensity of processing requests on the main server to 2.5.
As it can be seen from Figure 12, queues accumulate almost evenly and are small.
From Figure 13, we can see that the time in the queue to the proxy and to the main server is approximately the same, and the maximum time for a request to be in the system is about 80 conventional units of time, which is more than 2 times less than the values obtained in the simulation with the initial conditions.
So, the simulation model allows solving the technical problem of determining the minimum required hardware configuration of the system to service a finite number of tasks with a certain level of service - the time the task is in the system.
The article discusses the numericalanalytical and simulation models of the operation of a high-load distributed monitoring system for a data transmission network in a non-stationary mode. These models make it possible to determine the probabilistic characteristics of the system's behavior, as well as to determine the necessary hardware configuration to maintain the required service level of tasks.
Further development of the topic can be adding priorities to requests and adding the transfer of packages of requests from the proxy to the main server.
Research support. Research carried out on this topic was carried out within the framework of the budget topic No. 0073–2019–0004 queueing tasks in a non-stationary acyclic queueing network with a finite number of tasks. The Certificate on Official Registration of the Computer Program. No. 2020663070, 2020.