**9. Summary and conclusion**

In this chapter, by using BiLeG an allocation-plan is devised which reflects the overall resource allocation strategy comprising two parts; a policy used at the higher decision making module, TRPS, which has the responsibility to select a resource-pool for each of the tasks; and a resource allocation algorithm used at the lower decision making module, RA, which actually assigns resources from the resource-pool selected by TRPS for a particular PBDT task. Three RA algorithms and six TRPS policies have been proposed in this chapter forming different allocation-plans. The suitability of various allocation-plans under different sets of system and workload parameters has been explored.

Detailed study of the various trade-offs, implicit in the use of different allocation-plans, is the focal points of this chapter. The most suitable allocation-plan not only depends on various workload and system parameters, it also depends on the user requirements and the hardware available. It can be seen that from the performance perspective various trade-offs exist among different allocation-plans and understanding these trade-offs in depth is the focus of the experiments conducted in this chapter.

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For the choice of an appropriate allocation-plan, two of the important considerations that came out of these experimental results are the size of the Grid and the performance metric chosen for optimization. Generally, from the results obtained from the experiments conducted in chapter, it can be concluded that if an allocation-plan tries to minimize one of the performance metrics, it tends to yield higher values of the other performance metrics. For example, <SRPsp,ATSRAorg> always gives the lowest value of tcost but it also yields one of the highest values for tms-WOH , especially for a large number of nodes. At RA, the tradeoffs associated with reducing the accuracy of the ATSRA algorithm by relaxing some of the constraints in the LP formulation have been studied. The combination of the proposed RA algorithms and TRPS policies gives rise to various allocation-plans. These allocation-plans can be used under a wide variety of system and workload parameters to maximize the use of available resources according to a pre-determined optimization objective.

Although the research summarized in this chapter has focused primarily on the Grid systems, the proposed BiLeG architecture can also be used in a Cloud Computing environment. Cloud Computing environments are often classified as public and private Cloud environments [3]. The private Cloud environment is better suited for the BiLeG architecture; as a private Cloud environment uses a dedicated computing infrastructure that provides hosted services to a limited number of users behind a firewall and can, thus, more easily incorporate mechanisms to accurately predict the computing and communication costs.

The algorithms presented in this chapter are based on a dedicated resource environment. To adapt the BiLeG architecture to shared environments, more research is required. For example, in order to use it in a shared resource environment, mechanisms to accurately predict the unit communication and processing times are needed to be incorporated in the BiLeG architecture. Also, in a shared environment, investigating the impact of various types of communication models, such as many-to-one and one-to-many forms, an important direction for the future research.
