Open Access. ©2017 Ilya Chernov et al., published by De Gruyter Open. This work is licensed under the Creative Commons
Attribution-NonCommercial-NoDerivs 4.0 License.
Open Eng. 2017; 7:343–351
Ilya Chernov, Natalia Nikitina*, and Evgeny Ivashko
Task Scheduling in Desktop Grids:
Received September 7, 2017; accepted November 8, 2017
Abstract: We survey the areas of Desktop Grid task
scheduling that seem to be insuciently studied so far
and are promising for eciency, reliability, and quality
of Desktop Grid computing. These topics include optimal
task grouping, “needle in a haystack” paradigm, game-
theoretical scheduling, domain-imposed approaches, spe-
cial optimization of the nal stage of the batch computa-
tion, and Enterprise Desktop Grids.
Keywords: desktop grid, task scheduling, high-
performance computing, high-throughput computing,
enterprise desktop grid
Desktop Grid is a computational paradigm representing
a distributed computing system which uses idle time
of non-dedicated geographically distributed computing
nodes connected over general-purpose network. Fast de-
velopment of the Internet, quick growth of the number
of personal computers, as well as huge increase in their
performance, have made Desktop Grid a promising high-
throughput computing tool for solving numerous scien-
tic problems. Volunteer computing is one way to organize
computation: the work is done by volunteers that join the
project via the Internet. Another approach is Enterprise
Desktop Grid which joins computers within an organiza-
Ilya Chernov: Institute of Applied Mathematical Research of the
Karelian Research Center RAS; Petrozavodsk State University,
Petrozavodsk, Russia, E-mail: IAChernov@yandex.ru
*Corresponding Author: Natalia Nikitina: Institute of Applied
Mathematical Research of the Karelian Research Center RAS,
Petrozavodsk, Russia, E-mail: email@example.com
Evgeny Ivashko: Institute of Applied Mathematical Research of
the Karelian Research Center RAS; Petrozavodsk State University,
Petrozavodsk, Russia, E-mail: firstname.lastname@example.org
The rst large volunteer computing project
SETI@home was launched in 1999, providing the basis for
development of the BOINC (Berkeley Open Infrastructure
for Network Computing) platform. Today, there are more
than 60 active BOINC projects utilizing more than 15
million computers worldwide . Desktop Grid diers
from other high-performance systems, such as Compu-
tational Grids, computing clusters, and supercomputers;
scheduling policies should be aware of huge hardware
and software heterogeneity, lack of trust, uncertainty on
availability and reliability of computing nodes, etc.
Note that, besides the conventional Desktop Grids,
special types of them are considered in literature, includ-
ing hybrid, hierarchical, peer-to-peer Desktop Grids, etc.
Here, we address all variations as Desktop Grids. However,
we focus on the classical BOINC architecture: client-server
network with independent nodes, free to join or leave.
Hierarchical, peer-to-peer and decentralized Desktop
Grids open new problems and challenges from the point of
view of scheduling. The authors of , for example, pro-
pose a decentralized distributed scheduling method with
each node serving as a computing node, scheduler, and
router; in [20, 21, 27] (to mention a few) “Desktop Grids of
Desktop Grids” are considered, where the nodes of a Desk-
top Grid are Desktop Grids with their own schedulers; in
 scheduling in peer-to-peer Desktop Grids is studied.
Also, new technologies and approaches are used to cre-
ate new types of Desktop Grids. For example, the quickly
developing blockchain technology is used to construct a
market of Desktop Grid resources based on XtremWeb-HEP
software platform (http://iex.ec/). These technologies and
new approaches, together with scheduling policies and
open problems that appear in this area, deserve a whole
Scheduling policy used in Desktop Grid signicantly
aects its characteristics, such as performance (measured
as the makespan, overall throughput, throughput of vali-
dated results, turnaround, or otherwise) and/or reliability
(in the sense of producing correct results). So, much atten-
tion has been paid to development of eective (from dif-
ferent points of view) scheduling policies. However, there
344 |I. Chernov, N. Nikitina, E. Ivashko
remain open problems that deserve more attention of re-
searchers and are still promising.
Besides performance or reliability, one can consider
the problem of reducing the cost of an answer. In par-
ticular, this can be restricted to energy eciency (e.g.,
[34, 38, 40, 51]).
The aim of our work is to highlight several open prob-
lems in the domain of scheduling in Desktop Grid. Imple-
menting the solutions of these problems promises signi-
cant performance improvement both in the common com-
putational process and in specic domain-related prob-
lems. Our analysis is based on study of more than a hun-
dred research papers on Desktop Grid scheduling and re-
lated problems, dated from 1999 to 2016. The comprehen-
sive review of these papers is presented in : we summa-
rize methods of solving various challenges of task schedul-
ing, but do not discuss open problems.
There is a number of articles devoted to overview and
study of scheduling in Desktop Grids. The BOINC website
has a list of open problems  (however, mostly technical
ones). The paper  studies BOINC from the user and de-
veloper perspectives to develop high-level suggestions to
future development of BOINC.
There is also a number of review papers devoted to
Desktop Grid scheduling and related problems. The report
 presents classications of Desktop Grids from several
points of view. Although relatively old, this work is still a
valuable attempt to classify Desktop Grids and a collection
of real systems.
The authors of  consider and compare the most
popular contemporary middleware systems for Desktop
Grid: BOINC, XtremWeb, OurGrid and HTCondor. They re-
view scientic papers devoted to research of the factors
that inuence the performance of Desktop Grid (from the
point of view of both server and the client).
The review  considers Computational Grids,
though oers much knowledge about scheduling types
and methods. Computational Grids, e-science Grids,
Service Grids are all quite dierent compared to Desktop
The chapter  in a book is a review of scheduling
algorithms for Desktop Grids and the challenges they face.
Naive algorithms that ignore the work history, knowledge-
based ones that use accumulated and apriori information,
and adaptive methods are overviewed.
The paper  addresses availability of resources: the
problem of great importance for volunteer computing.
Measurement of resource availability, revealing availabil-
ity patterns, and using them for better control of the com-
puting process are the main considered questions. Sev-
eral naive and knowledge-based algorithms are reviewed
together with node grouping methods, security threats in
volunteer computing, and safety measures.
In this paper, we focus on the open problems on
task scheduling in BOINC. Using the BOINC terminol-
ogy , by task we mean an instance of a computational
workunit. The open problems we discuss in this paper in-
clude: task grouping and scheduling parcels of tasks; task
scheduling when searching for rare answers; evaluating
schedules from the point of view of the mean cost; non-
common, domain-imposed scheduling objectives; game-
theoretic methods of task scheduling; Enterprise Desktop
Grids; “tail” phase scheduling; and generating policies by
a complex “black box” algorithm, the inner logic of which
is unavailable (including genetic search and neural net-
By its essence, BOINC is designed to support applications
that have low data transmission/compute ratio . Thus,
an important concern is providing computational tasks
of reasonable runtime. On the one hand, too long tasks
lead to waste of resources due to computational errors and
deadline violations. It is also unfavourable from the point
of view of volunteer computing . On the other hand, too
short tasks lead to high server load because of large num-
ber of requests from the clients.
Some scientic problems, like search for extrema in
high-dimensional spaces, allow to vary task runtimes
quite exibly. But in some cases, selection of the proper
task size is more complicated. One of such cases is vir-
tual drug screening, where a single estimation of ligand-
protein interaction energy typically takes from a couple of
minutes to about an hour using molecular docking soft-
ware such as AutoDock Vina [23, 42]. Task grouping tech-
nique could be used to solve this problem.
We will refer to a group of tasks wrapped together
as a parcel. A parcel is created and processed as a sin-
gle BOINC workunit. This distinguishes the parcel concept
from a batch of tasks (which is a series of workunits) and
from a group of tasks which is an arbitrarily selected por-
tion of workunits.
Task parceling can serve at least three purposes.
Firstly, parcels of small tasks take more time to solve,
and communications between nodes and the server are
fewer; so the server load is less. We witnessed a drastic
slow-down of Desktop Grid computation when many quick
tasks (less than 1 minute each) were scheduled; grouping
tasks into parcels of 1000 tasks each improved the perfor-
Task Scheduling in Desktop Grids: Open Problems |345
mance. In  the server load is minimized by choosing
optimal parcel size by game-theoretic methods.
Next, parceling is able to improve scheduling e-
ciency by using variable size of parcels depending on a
node’s performance, reliability, trust level, network con-
nection, etc. This question has been paid relatively low
attention, up to our knowledge. In  dierent heuristic
scheduling algorithms, including a few “batch” ones, are
compared. Beside selecting the best ones by simulation,
they also show that the batch mode allows more eective
Finally, parcels can contain special tasks for reveal-
ing cheaters, checking purposes, etc. Adding some tasks
of a parcel to another parcel for check purposes can be
called “fractional replication”: only part of the complex
compound task is re-solved. This can be used to ght
saboteurs, including intellectual and organized ones, to
improve reliability, or for better eciency. For example,
 studies defense against intellectual cheaters (adver-
saries) in volunteer computing networks by putting check
tasks into task parcels. In  also test tasks are added
to parcels, not only to reveal the cheaters, but to hide
valuable answers. The Earth added to a parcel of planet
descriptions for search of life, drastically increases the
amount of positive results so that stealing a positive result
would be harder. In  the same aim is considered from
another point of view: they re-solve randomly chosentasks
of a parcel to reveal lying nodes. Dierent retrieval meth-
ods for better performance are compared in : this is not
exactly task parceling, but a related approach.
3Needle in a haystack
Many problems suitable for Desktop Grid consist of tasks
that give interesting results only seldom. For example, t-
ting experimental data of hydride decomposition  by
model curves by search in a space of parameters rejects
most of the tested sets. Then errors of the rst and sec-
ond kinds, i.e., accepting a wrong answer or rejecting the
correct one, have signicantly dierent value. Indeed, a
false interesting result would be quickly revealed; also,
additional checks, even expensive, are too rare to signi-
cantly aect the metrics. However, rejecting the unique in-
teresting result makes all the project completely useless.
This can be taken into account for optimizing replication,
scheduling, task validation, etc.
Besides, the existing paradigm “one task — one an-
swer” obviously causes needless communications be-
tween the server and the clients. It seems eective to
schedule huge parcels of tasks and consider the rst good
answer as the result of the whole parcel. In other words,
a node continues to look for a good answer until either
the parcel is complete or the deadline is missed. Up to our
knowledge, nobody has studied this possibility which can
be called “looking for the needle in a haystack”. In vol-
unteer computing, some reliability-improving measures
must be taken. Heartbeat and replication techniques 
can be used for this purpose. For Enterprise Desktop Grids,
employment of the PUSH model of interaction with com-
puting nodes may improve eciency and signicantly re-
duce the server load.
4Cost of an answer
In [10, 12] we consider the special optimal replication prob-
lem for Desktop Grid: the minimized quantity is the av-
erage total cost of solving a task. Each task can produce
an answer from some given set of values (a two-element
set in case of recognition problems). Wrong answers can
be obtained with some known probabilities. To reduce the
risk of error, each task is solved independently on dier-
ent computing nodes. An answer is accepted when it is
produced the given number of times called the quorum (it
can be answer-dependent). In case a wrong answer is ac-
cepted, some penalty is added to the computational cost.
Penalties can also depend on the accepted answer and
the correct answer and are usually large compared to the
cost of a single computation. So, the cost of a task, con-
sists of solving it several times (replication) and a possible
penalty. The problem is to choose quorums for the possible
answers to minimize the expected cost. Too high quorums
almost eliminate the risk of paying a penalty but instead
increase linearly the computational cost.
Obviously, the optimal quorums need not to be the
same for dierent possible answers: some answers need
to be checked more carefully than others, depending on
their penalty and error probability. For example, if penal-
ties are signicantly dierent (so some answers are more
valuable than other), less valuable answers need to be
checked more in order to reduce the risk of missing a valu-
able answer. Another result is the stability of the optimal
quorum with respect to the penalty values: critical penal-
ties in case of reliable (i.e., with low error risk p) comput-
ing nodes grow as p−νwith respect to the quorum ν. There-
fore, exact values of penalties need not to be known pre-
cisely: rough estimations up to a few orders of magnitude
are sucient. Also the quorum for rare results often can be
chosen higher than the optimal value at a very low cost.
346 |I. Chernov, N. Nikitina, E. Ivashko
Indeed, higher quorum further reduces the risk, but in-
creases computational costs linearly; this addition occurs
only seldom: when the true answer is a rare one. So the
total increase of the average total cost is low.
5Domain-imposed criteria for task
Desktop Grids allow solving problems from various scien-
tic domains. Beside the common optimization criteria,
such as the minimal makespan, the maximal throughput,
etc., the scientic domain of the problem being solved on a
Desktop Grid can impose additional requirements and op-
timization objectives to the process of problem solution,
which partly might be addressed by proper task schedul-
One of the examples of such objectives arises from
the bioinformatics eld. During virtual drug screening,
the challenge is not only to process large libraries of
molecules, but also to select a topologically diverse set of
hit compounds. Moreover, such set is highly desirable to
obtain at early stages of the research, because the virtual
screening process might take months before the results
proceed to a laboratory .
The work  proposes a task scheduling algorithm
to address this challenge. The space of molecules is di-
vided into blocks according to the chemical properties of
molecules. The client nodes of a Desktop Grid act as in-
dependent entities and select a block where they prefer
to perform molecular docking. The purpose of each node
is to obtain as many hit molecules as possible, but topo-
logically dierent from the ones found by other nodes.
The prototype implementation and computational experi-
ments show that the proposed algorithm allows to quickly
obtain topologically diverse hits.
The example above shows that domain-imposed crite-
ria for task scheduling can signicantly increase eective-
ness of a Desktop Grid.
6Games of scheduling
Mathematical game theory is an eective approach to con-
struct optimal rules of interaction between independent
agents. In Desktop Grids, computing nodes can be consid-
ered as independent agents with dierent characteristics
(i.e. computing power, network speed, availability and re-
liability metrics, and so on).
Game theory considers optimization problems where
utility functions of two or more agents called players de-
pend on decisions made by all players . One of the aims
of the theory is to design the game rules to provide the de-
sired behaviour as the optimal one.
Applied to scheduling in Desktop Grids, game-
theoretic methods can serve several purposes. The
literature considers the following two major examples.
First, well-designed rules are able to provide optimal be-
haviour in a decentralized way; this can reduce the server
load, eliminate necessity to gather information about the
nodes, improve robustness of the schedule [15, 29, 36, 37].
Second, game-theoretic methods eciently serve to
counteract sabotage in volunteer computing Desktop
Grid [6, 22, 56]. Besides the possibility to force saboteurs
to stop their malicious activity, it is vital to understand
interaction of intelligent and cooperating adversaries.
However, the potential of game-theoretic methods seems
to be much higher.
7Enterprise Desktop Grids
Desktop Grids can be classied to volunteer computing
projects, where volunteers grant their resources and are
connected via the Internet, and Enterprise Desktop Grid
systems that join resources of an institution and exploit
local area networks. Much attention has been paid to task
scheduling for volunteer computing: it is enough to men-
tion the thesis  and a review . Enterprise Desk-
top Grids are studied much less [26, 30, 31, 47]. However,
these two types of Desktop Grids are quite dierent, also
from the point of view of scheduling. Firstly, trust is much
higher in Enterprise Desktop Grids: at least there are no
saboteurs. Reliability is also higher. Secondly, nodes’ be-
haviour is much more predictable [33, 46] and all informa-
tion about them (including their performance, amount of
memory, type of OS, installed software, etc) is available.
In particular, availability patterns (distribution of ON and
OFF periods) is more predictable. Thirdly, the structure of
the Enterprise Desktop Grid is usually known. So, quite ef-
cient task scheduling is possible with lower level of repli-
Next, Enterprise Desktop Grids are often relatively
small in size, so that methods inapplicable in the general
case can be used (e.g., in  linear optimization problem
maximizes the throughput of a heterogeneous computing
Also, Enterprise Desktop Grid can be of dierent archi-
tecture, not necessarily adhere to just the classical client-
Task Scheduling in Desktop Grids: Open Problems |347
server model. Peer-to-peer, torrent-like, multi-level com-
puting networks can be used for solving various types
of scientic problems. For example, in  a peer-to-peer
torrent-like Desktop Grid where each node needs to get
all the results is considered. One of the nodes serves as a
Finally, Enterprise Desktop Grid can exploit the PUSH
model of interaction with nodes instead of the regular
PULL model. Using the PUSH model, the server assigns a
task to a node and is able to check the status of the task
(completion progress), the status of the node (ON/OFF,
available resources, availability periods, etc.); to cancel
the task when the needed number of results is achieved.
All this makes Enterprise Desktop Grids much closer to
Computational Grid than to volunteer computing. But En-
terprise Desktop Grids also keep many of the intrinsic char-
acteristics of regular Desktop Grids.
Enterprise Desktop Grids allow to solve local scien-
tic problems (e.g., data analysis) within an institution.
However, their ecient usage needs new mathematical
models and algorithms of task scheduling. The open prob-
lems of task scheduling in Enterprise Desktop Grids in-
clude both optimal replication, availability/reliability rat-
ings, etc. (more important for volunteer computing) and
special problems like “tail” computation or task grouping.
Another interesting problem is optimization of the “tail”
computation. A distributed computational experiment in-
volving a batch of tasks inherently consists of two stages
(, see Fig. 1). At the rst one, the throughput stage, the
number of tasks is greater than the number of comput-
ing nodes (in the very beginning it can be much more). At
this stage, the computational power is limiting the perfor-
mance, so it is reasonable to supply each node with a task.
With time, the number of unprocessed tasks decreases un-
til it is equal to the number of computing nodes: then the
second stage called the tail starts. At this stage, there is ex-
cess of computational power. In particular, during the rst
stage, the replication can be useless (from the point of view
of the makespan) as it was shown in . The same is true
for parallel multicore implementations of the used algo-
rithms. Tail computation is optimized in [8, 31].
A number of research problems related to specics of
Desktop Grids are connected to the two-staged batch com-
The rst problem is time estimation of the rst stage
completion. It is important for planning of the computa-
Figure 1: Two stages of batch completion in a Desktop Grid.
tional experiment, Desktop Grid maintenance, and keep-
ing interest to the project among volunteers. This prob-
lem is complicated due to the dynamic nature of Desktop
Grids: computational nodes can leave or join the compu-
tational network; high heterogeneity of computing nodes:
computing performance of new or leaving nodes can sig-
nicantly dier from the mean node performance; a com-
puting network performance can have a trend (because of
growth or lose an interest to the project among volunteers)
or even ashes (as a result of “competitions” among volun-
Another problem is the fastest experiment or batch
completion. In practice, “tail” computation takes a long
time (usually, about two or three times greater than dead-
line) because of missing deadlines. A computational net-
work does not have information of the current status of
tasks computation. So, the “tail” accumulates a lot of
nodes that have abandoned the computing network. As a
certain task assigned to such a node violates the deadline,
it is assigned again to a dierent node, possibly also going
to leave the network soon. So, this prolongs the “tail”.
A solution to the problem is in the redundant com-
puting: currently processing tasks are assigned to vacant
computing nodes. This strategy signicantly increases the
chances that at least one copy is solved in time. Employ-
ing this strategy, one should to take into account char-
acteristics of computational nodes (availability, reliabil-
ity, computing power), processing the same task; accumu-
lated task processing time; expected task completion time;
and so on.
An approach for reducing the total batch completion
time by about 14% and the “tail” phase, in particular, by
about 40% is proposed in : the nodes and the tasks are
grouped by their dynamically evaluated performance and
348 |I. Chernov, N. Nikitina, E. Ivashko
Three strategies of task replication with performance
of the nodes taken into account are experimentally tested
in : idle resources need to be used to decrease the
The fastest batch completion problem is even more
complicated if a new tasks batch should be started after the
current batch completion. In this case redundant comput-
ing reduces accessible computing power. The more com-
plicated case is connected to inter-dependency between
the tasks of the new batch and completion of certain tasks
of the current batch.
9Black box schedule generators
Attention to articial intelligence (machine learning, neu-
ral networks) methods has been growing recently. They
can be used for producing enhanced schedules. For exam-
ple, in  neural networks forecast the load in order to
schedule jobs to volunteer resources. The authors of 
also apply neural networks to evaluate the load. Such sys-
tems have proved to be able to solve complex tasks of esti-
mating fuzzy concepts like reliability, availability patterns,
task complexity, etc.
Another approach is the genetic algorithms. Applied
to scheduling (e.g., [18, 19, 41, 55]), they simulate the Dar-
winian evolution in a population of schedules to produce
the best ones. Some authors use genetic algorithms to de-
velop optimal scheduling policies.
Scheduling in Desktop Grids is generally a multi-criteria
optimization problem. The demands can be classied to:
– Performance or eciency, measured as makespan,
throughput, turnaround time, total time for the bath
of tasks, overhead time, etc; early production of valu-
able results also belongs here.
– Reliability or trust, meaning low risk of getting a
wrong result or high chances to get any result, includ-
ing counter-sabotage measures.
– Justice, important mostly in volunteer computing,
meaning taking into account both the desires of
volunteers and the project goals (possibly, multiple
Obviously, the criteria often contradict each other. Even
criteria from the same class can be mutually exclusive;
e.g., task replication can be used to get an answer sooner
(performance) but on the cost of redundant use of re-
sources. Also the criteria are quite hard to formalize.
Even makespan (the time to complete a set of tasks) and
throughput (tasks per hour) are not exactly reciprocals.
Black box algorithms, including machine learning, ge-
netic search, and other, are able to solve such “fuzzy”
problems. They are potentially able to improve eciency,
reliability, and justice, in any combination. The same is
true for multi-agent models (game-theoretic models): rst,
decentralization denitely improves scalability, reduces
the server load, forces the nodes to work better and thus
is able to optimize eciency. Also, well-chosen game rules
can stop nodes from lying or poorly solve tasks, so that re-
liability is better (including an important question of stop-
ping sabotage). Finally, game-style interaction of indepen-
dent agents helps justice; e.g., nodes can distribute tasks
of a few projects in a fair way.
As for the task grouping, it is generally promising for
better eciency, though, as we have noted, can be used
for cross-checking. Creating parcels of variable size can be
useful for fair use of computing power of the nodes.
Other problems that we discuss in the paper are more
specic. Tail-stage optimization is mostly from the point of
view of eciency. Also, when serving multiple projects in
a queue, resource management at the nal stage becomes
Enterprise Desktop Grids are free of some volun-
teer computing problems, as has been discussed already.
Therefore, the main criteria are those of the rst class,
i.e., eciency. However, relatively low computing power
in case of a few projects can be a challenge from the point
of view of fair scheduling of the available resources. Also
interests of computer users, project owners, and other con-
cerned parties may not coincide. So, the fair distribution of
the available resources without violating anybody’s rights
can be a challenging problem.
Domain-specic optimization or optimal choice of the
order of the search is mostly for eciency in the special
sense of getting results sooner. However, it is quite a new
subject, so new applications can appear. Also (at the mo-
ment) optimizing the cost of the computing seems to serve
only reliability. Though, we are sure that this approach can
be useful also for the eciency, including fewer deadline
violations (if one is punished by a virtual penalty increas-
ing the cost). Possibility to join the cost metrics with game-
theoretic approach is (up to our knowledge) completely
As for the “needle in a haystack” search, it is proposed
for less server load and thus better eciency; however,
rare valuable answers are expensive, so this becomes a
question of reliability.
Task Scheduling in Desktop Grids: Open Problems |349
Table 1: Eect of the discussed subjects.
A problem Performance Reliability Justice
Task grouping 3 3 3
Needle in a haystack 3 3
Task cost ? 3?
Domain-specics 3? ?
Game theory 3 3 3
EDG 3 3
Tail stage 3 3
Black box 3 3 3
So, most open problems selected by us serve at least
two optimization purposes (see Table 1), which agrees
well with a nature of heterogeneous computing systems
where multiple conicting interests meet and need to be
Task scheduling plays crucial role in Desktop Grids. Much
eorts were taken to develop new mathematical models
and algorithms. With some prerequisites they promise sig-
nicant increase of computing performance. But there are
also open problems in this domain.
We have considered several promising directions to
improve task scheduling in Desktop Grids. They in-
clude task grouping (parceling), “needle in a haystack”,
mean cost evaluation, domain-imposed scheduling objec-
tives, game-theoretical methods of robust or decentralized
scheduling, Enterprise Desktop Grids, optimizing the nal
part of the project, and sophisticated algorithms.
Solution of each of the described problems promises
higher eciency of Desktop Grids. Optimal task group-
ing reduces the server load, increasing the number of si-
multaneously served computing nodes and thus the Desk-
top Grid performance. The same is true for “needle in
a haystack” problems, using some game-theoretical so-
lutions, and the mean-cost approach. Domain-imposed
criteria, mathematical models, and algorithms promise
faster solutions of higher quality. Games of scheduling po-
tentially give more eective protocols of interaction with
computing nodes and more eective tasks distribution.
Optimizing the “tail” computation reduces makespan. AI
schedule generators are promising in producing ecient
schedules that suit best for the given environment and take
into account all available informtion. Finally, Enterprise
Desktop Grids are the special type of computing systems
not widely propagated. We believe that the described prob-
lems are hot topics of task scheduling in Desktop Grids.
Acknowledgement: The authors are grateful to the anony-
mous reviewers for their useful comments that helped to
improve the paper.
The work is supported by the Russian Foundation for
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