International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. 14, No. 1 (Dec-23) http://dx.doi.org/10.12785/ijcds/1401120 Resource Allocation in Cloud Computing with Economical Strategic Setting Anjan Bandyopadhyay 1 , Sujata Swain 1 , Dipti Dash 1 and Arup Roy 2 1 School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India 2 Budge Budge Institute of Technology, Kolkata, India Received 29 June 2023, Revised 18 Sep. 2023, Accepted 31 Oct. 2023, Published 28 Dec. 2023 Abstract:Resource allocation and pricing techniques in cloud computing is a challenging task for every researcher. Everyone try to find out a good solutions of resource allocation and pricing in a dynamic way. Every user give their demands for taking the cloud resources for a particular time period. If the request is not fulfilled within the time period then the user ask for extra time for completion his task. The cloud computing automatically generate a reverse auction strategy for implementing the task and the task will be performed in the other cloud which have been participated in the auction. Here we have proposed Resource Allocation with Economical Strategy (RAES) as an efficient and cost-effective framework for resource allocation with pricing . We have compared our algorithm with ECON and Greedy algorithm. For simulation purpose, we keep number of users and capacity of the system constant and vary average number of CPU required by the users. We observe that number of allocations made by our algorithm is significantly good perform than the basic ECON scheduling algorithm and Greedy algorithm. Keywords: Resource Allocation, Auction Theory, Pricing Technique, Capacity conflict 1. INTRODUCTION A new computer paradigm called cloud computing en- ables users to share on-demand computing resources like CPU and RAM. Services like Dropbox and Google Docs, as well as apps like Flikr, leverage cloud computing to store documents. Because cloud computing is an infrastructure- less service, we must share a significant amount of virtual infrastructure with the users rather than installing physical equipment at their end. When sharing these resources, users confront two main challenges: the first is how to distribute system infrastructures like CPU, RAM, and other hardware across several users; the second is how to reimburse service providers for the pricey infrastructures they offer. Allocating infrastructure to users that require services has involved a lot of work. Server sharing and task scheduling across a time horizon are the two main concerns that infrastructure allocation primarily addresses. [1]. Virtual machines and container-based technology are used to share servers among users who are concerned about security [1], [2], [3]. Scheduling tasks that arrive over a time horizon is primarily driven by two goals. The first is service level objectives, or SLOs, which are addressed in [1], [4], [5], [6] (for example, meeting the deadline of the tasks under consideration). Utilizing clusters is the second goal, which is well-discussed in the literature [7], [8], [9]. Both public and private clouds have implemented the theories concerning system level problems that have developed in the literature [10], [11]. This is a strong indicator that allocation perspective has developed to some extent [1]. It is vital to design effective pricing systems (that is, how much money should be required based on the service being delivered) together with suitable allocation methods when infrastructure-less service demand from individuals (may be a person, an organisation, etc.) grows [1]. There haven’t been many recent efforts in this regard, where the main emphasis is on economically viable solutions [1], [12], [13], [14], [15]. In the current cloud computing environment, the following pricing models are in use: • Prepaid fixed prices with a certain volume of services guaranteed [1], [16], [17]. • Unit costs based on the demand for the resources being used [1], [9], [10], [11]. • Based on the spot instance price, an interesting pric- ing scheme is there, where an individual can bid according to his maximum willingness to pay [1], [17]. In contrast to the current pricing schemes utilised in the current deployment of cloud computing, the major challenge E-mail address: anjan.bandyopadhyayfcs@kiit.ac.in, sujata.swainfcs@kiit.ac.in, dipti.dashfcs@kiit.ac.in https:// journal.uob.edu.bh