A Smart Algorithm for Dynamic Task Allocation for Distributed Processing Environment

Govil, Kapil

doi:10.5120/3362-4641

Cited by 9 publications

(7 citation statements)

References 23 publications

(16 reference statements)

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“…The communication cost of the tasks in the same cluster is assumed to be zero. Using this strategy it has been observed that the total system cost is less as compared to that which is obtained by the heuristic reported in [5].…”

Section: Cluster-based Schedulingmentioning

confidence: 93%

“…Now, the ACL(,) and SUMNEW(,) are: Step 8: COST(,) = FIN(,). On applying Hungarian Algorithm [5] to the FIN (,). We get 0 1 0 ALLOC(,) = 0 0 1 1 0 0…”

Section: Implementation Of Algorithmmentioning

confidence: 99%

“…The optimal assignment graph is shown in Figure 4. The performance comparison of proposed algorithm and the algorithm discussed in [5] on the same data set is shown in Table 2.…”

Section: Implementation Of Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

An Effective Load Balancing Task Allocation Algorithm using Task Clustering

Bhardwaj¹,

Kumar²

2013

IJCA

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In Distributed Computing Systems (DCSs), a program is split into small tasks and distributed among several computing elements to minimize the overall system cost. Several challenges have been posed by this mode of processing which can be classified mainly into two broad categories. One class belongs to the hardware oriented issues of building such systems more and more effective while the other aims at designing efficient algorithms to make the best use of the technology in hand. The task allocation problem in a DCS belongs to the later class. Intrinsically, task allocation problem is NP-hard. To overcome this issue, it is necessary to introduce heuristics for generating near optimal solution to the given problem. This paper deals with the problem of task allocation in DCSs in such a way that the load on each processing node is almost balanced. Further, the development of an effective algorithm for allocating 'm' tasks to 'n' processors of a given distributed system using task clustering by taking both Inter Task Communication Cost (ITCC) and the Execution Cost (EC) is taken into consideration.

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Section: Cluster-based Schedulingmentioning

confidence: 93%

“…Now, the ACL(,) and SUMNEW(,) are: Step 8: COST(,) = FIN(,). On applying Hungarian Algorithm [5] to the FIN (,). We get 0 1 0 ALLOC(,) = 0 0 1 1 0 0…”

Section: Implementation Of Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Load Balancing Task Allocation Algorithm using Task Clustering

Bhardwaj¹,

Kumar²

2013

IJCA

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“…For this master problem, two main directions of finding solutions are suggested, either classifying solutions from a best non-dominated level, or projecting the solutions to a created dimension so as to have a comparable objective function. Aligning to the former suggestion, Chaharsooghi et al enhanced the ant colony optimization algorithm to figure out an allocation (Chaharsooghi, 2008), and Govil considered the cost of re-allocation as the objective to be optimized (Govil, 2011). Since the design problem in the operation stage limits the number of plan possibilities, it makes the constraints more stringent than common NP-hard problems.…”

Section: Decision-making In Multi-objective Questionmentioning

confidence: 99%

Optimization of a User-Involved Interior Layout Recommendation System at the Operation Stage

Wen¹,

Daher²,

Kubicki³

2020

CIB W78 Proceedings

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The parametric modelling method, an algorithmic thinking process based on visual programming, is recently established as a supportive tool in the decisionmaking practice for architecture. For the time being, the method helps to inform, control and optimize the architectural design by expressing the input parameters and conditional rules that are generated according to the design objectives. This paper describes how a parametric-model-based recommendation system is developed for an interior floor layout optimization problem which supports user involvement in different aspects of the process. The system connects the design objectives and the user preferences to propose customized space layouts in the operation stage of buildings, and one of the feasible relaxations of this design study is to substantially generalize it as a multi-objective optimization problem. As such, the algorithm-part of the system contains three different functionalities: (a) a screening scheme to select the available spaces concerning given requirements, (b) a generation process to figure out different possibilities of interior plans, and followed by (c) an evaluation system to compare and recommend the best-matching solutions. Simultaneously, the user interface of the system builds up various interactions between the users and the parametric models throughout the process of design, so as to collect the criteria, preferences, and priorities of the design objectives. The recommendation system is also implemented in a real case study, the floor planning of the IAK-1 building of the European Investment Bank in Kirchberg, Luxembourg, to assess the algorithm performance and user experience. The results illustrate the applicability of this approach in real-life design, and the pros and cons of the generated plans are also analysed by comparing to traditional designs given by expert architects.

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“…There is several task allocation methods like load balancing, integer programming, divide and conquer, grid computing [1] are reported in literature for distributed production and distributed systems and specifically for GSD techniques in literature are modification request [2], global studio Project [3], distributed Cocomo [4], Simulation model [5], Reference model for GSD [6], 24 hour software development model [7], TAMARI [8]. Still there is a need for appropriate and efficient work load sharing strategies to improve the performance of GSD teams.…”

Section: Introductionmentioning

confidence: 99%