Faculty Publications
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Publications by NITK Faculty
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Item Improving the efficiency of genetic algorithm approach to virtual machine allocation(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Virtual machine (VM) allocation is the process of allocating virtual machines to suitable hosts. This problem is an NP-Hard problem. It can be considered as a variation of the bin-packing problem. Among various solutions that attempt to solve this problem, several approaches that apply Genetic Algorithm have been proposed. This paper proposes a method to improve the efficiency of such approaches. Implementation of the proposed approach shows significant improvements in the runtime, memory used, energy efficiency and SLA violations. © 2014 IEEE.Item A perspective study of virtual machine migration(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Cloud Computing is one of the leading technologies. As a solution to many of the challenges faced by Cloud providers, virtualization is employed in Cloud. Virtual machine migration is a tool to utilize virtualization well. This paper gives an overview of the different works in literature that consider virtual machine migration. The different works related to virtual migration are classified into different categories. Some of the works that consider less explored areas of virtual machine migration are discussed in detail. © 2014 IEEE.Item Fuzzy Reinforcement Learning based Microservice Allocation in Cloud Computing Environments(Institute of Electrical and Electronics Engineers Inc., 2019) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Kandasamy, A.Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies. © 2019 IEEE.