In Distributed computing systems (DCSs), task allocation strategy is an essential phase to minimize the system cost (i.e. the sum of execution and communication costs). To utilize the capabilities of distributed computing system (DCS) for an effective parallelism, the tasks of a parallel program must be properly allocated to the available processors in the system. Inherently, task allocation problem is NP-hard in complexity. To overcome this problem, 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 DCS such that the system cost is minimized. This can be done by minimizing the inter-processor communication cost (IPCC). Therefore, in this paper we have proposed an algorithm that tries to allocate the tasks to the processors, one by one on the basis of communication link sum (CLS). This type of allocation policy will reduce the inter-processor communication (IPC) and thus minimize the system cost. For an allocation purposes, execution cost of the tasks on each processor and communication cost between the tasks has been taken in the form of matrices.

Distributed computing systems [DCSs] offer the potential for improved performance and resource sharing. To make the best use of the computational power available, it is essential to assign the tasks dynamically to that processor whose characteristics are most appropriate for the execution of the tasks in distributed processing system. We have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phasewise execution cost [EC], intertask communication cost [ITCT], residence cost [RC] of each task on different processors, and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model. The present model is suitable for arbitrary number of phases and processors with random program structure.

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A Distributed Computing System (DCS) is defined as a set of processing elements interconnected by communication links. Reliability analysis of these processing elements and communication links is one of the important parameters to achieve the system efficiency. The system efficiency can be improved by making the task allocation properly in DCS. In this paper, we have presented a mathematical model, considering DCS with heterogeneous processors in order to achieve optimal cost and optimal reliability by allocating the tasks to the processors, in such a way that the allocated load on each processor is balanced. The task allocation in DCS is known as NP-hard problem even in the best conditions, and based on the present model, an efficient algorithm have been proposed to obtain optimal solutions. To design the mathematical model, execution time of the tasks on each processor as well as communication time between the tasks has been taken in the form of matrices.

Distributed Computing System [DCS] has attracted several researchers by posing several challenging problems. In this paper we have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Most of the researchers have considered the cost for relocating the task from one processor to another processor at the end of the phase as a constant. But in real life situations the reallocating cost of the tasks may very processor to processor this is due to the execution efficiency of the processors. Phase-wise execution cost [EC], inter task communication cost [ITCT], residence cost [RC] of each task on different processors and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model.