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Cloud Computing is becoming an increasingly popular computing model in which computing resources are made available on-demand to the user as needed (Mell and Grance, 2011; Hayes, 2008). Work on Cloud Computing was started late 2007, when Grid Computing (Voorsluys et al., 2011; Ismail et al., 2008) was used. Growth of Cloud Computing is increasing exponentially (Buyya et al., 2009; Buyya et al., 2011). The benefits of cloud computing include services on low costs and capital expenditures, increased operational efficiencies, scalability, flexibility and so on.
Cloud Computing provides different types of services, which falls mainly in three categories. I) SaaS (Software as a Service) II) PaaS (Platform as a Service) III) IaaS (Infrastructure as a Service) (Khiyaita, 2012).Cloud Computing provides many opportunities for enterprises by offering a range of computing services (Miller, 2008; Armbrust et al., 2009, Chieu et al., 2009). These opportunities, however, don’t come without challenges. Dynamic resource allocation is also a challenge in Cloud Computing. When many users make request for cloud resources concurrently then how these requested resources will be allocated to user and in which order users will get resources. This is a challenging task in Cloud Computing.
There has been lot of research work on internet traffic handling, data security and many algorithms are proposed regarding to resource allocation (Heiser et al., 2007;Schlegel et al., 2008; Bowers et al., 2009; You et al., 2009; Kumar et al., 2011; Emeakaroha et al., 2011; Dai et al., 2012; Wang et al., 2012; Zhu et al., 2012; Huang et al., 2013). As resources are limited on the internet, hence resource allocation should be efficient and dynamic, so that better resource utilization can be achieved. Dynamic resource allocation is necessary because network bandwidth is also limited and traffic should be handled carefully. So that the congestion can be minimized. It will also improve quality of service.
In this paper a resource allocation technique called Multicriteria Preference Synthesis (MPS) is proposed. MPS method is used to synthesize the Analytical Hierarchy Process (AHP) and AHP is used to rank the tasks of users. MPS method uses error criteria like Euclidean Distance and Minimum Violation to maintain the consistency in the priorities of the tasks.
Analytical Hierarchy Process has been used for resource allocation in Cloud Computing (Ergu et al., 2011). EigenVector Method is used for synthesis of AHP method (Saaty, 1986, 1999, 2003) in Cloud Computing. The benefit of this method is that it derives the weight of users’ tasks accurately. This method assigns a particular priority to each user task, which is further used in assigning Cloud resources to users’ tasks.
This method has a disadvantage in Cloud Computing environment. In the EigenVector method when the size of comparison matrix is large, then inconsistency occurs in large amount (Srdjevic, 2005; Alonso Lamata, 2006). In Cloud Computing there are thousands of users which use Cloud resources at one time. Hence the size of comparison matrix using this method will be larger and there will be lots of inconsistency in priorities of users’ tasks.