https://orcid.org/0000-0002-8675-4115
Figures
Abstract
This research dealt with the problem of scheduling applied to the supercomputer’s execution. The goal is to develop an appreciated algorithm that schedules a group of several programs characterized by their time consuming very high on different supercomputers searching for an efficient assignment of the total running time. This efficient assignment grantees the fair load distribution of the execution on the supercomputers. The essential goal of this research is to propose several algorithms that can ensure the load balancing of the execution of all programs. In this research, all supercomputers are assumed to have the same hardware characteristics. The main objective is to minimize the gap between the total running time of the supercomputers. This minimization of the gap encompasses the development of novel solutions giving planning of the executable programs. Different algorithms are presented to minimize the gap in running time. The experimental study proves that the developed algorithms are efficient in terms of performance evaluation and running time. A comparison between the presented algorithms is discussed through different classes of instances where in total the number of instances reached 630. The experiments show that the efficient algorithm is the best-programs choice algorithm. Indeed, this algorithm reached the percentage of 72.86%, an average running time of 0.0121, and a gap value of 0.0545.
Citation: Algashami AM (2022) Algorithms for the executable programs planning on supercomputers. PLoS ONE 17(9): e0275099. https://doi.org/10.1371/journal.pone.0275099
Editor: Ali Safaa Sadiq, Nottingham Trent University School of Science and Technology, UNITED KINGDOM
Received: March 17, 2022; Accepted: September 11, 2022; Published: September 26, 2022
Copyright: © 2022 Abdullah M. Algashami. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript.
Funding: The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under project no R-2022-198.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
This paper focuses to develop an algorithm for the problem related to the scheduling of the execution programs by supercomputers. Different programs are received by the administrator to be scheduled on the available supercomputers. Each program has its own executing time. The supercomputers must be operated in the same way and with the same use of time. This is can be reached by an appropriate algorithm that can schedule the received programs with a fair way distribution. Otherwise, when fairness is not applicable, one supercomputer can be more exploited than another one.
The problem presented in this paper can be defined as follows. Suppose that there are several executable programs. These programs need a big amount of memory and a robust recourses like processor to ensure the accomplishment of the total execution. Each program is described by its own running time. The objective is concerned with finding a method that give a schedule for these executable programs guaranteeing a fair distribution in term of running time. There is no previous research in the literature that studied the proposed problem. However, many research works can be referred to the load balancing problem.
The load balancing treated the budget distribution is developed in [1]. A mathematical formulation was proposed to give an objective function for the problem. This formulation applied the minimization of the gap in the cumulative income between different regions. Three algorithms were treated to solve the latter problem. The randomization procedure is used as the first approximate solution. The second one is an iterative algorithm. The last one is the moving of the probabilistic values. In the same context, another work treated the project assignment was studied in [2]. In this work, the authors formulated the problem by giving a new objective function. Indeed, a minimization of the maximum revenue was studied. In this latter research, different algorithms were developed. These algorithms utilize the dispatching rules method and the multi-fit method. Different groups of instances are tested to measure the efficacy of the given algorithms.
In [3], the authors developed solutions regarding the cloud repository problem using the load balancing procedures. This paper focus on the issues in relation to good operation of storage servers in the cloud environment. The main objective of this research will contribute in handling several challenges concerning the load balancing in the cloud environment. Though analyzing the researches discussing topics in this field, in this paper 2 distributed load-balancing procedures. Similar work can be cited as [4]. A review regarding the load-balancing approaches in cloud context is proposed in [5].
Load-balancing dispatches the workload through several nodes to obtain better results when exploited the system. Different load balancing procedures occur to reach better resource exploitation. In [6], authors developed a discussions of load balancing procedures. In addition, the authors in the latter paper, give a comparison between the proposed algorithms on the basis of different indicators like mean no-waiting time, processing time, and data cost time.
The load balancing procedures are used in literature in the domain of the projects and budgets distribution. Indeed, in [7], several lower bounds were proposed, different heuristics, and a exact method for the project distribution were dedicated to propose solution. In this latter paper, the objective function is proposed as a new one compared with the one given in [2]. In [1], the author proposed three heuristics to solve the problem of the projects revenues assignment problem. In the same context, in [8] the authors proposed an exact algorithm for the budget scheduling problem. Different heuristics and algorithms were proposed to be used in the exact approach. The load balancing problem is studied and applied on many domains in literature.
The load balancing used on the storage spaces is utilized in [9]. In fact, several algorithms were developed. These algorithms are used in different by the dispatching rules approach. A comparison between the proposed algorithms are discussed.
A load balancing procedures regarding the supercomputers are treated in different previous works. A periodic load balancing procedure are studied in [10]. The load balancing regarding the message-passing in supercomputer were treated in [11].
On the other hand, the utilization of the load balancing is adopted in the network field. Indeed, several algorithms were developed to find an acceptable solution that ensuring the equity transmission of the give data [12].
Another field that the load balancing is applied, is the aircraft field [13]. Authors proposed several lower bounds regarding the load balancing applied on the gas turbine field. These lower bounds are based basically on the iterative approach, subset sum problem and the knapsack problem. In [14], the authors treated the same problem studied in [13] by using the randomization method.
In the domain of health care, several clustering algorithms which are used two sets is the best algorithm with 96% for the small scale instances and 98% for the big instances. The novelty of this research is the utilization of the dispatching rules by different modifications; randomized method, clustering approach; probability application algorithm, and multi-start algorithm to schedule quality reports to available physicians. The objective is to guarantee the fair assignment of the number of papers workload [15].
Authors in [16] developed a cost model related to the correctness of the load imbalance. This model offers discussions of the efficiency of load balancing procedures in any particular imbalance case. The developed process, in this latter paper, correctly selects the algorithm that carry out the lowest running time in up to 96% of the total cases, and may accomplish a 19% profit over selecting a single balancing procedure for all generated cases.
Utilizing loop parallelism is obviously most critical in accomplishing high system and routine efficiency. Because of the clarity of this method, guided self-scheduling is specifically adapted for execution on real parallel machines. This approach accomplishes concurrently the two most significant goals: load balancing and extremely low synchronization overhead. For particular types of repeating the results prove analytically that guided self-scheduling utilize minimal overhead and accomplishes optimal schedules. Two other interesting properties of this approach are its insensitivity to the first processor configuration (in time) and its parameterized nature which enables us to tune it for different systems [17].
In the industrial domain, the load balancing algorithms are used in [18] to ensure a better utilization of the machines and guarantee an equity use of machines. In the same context, the authors in [19] solve the equity distribution of the jobs on the machines by the multi-start algorithms applying the probabilistic method.
A dynamic balancing algorithm assumes the low effects of two main elements of the system which related to job behavior and the general state of the system, i.e., load balancing procedure using the actual status of the system. The establishment of an efficacious dynamic load balancing procedure encompasses several essential issues: load cost, load standard comparison, assessment indicators, system stability, amount of data exchanged between nodes, job resource required, job’s chosen for transfer, remote nodes chosen, and more [20].
In addition, dispatching the persons into vehicle ti ensure an equity distribution of these persons on the available parking is proposed in [21]. Recently, authors in [22] proposed novel algorithms to solve the parking managment. Several researches have been uploaded in the literature in order to cover some aspects in relation to the execution period and the gap valuation, which have been used to explore the assessment of the development procedures. Analyzing the gathered experiments shows a good indication in the assessment behavior of the proposed algorithm. In addition, it shows the proposed algorithm can narrow the gap in issues in relation to gap and time calculation in the developed researches. The MR heuristic reached an exceptional assessment results compared this result with the best algorithms proposed in [21]. The MR heuristic reached a percentage of 96%, a gap of 0.02, and a running time of 0.007s.
The usage of the supercomputers is largely referred in the literature. In fact, the utilization of the scheduling on the supercomputers filed may be cited to the different works. In [23], the authors studied the resource management through the usage of the supercomputers by an energy-performance procedure. The parallelism of the processing in the supercomputers is proposed by [24].
In [25], authors considered the methods of optimal load balancing and the storage requirements of algorithms. Other algorithms proposed in [26] can be utilized in future work to enhance the proposed algorithms. A mathematical model for scheduling activities while there are priorities between devices are proposed in [27]. In [28], authors treated the scheduling algorithms into networks. These algorithms can be exploited to give a new solutions for the presented problem. In [29], authors developed algorithms for the category constraint into network based on scheduling problem. Authors in [30] developed algorithms for the read frequency of data. These algorithms may be exploited on and adopted for the presented problem. A recent similar work for the latter paper are proposed in [31].
In this paper, a mathematical model of the presented problem is proposed. In addition, many algorithms were proposed to manage the studied problem. The problem can be defined as follows. Several programs characterized by its estimated execution time need to be ran by several supercomputers. All the supercomputers are assumed to be characterized by the same criteria as the hardware. The goal is to search for an efficient algorithm to schedule these programs to the available supercomputers. This is can be mathematically written as the load balancing of programs to supercomputers.
The rest of the paper is structured as follows. Section 2 presents the justification and motivation to work the studied problem. In section 3, the problem definition is described. Section 4 presents the research method and design. The developed algorithms to solve the presented problem are detailed in Section 5. The experimental results and discussions are analysed in Section 6. Section 7 is reserved for the conclusions and perspectives.
2 Justification and motivation
The presented problem may be defined as follows. Suppose that there are a set of several programs characterized by its estimated running time. This set of programs need to be executed by many available supercomputers. The set of programs is homogeneous. This is means that all the programs in the set have the same hardware characteristics. In addition, these programs are supposed to consume more resources in memory and time execution. Consequently, it is primordial to seek an efficient way to schedule the given programs on the available supercomputers. This may be mathematically written as the equity assignment of programs to the supercomputers. The goal of this paper is to concept and design an algorithm that may minimize the execution time gap between all supercomputers. The first phase toward achieving the goal of this paper is to formulate mathematically the proposed problem. After that, this mathematical formulation is utilized to develop different algorithms to solve the presented problem. This is constitute the second phase. A detailed discussions and explanation of these two phases are presented in this paper.
3 Problem definition
Table 1 gives an overview for all variable notations and definitions used in the paper.
Example 1 explains the presented problem using all above definitions.
Example 1 Suppose that nsu = 2 and npr = 7. The ep value for each program is illustrated in Table 2.
Now, an algorithm is ran to assign the programs detailed in Table 2 to the available supercomputers. This algorithm is the shortest execution time algorithm, the obtained schedule is illustrated in Fig 1. It is evident to see that programs {1, 5, 6, 7} are executed by Su1 and programs {2, 3, 4} are executed by Su2.
Fig 1 shows that Su1 has a total execution time of 1264. Moreover, Su2 has a total execution time of 893. Accordingly, the execution time gap between Su1 and Su2 is Rt1 − Rtmin = 1264 − 893 = 371. The goal is to conecpt and design an algorithm that can reduce the returned gap between supercomputers. In fact, for Example 1 another schedule must be given with a better solution comparing with schedule 1. This is means that an algorithm giving a gap less than 371.
Example 2 For this example, the instance detailed in Table 2 is examined. Calling the longest execution time algorithm, the result given the schedule is illustrated in Fig 2. It is easy to see that programs {2, 3, 5} are executed by Su1 and programs {1, 4, 6, 7} are executed by Su2.
Fig 2 shows that Su1 has a total execution time of 1013. Moreover, Su2 has a total execution time of 1144. Thus, the execution time gap for Su1 and Su2 is Rt2 − Rtmin = 1144 − 1013 = 131. For this example, the schedule 2 gives a better results than schedule 1.
In general, facing on different supercomputers, an indicator must be determined to evaluate the gap of the algorithm that searching the load balancing. Eq 1 represent the gap of the execution. This gap is calculated between the supercomputer that having the minimum total execution time and all others supercomputers. This gap must be minimized to guarantee the load balancing. Hereafter, this gap is denoted by Grt.
(1) Proposition 1 Based on Eq 1, the gap Grt may be formulated such that in Eq 2.
(2) Proof 1
. It is clear to see that
. Thus, the Eq 2 is obtained.
4 Research method and design
In this section, six components are proposed for the proposed model. To more understand the model proposed in this research, an example is given of two supercomputers and four programs as shown in Fig 3. The first component is “supercomputer”. For the example given in This component contain the supercomputer. The “data center” is a component that can receive the results of the executed programs by the supercomputers. In addition, this component is responsible to send all new programs to be executed to the administrator. The component which guarantee an efficient transmission between the supercomputers and the data center is the “Wireless Access Point”. The “administrator” is the component represented by the user that having all access authorization and can decide for choosing the programs sent by the data center. Finally, the component “scheduler” is responsible to apply the developed algorithms to propose a solution regarding the good distribution of programs to the different supercomputers. The main component in this research work is the “scheduler”.
In this paper, a new design of the studied problem regarding the planning of the executable program on the available supercomputers is proposed as illustrated in Fig 3. The proposed components are focalized on the scheduler one. The scheduler is responsible to call all the algorithms to solve the scheduling problem and decide which program must be executed on the fixed supercomputer. Several variants of probabilistic method are proposed. In general, using a probabilistic method and the randomization approach give an efficient approximate solution for the scheduling solution problem. It is important to notice that the developed problem is NP-hard one. This is confirm that a good approximate solution represent a great archive for the studied problem.
Based on the example of 4-programs and 2-supercomputers shown in Fig 3, a generalization of the model as shown in Fig 4 can be illustrated. The general model is composed by five fundamental components: Supercomputers engine, scheduler, Programs engine, wireless access point, and data center.
5 Developed algorithms
In this section, seven proposed algorithms will be presented and detailed. Indeed, each algorithm will be explained and a pseudo-code will be illustrated for some algorithms to explain the functionality of these algorithms. The complexity of each algorithm is given.
5.1 Longest execution time algorithm
A dispatching rule method is applied for the longest-running time algorithm (LTA). Firstly, all programs are listed in the non-increasing order of its execution time. Next, the programs that have the longest execution time will be executed by the supercomputer that has the minimum ctp and so on until completing all the workload.
5.2 Shortest execution time algorithm
A dispatching rule method is applied for the longest-running time algorithm (STA). Firstly, all programs are listed in the increasing order of its execution time. Next, the programs that have the longest execution time will be scheduled on the supercomputer that has the minimum ctp, and so on until completing all workload.
5.3 Programs choosing probabilistic algorithm (PCP)
This algorithm applies the probabilistic approach. The first program which will be chosen to be ran by the supercomputer is picked by a probability β. After that, the second program will be picked among the remaining programs applying the same probability β and so on until completing all workload. In the implementation code, the calculation of the probability is determined as follows. In the first, a number h is generated and returned randomly in [1, ns]. Now, Prh is selected and executed by the available supercomputer. The set Pr will be updated by removing Prh from this set. So, Pr = Pr \ Prh. This procedure is repeated Lmt = 1000 times and the minimum value of the returned gap Grt will be stored.
Hereafter, Rad(x, y) denoted the function that give a random number in [x, y]. SLg(r, z) is the function that schedules Prr on Suz.
This algorithm is PCP and all details are illustrated in Algorithm 1. The complexity of PCP algorithm is O(n2).
Algorithm 1 PCP algorithm
1: for (v = 1 to Lmt) do
2: Fix x = nsu
3: while (x ≥ 1) do
4: Fix k = Rad(1, x)
5: Available supercomputer is Suz
6: Call SLg(k, Suz)
7: Fix x− −
8: end while
9: Determine Grtv
10: end for
11: Determine
5.4 Non-decreasing-order-programs choosing probability algorithm
This algorithm applies the probability-approach as detailed in the Subsection 5.3. In the first, the programs is sorted in the non-decreasing order of its estimated execution time. The selected program that can be ran by the free supercomputer is picked by a probability γ. This instruction will be repeated Lmt = 1000 times and the minimum obtained gap Grt will be stored. Hereafter, ICG(m) represent the function that sort Prm in the non-decreasing order of its estimated execution time. The complexity of NCP algorithm is O(n2).
This algorithm is denoted by NCP. The instructions of NCP are illustrated in Algorithm 2.
Algorithm 2 NCP algorithm
1: Call ICG(Pr)
2: for (v = 1 to Lmt) do
3: Fix x = nsu
4: while (x > 0) do
5: Fix k = Rad(1, x)
6: Available supercomputer is Suz
7: Call SLg(k, Suz)
8: Fix x− −
9: end while
10: Determine Grtv
11: end for
12: Determine
5.5 Decreasing-order-choosing probabilistic algorithm
This algorithm applies the randomization approach as detailed in the Subsection 5.3. In the first, the programs is sorted in the non-increasing order of its ep. The picked program which will be ran by the free supercomputer is chosen by a probability γ. This instruction is repeated Lmt = 1000 times. The minimum gap will be stored. Denoted by DRG(Pm) the procedure that sort the programs Pm in the non-increasing order of its estimated execution time. The complexity of DCP algorithm is O(n2).
This algorithm is DCP. The details of DCP are illustrated in Algorithm 3.
Algorithm 3 DCP algorithm
1: Fix DRG(Pr)
2: for (v = 1 to Lmt) do
3: Fix x = nsu
4: while (x > 0) do
5: Fix k = Rad(1, x)
6: Available supercomputer is Suz
7: Call SLg(k, Suz)
8: Fix x− −
9: end while
10: Determine Grtv
11: end for
12: Determine
13: Return Grt
4.6 Three-variant-programs choosing probabilistic algorithm
This algorithm applies the probabilistic approach as detailed in the above subsections. In first, PCP algorithm is called. The solution obtained by PCP is Denoted by Grt1. After that, the NCP algorithm is called. Denoted by Grt2 the returned solution. Finally, DSP algorithm is called and denoted by Grt3 the returned solution. The best solution between Grt1, Grt2 and Grt3 is picked. The complexity of TSP is O(n2). Denoted by PCP(Pm), NCP(Pm), and DCP(Pm) the functions calling the algorithms PCP, NCP, and DCP, respectively.
This algorithm is TVP. The details of TVP are illustrated in Algorithm 4.
Algorithm 4 TVP algorithm
1: Call PCP(Pm)
2: Determine Grt1.
3: Call NCP(Pr)
4: Determine Grt2.
5: Call DCP(Pr)
6: Determine Grt3.
7: Determine Grt = min(Grt1, Grt2, Grt3)
8: Return Grt
6 Experimental results and discussions
Many indicators are given to assess the efficiency of the presented algorithms. Through these indicators, a comparison between the proposed algorithms are discussed. All proposed algorithms were implemented in C++. The computer executing all the developed code is an Intel(R) Core (TM) i5-3337U CPU and the operating system is Windows 10.
6.1 Instances and tests
Three classes are proposed in the subsection “Instances” to measure the efficiency of the presented algorithms and three indicators are presented in the subsection “Tests”.
6.1.1 Instances.
Different instances are tested and experimented. The types of classes applied in this paper are the uniform distribution which is denoted by UN[x1, x2]. The comparative study between the algorithms and the manner that the instances are generated are inspired from the study [32].
The ep values will be as:
- Class A: x1 = 1 and x2 = 100, ep ∈ UN[1, 100].
- Class B: x1 = 10 and x2 = 150, ep ∈ UN[10, 150].
- Class C: x1 = 100 and x2 = 500, ep ∈ UN[100, 500].
The permutation of the pair (npr, nsu) discussed in this experimental results are listed as follows. The small scale is for npr = {10, 25, 30} the number of supercomputers is nsu = {4, 5, 6}. The big scale is for npr = {50, 60, 80, 100} the number of supercomputers is nsu = {5, 6, 10, 12}
For each pair (npr, nsu) and each class, 10 instances were tested. The total generated instances is 630.
6.1.2 Tests.
The indicators Gb, Time, and Per used to asses the algorithms are defined in Table 1.
Fig 5 represented the performance test measurement of the proposed algorithms. In this latter figure, it is supposed that there are four different values of the Grt obtained by four different algorithms. These values are Grt1, Grt2, Grt3 and Grt4. It is clear to see that Grt1 is better than Grt2, Grt3 and Grt4 because Grt1 is the minimum value and the objective is to minimize Grt. The value LB in Fig 5 represented the value of the lower bound for the studied problem. In general, the exact solution is in LB, Grt, with Grt is the value obtained by any algorithm. The closest value of Grt to LB is reached by Grt1. This is meaning the interval [LB, Grt1] which represent the exact solution interval is the smallest interval comparing when choosing Grt2, Grt3 and Grt4. This is prove that the choice of Gb value to asses the performance of the developed algorithms.
6.2 Results
Table 3 illustrated the overview of all algorithms. The variation of Per, Gb, and Time are presented in this latter table. The best algorithm that have the minimum gap is BPC reaching a percentage of 72.86%, an average gap of 0.0545 and average execution time of 0.0121 s. The second best algorithm is TVP reaching a Pcg value of 45.08%, a Gp value of 0.1944, and a Time value of 0.0121 s. The STA never obtained a minimum gap value.
Table 4 shows the Gp values variation for all proposed algorithms according to the number of programs. This table displays that the best Gp value of < 0.0001 is reached by TVP and BPC where npr = 10. On other hand, a maximum Gp value of 0.8905 is reordered by SRT where npr = 30. For npr = 100, the best Gp value of 0.0531 is recorded by BPC and a maximum Gp value of 0.7803 is recorded by STA.
Table 5 shows the Gp values for the proposed algorithms when nsu according to the number of supercomputers. This table displays that the best Gp value of 0.0263 is reached by BPC where nsu = 14. On other hand, a maximum Gp value of 0.8817 is returned by SRT where nsu = 4. Where nsu = 14, a maximum Gp value of 0.6952 is returned by STA.
Table 6 illustrates the Gp values for all algorithms and for each class.
Table 7 shows the Time variation for all proposed algorithms when npr changes. For BPC algorithm, the Time values increases when npr increase. In addition, the minimum Time value of 0.0027 s is returned where npr = 10 and the maximum Time value of 0.0236 s is returned where npr = 100.
Table 8 shows the Time variation for all proposed algorithms when nsu changes.
Each triple (npr, nsu, class) will be denotes by Tp. The values of npr are {10, 25, 30, 50, 60, 80, 100} and the values of nsu are {4, 6, 8, 12, 14}. Three classes are proposed. So, in total 63 values of Tp are presented. Fig 6 shows the Gp values variation when Tp changes for BPC.
Fig 7 shows the Time behavior when Tp changes for BPC. This figure prove that the Time values are constantly increasing when npr increase. The maximum Time value of 0.0281 s is returned where (npr, nsu, class) = (100, 14, 1) and the minimum Time value of 0.0022 s is returned where (npr, nsu, class) = (10, 4, 1).
6.3 Discussions
Seven algorithms are proposed and tested in this paper. Table 3 shows the overview of the results given by all algorithms. this table shows that the best algorithm that reached the maximum percentage is BPC with a percentage of 72.86%. This percentage is not reaching the value of 100%, this is means that there is no dominance between the proposed algorithms. Indeed, for certain instances, the other algorithms excepting BPC give best results than BPC but in total BPC reaches 72.86%. The algorithms PCP, NCP, and DCP give closer results in the range of 25%. The maximum average time obtained by all algorithms is around 0.0121 s. This gives a remarkable runtime to reach an efficient solution. It is worth noting that, the TVP reached a 45.08% as performance compared with BPC that has 72.86% as a performance, in the same average runtime of 0.0121 s. Therefore, by consuming the same time a better solution can be reached by choosing the algorithm BPC. In this paper, the choice of three classes is based on several works in literature that use these classes and the generation of the instance to test the obtained results. Indeed, the choice is to give a variety of different ranges of the scale of the proposed problem. For Class 1, the range is for the small instances. For Class 2, the range is for the medium instances. Finally, Class 3 is for the big-scale instances reaching 500 programs. On other hand, a different range of scales is proposed for the number of programs and the number of supercomputers. The runtime of the algorithms is related to the structure of the utilization of the loop instructions in each algorithm. For LTA and STA, the average execution time is always less than 0.0001 s. This is obtained because these algorithms utilizing the dispatching-rules which are executed by calling the heap-sort algorithm which is O(nlogn). However, for algorithms PCP, NCP and DCP the complexity is O(n2). This explains the average execution time which is very close.
The algorithms proposed in this paper are based on several variants of the probabilistic method. In practice, the probabilistic method and the randomization approach provide a better solution for the scheduling problem. This is due to the multiple choice selection of the programs and the repetitive procedure. This observation is always applicable for the studied problem when the best-programs choosing algorithm is the best compared with other algorithms. Consider the two case studies for the presented problem: the small-scale instances and the big-scale instances. The small-scale instances are the instances such as npr ≤ 50. However, the big-scale instances are the instances such as npr > 50. The BPC algorithm gives remarkable results for the first case study which is the small-scale instances and for the second case study which is the big-scale instances in term of gap. Indeed, the average gap of all small-scale instances is 0.0616 and the average gap of all big-scale instances is 0.0451. In term of running time, the BPC algorithm gives remarkable results for the first case study and for the second case study. Indeed, the average running time of all small-scale instances is 0.0067 and the average running time of all big-scale instances is 0.0194. The proposed algorithms can be applied on several real applications. Indeed, the parallel computing which is a type of computation in which several calculations must be executed at the same time. Big problems can be classed into many small problems, which may then be solved simultaneously. Other real application of the proposed algorithms is the parallel programming which is the process of utilizing a group of resources to solve a problem in minimum time by partitioning the big work. It is worth noting that the studied problem is an NP-hard one. Consequently, a good approximate solution represents a great archive for the studied problem. Several meta-heuristics can be applied using the proposed algorithms to enhance the obtained results.
7 Conclusion
This paper discussed the program’s planning problem. These programs must be executed by a fixed number of available supercomputers. Each program requires a long period of time to be executed. the presented problem is strongly very hard. Especially, the hardness of the problem appears face on a big number of programs. In this paper, seven algorithms were developed to present solution of the studied problem. These algorithms utilize the dispatching rules and the randomization approach with several variants. The experimental results show that an efficient feasible solution can be offered in an acceptable running time. The best-developed algorithm is the best-programs choosing algorithm in 72.86% of instance cases. In addition, the experiments show that the developed algorithms do not impose any dominance on them.
From a perspective of the studied problem, the developed algorithms can be exploited to develop a new better solution based on metaheuristics like a genetic algorithm or swarm optimization. The optimal of the studied problem can be constructed by an exact solution method based on the tree searching. In this case, the developed algorithms will be used to test and calculate certain rules in the node of the research tree.
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