=Paper= {{Paper |id=Vol-2638/paper10 |storemode=property |title=Predicting runtime of computational jobs in distributed computing environment |pdfUrl=https://ceur-ws.org/Vol-2638/paper10.pdf |volume=Vol-2638 |authors=Alexander Feoktistov,Olga Basharina |dblpUrl=https://dblp.org/rec/conf/iccs-de/FeoktistovB20 }} ==Predicting runtime of computational jobs in distributed computing environment== https://ceur-ws.org/Vol-2638/paper10.pdf

Predicting runtime of computational jobs in distributed
computing environment

A G Feoktistov1 and O Yu Basharina2
1
Matrosov Institute for System Dynamics and Control Theory of SB RAS,
Lermontov St. 134, Irkutsk, Russia, 664033
2
Irkutsk State University, Karl Marx St. 1, Irkutsk, Russia, 664003

agf@icc.ru

Abstract. The paper addresses a relevant problem of predicting the runtime of jobs for
executing problem-solving schemes of large-scale applications in a heterogeneous
distributed computing environment. Such an environment includes nodes that have
various hardware architectures, different system software, and diverse computational
possibilities. We believe that increasing the accuracy in predicting the runtime of jobs
can significantly improve the efficiency of problem-solving and rational use of
resources in the heterogeneous environment. To this end, we propose new models that
make it possible to take into account various estimations of the module runtime for all
modules included in the problem-solving scheme. These models were developed using
the special computational model of distributed applied software packages (large-scale
scientific applications). In addition, we compare the prediction results (jobs runtime
and their errors) using different estimations. Among them are the estimations obtained
through the modules testing, user's estimations, and estimations based on
computational history. These results were obtained in continuous integration, delivery,
and deployment of applied and system software of a package for solving warehouse
logistics problems. They show that the largest accuracy is achieved by the
modules testing.

1. Introduction
Todays, scientific applications focus on carrying out large-scale scientific experiments in a
heterogeneous distributed computing environment. They play a significant role in the process of
solving important practical problems based on mathematical modeling of complex systems under
study [1]. Often, such applications are implemented as distributed applied software packages. The
environment heterogeneity means that its nodes (PCs, compute servers, HPC-clusters, and cloud
resources) have various hardware architectures, different system software, and diverse computational
possibilities. Various local resource managers (LRMs) are hosted in nodes of the environment.
In distributed applied software packages, problem-solving is described by schemes that specify the
computing process in terms of the subject domain. We apply methods of the computation and
information planning in constructing such problem-solving schemes on the special computational
model [2]. Wherein, this model is a special case of the semantic network.
To execute a problem-solving scheme in the heterogeneous distributed computing environment, a
computational job is generated. A job enters into the environment. The meta-scheduler selects an
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0).
environment node suitable for this job. It then submits the job to LRM located on the node. The job
falls into the LRM's queue. When the resources of the node are freed, the job is launched.
An improvement in the efficiency of problem-solving and rational use of resources depends a lot
on the ability to estimate jobs runtime. In this regard, we propose new models for predicting jobs
runtime in the environment. Unlike well-known similar models [3-5], the proposed models make it
possible to take into account various estimations of the module runtime for all modules included in the
problem-solving scheme. Among them estimates that are obtained on the basis of the methodology
proposed by the authors.
This methodology allows us to test the program runtime. It is also used in the process of continuous
integration of applied software [6].
The rest of the paper is structured as follows. In Section 2, we briefly review related works on the
problem under study. Section 3 provides the models for predicting the jobs runtime. An example of
applying the proposed models is considered in Section 4. Section 5 concludes the paper.

2. Related work
When starting jobs in a heterogeneous distributed computing environment, it is necessary to solve the
following two problems:
 Forming a rational configuration of heterogeneous resources of the environment,
 Planning a suboptimal schedule of the job execution on the formed configuration of resources.
It's obvious that a qualitative solution to these problems for a large spectrum of practical scientific
applications requires an estimation of the execution time of applied programs [7]. For example, such
an estimation is used to cluster jobs in the allocation of resources to them [8]. Generally, job runtime
estimates are implemented by the user or some runtime prediction procedure [9].
Most of traditional job management systems and many workflow management systems are based
on the use of estimates for program execution time that are specified by the user. This is a simple and
very flexible approach. However, the errors of such estimates are usually highly large in practice.
There are various methods for predicting program execution time [10]. Among them are static and
dynamic methods of program analysis.
The use of methods and tools of static analysis of program code without real program execution in
heterogeneous environments is characterized by high overheads to additional programming. Such
overheads are owing to the need to simulate operating the processor of the target computing node for
executing a large spectrum of programs written in various programming languages.
In practice, the method of frequency characteristics has proven itself well [11]. It is based on the
use of special tools for dynamic analysis of programs [12, 13]. Algorithms based on the use of such
analysis differ in the sets of studied software and hardware characteristics [14]. Each algorithm
determines specific relations between these characteristics. In this regard, the method of profiling the
program on some reference node is most applicable in practice [15]. Its results are then extrapolated
relative to the characteristics of the target node. Within this method, the accuracy of estimating the
program execution time largely depends on the selection of scaling coefficients for computation
speedup. These coefficients are determined by the ratios of characteristics for the reference and
target nodes.
However, our practical experience in applying continuous integration of applied software
represented in [6] allows us to draw the following conclusion. If the target nodes are available, then
testing the programs in them can simplify the program runtime prediction in comparison with the
dynamic analysis.

3. Models for Predicting Computational Jobs Runtime
The computational model of the environment is determined by the structure
𝕄 =< 𝐴, 𝑍, 𝐹, 𝑀, 𝑆, 𝐽, 𝑅, 𝑄, 𝑂, 𝑀𝐻 >,
where 𝐴, 𝑍, 𝐹, 𝑀, 𝑆, 𝐽, 𝑅, and 𝑄 are, respectively, the sets of applied software packages, parameters,
operations, program modules, problem-solving schemes, jobs, resources, and constraints to the job
execution and resources use. 𝑂 is the set of relations between the above-listed objects. The data
structure 𝑀𝐻 represents the computational history that reflects the functioning of modules from 𝑀. If
necessary, a description of components of the model 𝕄 can be found in details in [2].
Operations from 𝐹 determine computational actions on the set 𝑍 of parameters. Parameters can be
represented by scalars, vectors, and matrices of various basic data types or arbitrary data structures.
Each operation 𝑓𝑖 ∈ 𝐹 is implemented by the module 𝑚𝑗 ∈ 𝑀, where 𝑖 ∈ ̅̅̅̅̅̅ ̅̅̅̅̅̅̅
1, 𝑛𝑓 , 𝑗 ∈ 1, 𝑛𝑚 , 𝑛𝑓 is the
number of operations, and 𝑛𝑚 is the number of modules. One module can implement several
operations. Each operation 𝑓𝑖 has two subsets 𝑍𝑖𝑖𝑛 , 𝑍𝑖𝑜𝑢𝑡 ⊂ 𝑍 of parameters. The subset 𝑍𝑖𝑖𝑛 consists of
input parameters. Their values must be known in order to calculate values of parameters from 𝑍𝑖𝑜𝑢𝑡 .
Parameters of the subsets 𝑍𝑖𝑖𝑛 and 𝑍𝑖𝑜𝑢𝑡 reflect the purpose and semantics of formal parameters of the
module 𝑚𝑗 that implements the operation 𝑓𝑖 . Parameters are transferred between modules in the form
of data files.
Schemes from 𝑆 represent problems-solving processes in packages. The scheme 𝑠 ∈ 𝑆 that
performs operations from 𝐹 is an analogue of the tiered-parallel form of the algorithm graph. Within
the computational model under consideration, a scheme is a connected subgraph of an oriented acyclic
bipartite graph. Such a graph represents schematic knowledge about algorithms for studying a subject
domain. The example of such a graph is represented in Figure 1. Operations and parameters are
depicted on it by filled and unfilled circles. The problem-solving scheme represented by the graph
includes 3 tiers. Tier 1 contains the operation 𝑓3. The second consists of the operations 𝑓4 , 𝑓5, and 𝑓6.
Finally, two operations 𝑓7 and 𝑓8 are placed on tier 3.

Tier 1 Tier 2 Tier 3

z2 f4 z5 f7 z7

z1 f3 z3 f5 z6 f8 z8

z4 f6 z9

Figure 1. Bipartite directed graph of the problem-solving scheme.

Two special operations 𝑓1 and 𝑓2 are emphasized in the computational model. This allows us to
maintain a commonality for computations planning when constructing problem-solving schemes using
their formulations [16]. The operations 𝑓1 and 𝑓2 model the conditions of the problem. They
respectively define a subset of parameters whose values are known, and a subset of the parameters
whose values need to be calculated. Thus, the operation 𝑓1 defines the subsets 𝑍1𝑖𝑛 = ∅ and 𝑍1𝑜𝑢𝑡 ≠ ∅.
The operation 𝑓2 determines the subsets 𝑍2𝑖𝑛 ≠ ∅ and 𝑍1𝑜𝑢𝑡 = ∅. In the above example, 𝑍1𝑖𝑛 = ∅ and
𝑍1𝑜𝑢𝑡 = {𝑧1 } (Figure 2). At the same time, 𝑍2𝑖𝑛 = {𝑧7 , 𝑧8 , 𝑧9 } and 𝑍2𝑜𝑢𝑡 = ∅.
The execution of a problem-solving scheme in the environment is specified by a computational job.
A job includes a list of modules of a problem-solving scheme. In addition, it contains requirements to
the environment that determine the computing resources needed to execute the listed modules. These
requirements include the number of processors or cores, sizes of RAM and disk memory, execution
time, etc.
z7

f1 z1 ... z8 f2

z9
Figure 2. The operations 𝑓1 and 𝑓2.

We propose new models for predicting the job runtime in the heterogeneous environment. In these
models, we consider cases of job execution in modes of data readiness and fork/join, resource
virtualization, and job restarts.
Let the matrix 𝐏 of dimension 𝑘 × 𝑘 represent information on the precedence of 𝑘 operations of the
problem-solving scheme. The element 𝑝𝑖𝑗 = 1 (𝑝𝑖𝑗 = 0) of the matrix 𝐏 means that the operation 𝑓𝑗 in
the problem-solving scheme precedes (does not precede) the operation 𝑓𝑖 and 𝑍𝑖𝑖𝑛 ∩ 𝑍𝑗𝑜𝑢𝑡 ≠ ∅ (𝑍𝑖𝑖𝑛 ∩
𝑍𝑗𝑜𝑢𝑡 = ∅). The matrix 𝐃 of dimension 𝑘 × 𝑘 reflects estimates of the data volumes transmitted
between operations. The matrix element 𝑑𝑖𝑗 ≥ 0 shows the amount of data transmitted by the
operation 𝑓𝑖 to the operation 𝑓𝑗 . The matrix 𝐖 of dimension 𝑘 × 𝑘 provides information about the
interconnect bandwidth between nodes where modules that implement scheme operations are
launched. The matrix element 𝑤𝑖𝑗 ≥ 0 demonstrates the interconnect bandwidth between nodes, in
which the modules implementing the operations 𝑓𝑖 and 𝑓𝑗 operate.
The estimate 𝐸𝑠 of the job runtime in the asynchronous mode based on data availability is defined
as follows:
𝑞 𝑑
𝐸𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖 + max (𝑒𝑗𝜏 + 𝑢), 𝑢 = 𝑐 (𝑡)𝑤 .
𝑗𝑖
(1)
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘 𝑗𝑖 𝑗𝑖

The variables in the formulas (1) are interpreted as follows:
 𝑒𝑖𝜏 (𝑒𝑗𝜏 ) is the estimate of the period 𝜏 elapsed from the beginning of the job execution to the
completion of the operation 𝑓𝑖 (𝑓𝑗 ),
𝑞
 𝑒𝑖 is the estimate of the wait time in a queue for the module that implements the operation 𝑓𝑖
(at a time when all the necessary data is ready to execute it),
 𝑒𝑖 is the predictive estimate of the module runtime,
 𝑘 is the number of scheme operations,
 0 < 𝑐𝑗𝑖 (𝑡) ≤ 1 is the coefficient of decrease in interconnect bandwidth between nodes, in
which the modules implementing the operations 𝑓𝑖 and 𝑓𝑗 operate, at the time 𝑡.
Let the matrix 𝐒 of dimension 𝑚 × 𝑘 be a tiered-parallel form describing the scheme execution in
the fork/join mode, and 𝑚 is the number of scheme tiers. The element 𝑠𝑙𝑖 = 1 means that the operation
𝑓𝑖 must be performed on the lth tier. The transition to operations of the (l+1)th tier is possible provided
that all operations on the lth tier are completed. The estimate 𝐸𝑠′ of the job runtime in the fork/join
mode is defined as follows:
𝑞 𝑑𝑗𝑖
𝐸𝑠′ = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖 + 𝑣), 𝑣 = ∑∀𝑗∈1,𝑘
̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
𝑐 (𝑡)𝑤
. (2)
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘 𝑗𝑖 𝑗𝑖

We use formulas (1) and (2) to define other estimates. Among them the estimates of the job
runtime with virtualized resources, module restarts, user estimates of the module runtime, and
estimates adjusted based on the computational history.
In the environment with virtualized resources, the estimate 𝐸𝑣𝑠 of the job runtime in the
asynchronous mode is defined as follows:
𝑞
𝐸𝑣𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑟 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

The variables 𝑒𝑖𝑣𝑚𝑙 and 𝑒𝑖𝑣𝑚𝑟 are, respectively, estimates of the time it takes to launch and remove
virtual machine with a module that implements the operation 𝑓𝑖 . The estimates 𝑒𝑖𝑣𝑚𝑙 and 𝑒𝑖𝑣𝑚𝑟 are
determined experimentally for virtual machines of various configurations.
′
For the same environment, the estimate 𝐸𝑣𝑠 of the job runtime in the fork/join mode is defined as
follows:
𝑞
′
𝐸𝑣𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑟 + 𝑣).
̅̅̅̅:𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

In the asynchronous mode with restarting modules, the estimate 𝐸𝑟𝑠 of the job runtime is defined as
follows:
𝑞 𝑓
𝐸𝑟𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖𝑟𝑢𝑛 + 𝑒𝑖𝑟𝑒𝑠 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

The variables in the formula above are interpreted as follows:
𝑓
 𝑒𝑖 is the estimate of the time it takes for the detection and identification of a failure,
 𝑒𝑖𝑟𝑒𝑠 is the estimate of the time it takes for the restart of a module that implements operation
𝑓𝑖 ,
 𝑒𝑖𝑟𝑢𝑛 is the module runtime to failure.
𝑓
These variables are required in the case of a hardware-software failure. The estimates 𝑒𝑖 and 𝑒𝑖𝑟𝑒𝑠
are determined by the average execution time of such processes by a meta-monitoring system for
different types of software and hardware failures.
′
At the same time, in the fork/join mode with restarting modules, the estimate 𝐸𝑟𝑠 of the job runtime
is defined as follows:
𝑞 𝑓
′
𝐸𝑟𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖𝑟𝑢𝑛 + 𝑒𝑖𝑟𝑒𝑠 + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

Let us now look at a case for applying user's estimates. In the asynchronous mode, the estimate 𝐸𝑢𝑠
of the job runtime is defined as follows:
𝑞
𝐸𝑢𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖′ + max (𝑒𝑗𝜏 + 𝑢),
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

where 𝑒𝑖′ is the user’s estimate of the module runtime.
′
The estimate 𝐸𝑢𝑠 of the job runtime in the fork/join mode is defined as follows:
𝑞
′
𝐸𝑢𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖′ + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

In the environment with virtualized resources, the estimate 𝐸𝑢𝑣𝑠 of the job runtime in the
asynchronous mode is defined as follows:
𝑞
𝐸𝑢𝑣𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑒𝑖′ + 𝑒𝑖𝑣𝑚𝑟 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘
′
For the same environment, the estimate 𝐸𝑢𝑣𝑠 of the job runtime in the fork/join mode is defined as
follows:
𝑞
′
𝐸𝑢𝑣𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑒𝑖′ + 𝑒𝑖𝑣𝑚𝑟 + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

In the asynchronous mode with restarting the modules, the estimate 𝐸𝑢𝑟𝑠 of the job runtime is
defined as follows:
𝑞 𝑓
𝐸𝑢𝑟𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖′ + 𝑒𝑖𝑟𝑒𝑠 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘
′
Meanwhile, the estimate 𝐸𝑢𝑟𝑠 of the job runtime in the fork/join mode is defined as follows:
𝑞 𝑓
′
𝐸𝑢𝑟𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖 + 𝑒𝑖′ + 𝑒𝑖𝑟𝑒𝑠 + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

Finally, let us consider the use of estimates based on computational history. In the asynchronous
mode, the estimate 𝐸𝑢𝑠𝑠 of the job runtime is defined as follows:
𝑞
𝐸𝑢𝑠𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑏𝑖 𝑒𝑖′′ + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

The variable 𝑒𝑖′′ is the estimate adjusted based on the computational history. Its correction
coefficient 𝑏𝑖 = ℎ(𝑖, 𝑀𝐻, 𝑇ℎ ) > 0 is calculated on the basis of the computational history. The function
ℎ(𝑖, 𝑀𝐻, 𝑇ℎ ) calculates 𝑏𝑖 using the average or median values from the sample time of the execution
of the module that implements the operation 𝑓𝑖 for the period 𝑇ℎ .
′
The estimate 𝐸𝑢𝑠𝑠 of the job runtime in the fork/join mode is defined as follows:
𝑞
′
𝐸𝑢𝑠𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑏𝑖 𝑒𝑖′′ + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

In the environment with virtualized resources, the estimate 𝐸𝑢𝑠𝑣𝑠 of the job runtime in the
asynchronous mode is defined as follows:
𝑞
𝐸𝑢𝑠𝑣𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑏𝑖 𝑒𝑖′′ + 𝑒𝑖𝑣𝑚𝑟 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

′
For the same environment, the estimate 𝐸𝑢𝑠𝑣𝑠 of the job runtime in the fork/join mode is defined as
follows:
𝑞
′
𝐸𝑢𝑠𝑣𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖𝑣𝑚𝑙 + 𝑏𝑖 𝑒𝑖′′ + 𝑒𝑖𝑣𝑚𝑟 + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

In the asynchronous mode with restarting modules, the estimate 𝐸𝑢𝑠𝑟𝑠 of the job runtime is defined
as follows:
𝑞 𝑓
𝐸𝑢𝑠𝑟𝑠 = max 𝑒𝑖𝜏 , 𝑒𝑖𝜏 = 𝑒𝑖 + 𝑒𝑖 + 𝑏𝑖 𝑒𝑖′′ + 𝑒𝑖𝑟𝑢𝑛 + 𝑒𝑖𝑟𝑒𝑠 + max (𝑒𝑗𝜏 + 𝑢).
̅̅̅̅
𝑖=1,𝑘 ̅̅̅̅ :𝑝𝑖𝑗 =1,𝑖≠𝑗
∀𝑗∈1,𝑘

′
At the same time, the estimate 𝐸𝑢𝑠𝑟𝑠 of the job runtime in the fork/join mode is defined as follows:
𝑞 𝑓
′
𝐸𝑢𝑠𝑟𝑠 = ∑𝑚 𝜏 𝜏
𝑙=1 𝑒𝑙 , 𝑒𝑙 = max (𝑒𝑖 + 𝑒𝑖 + 𝑏𝑖 𝑒𝑖′′ + 𝑒𝑖𝑟𝑢𝑛 + 𝑒𝑖𝑟𝑒𝑠 + 𝑣).
̅̅̅̅ :𝑠𝑙𝑖 =1
∀𝑖∈1,𝑘

Estimates of the job runtime for virtual machines can be adapted to the container application case.

4. Example
As an example, we consider the problem of improving the processes of loading and unloading of
goods in a warehouse through their simulation. To solve this problem, a distributed applied software
package has been developed using the Orlando Tools framework [17]. This package is a parameter
sweep application [18]. Simulations models (modules) are created using a special toolkit [19].
The heterogeneous distributed computing environment, in which this package is applied, was
created on the basis of the resources of the public access Irkutsk Supercomputer Center SB RAS [20].
We compare the prediction results (jobs runtime and their errors) using different estimations.
The experiments were carried out in the continuous integration process of package modules. Such
integration includes the modification, version control, build, testing, delivery, and deployment of
applied and system software on different nodes of the heterogeneous environment. The experiments
were carried out on two different clusters: PC-cluster and HPC-cluster. The characteristics of both
clusters are provided in [17]. Figure 3 and Figure 4 show the actual and predicted jobs runtime on the
PC-cluster and HPC-cluster, respectively. The jobs runtime are shown for different data size that is
determined by the number of problem parameter variants. This time was predicted using the three
methods discussed above. Obviously, the most accurate prediction in both cases was formed on the
basis of the modules testing.

350000
Actual job runtime
280000 Predicted job runtime based on the
Job runtime, s

module testing
210000 Predicted job runtime based on the
computational history
140000 Predicted job runtime based by users

70000

0
31.73 61.57 121.98 246.73
Data size, MB
Figure 3. Predictive and actual results for PC-cluster.
8000
Actual job runtime
7000
6000 Predicted job runtime based on the
Job runtime, s

module testing
5000 Predicted job runtime based on the
4000 computational history
Predicted job runtime based by users
3000
2000
1000
0
31.73 61.57 121.98 246.73
Data size, MB
Figure 4. Predictive and actual results for HPC-cluster.

Figure 5 and Figure 6 demonstrate the runtime prediction errors obtained through the various
methods for estimating the module runtime. The errors are shown in percentages relative to the actual
job runtime on the PC-cluster and HPC-cluster, respectively.

300 Error of the prediction based on the modules testing
Error of the prediction based on the user's estimation
250 Error of the prediction based on the computational history
200
Error, %

150
100
50
0
31.73 61.57 121.98 246.73
Data size, MB

Figure 5. Runtime prediction error for the PC-cluster.
250 Error of the prediction based on the modules testing
Error of the prediction based on the user's estimation
200 Error of the prediction based on the computational history

Error, %
150

100

0
31.73 61.57 121.98 246.73
Data size, MB
Figure 6. Runtime prediction error for the HPC-cluster.

These results show that the error in the job runtime predicted based on the module testing decreases
with increasing the data size in the both cases. In the example, it does not exceed 10%. At the same
time, a change of the runtime prediction errors obtained owing to user’s estimates or computational
history can be non-monotonous. In practice, the job runtime predicted based on a user’s estimates is
usually overstated. The use of computational history can slightly reduce such errors.

5. Conclusions
The rational allocation of resources in solving large problems in a heterogeneous distributed
computing environment depends on the effectiveness of job maintenance schedules planned by LRMs.
Usually, these schedules are based on estimates of program execution time.
In the paper, we propose new models for predicting the job runtime using a variety of estimates.
Such job specifies the execution of a problem-solving scheme of a distributed applied software
package (large-scale scientific application) in the environment. Within these models, we take into
account job execution in modes of data readiness and fork/join, resource virtualization, and job
restarts.
The practical significance of the study lies in improving the quality of planning computations and
resource allocation in heterogeneous environments. Such an improvement is due to reducing the error
in estimating the job runtime through the application of the proposed models.

Acknowledgments
The study is supported by the Russian Foundation of Basic Research, project no. 19-07-00097. The
development of the heterogeneous distributed computing environment was supported in part by the
Basic Research Program of SB RAS, project no. IV.38.1.1.

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