Dr George Justy Mirobi
@kbncollege.ac.in
Assistant Professor
Department of MCA KBN College Vijayawada Andhra Pradesh
Dr George Justy Mirobi works as an Assistant Professor in the Department of Computer Applications at K.B.N. College, Vijayawada, Andhra Pradesh. She received her Ph.D. in Computer Science from Bharathiar University, Coimbatore, Tamil Nadu. She has received two Best Paper Awards in i) International Conference on Computer Networks, Big Data and IOT (Springer-ScopusIndexed-ICCBI2019)atVagai Engineering College, Madurai. ii) International Conference on Smart Systems and Inventive Technology (IEEE-ICSSIT 2019) at Francis Xavier Engineering College, Tirunelveli. She has worked in King Abdul Aziz University, Jeddah, Saudi Arabia. She has published many papers in International and National journal and conferences on Cloud Computing and IOT. Her research interest includes Internet of Things, Machine Learning, Big Data, Block Chain, Cloud Computing and Cyber Security.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Computer Science Applications, Computational Theory and Mathematics
Scopus Publications
Scopus Publications
George Justy Mirobi, V. Suresh, and M Kala Devi
IEEE
Workflow Scheduling (WS) is a technique to arrange the jobs in an order of workflow, map the tasks on distributed system and manage the execution process of interdependent tasks in a workflow. It presents to allocate the proper resources for particular tasks of workflow, therefore, execution process can be done with the objectives defined by the end users and fulfill the end user’s request. This research targets to organize the given workflow that describes an accurate sequence of implementation of tasks. When the number of incoming tasks is maximized, the response time is also raised and performance becomes low. To handle a large number of consumers and their applications, an efficient workflow scheduling approach is necessary. The existing workflow scheduling algorithms are analyzed, that algorithms do not consider incoming request variations and performance variations. An efficient workflow scheduling procedure is proposed to arrange the incoming consumer’s requests according to the priority of the consumer, assign the suitable virtual machines, virtual machines are mapped to the tasks; it succeeds implementation process, offer resources within the allotted period and enhances system performance.
G. Justy Mirobi and L. Arockiam
Springer Science and Business Media LLC
G. Justy Mirobi and L. Arockiam
Springer International Publishing
G. Justy Mirobi and L. Arockiam
IEEE
Cloud computing covers a broad range of cloud-based service models such as SaaS: Software as a Service, PaaS: Platform as a Service, IaaS: Infrastructure as a Service, XaaS: Everything as a Service, NaaS: Network as a Service and RaaS: Recovery as a Service. Whether the users are looking at SaaS, PaaS, IaaS, XaaS, NaaS or RaaS, the estimations are the same; fast development of the workloads placed on the VMs in the cloud and a maximized percentage of the IT budget moving toward cloud computing. Therefore, there is a need for an enhanced VM level load balancer to balance the VM level load. In this research, different dynamic load-balancing methods and enhanced algorithms are analyzed to develop a load balancer for allocating the workloads evenly to the VMs and to migrate the excess tasks from the overloaded VMs to the underloaded VMs. The motivation of our research is to propose an Enhanced Throttled Load Balancer for allocating the workloads evenly to all VMs and for minimizing the delay time and response time of the service. The proposed load balancer distributes the arriving requests evenly to all VMs and allocates the resources dynamically. Using the threshold value, the proposed load balancer categorizes the VMs as overloaded VMs, underloaded VMs and balanced VMs. If there is an overloaded VM then the proposed Enhanced Throttled Load Balancer finds out the suitable underloaded VM and automatically begins the task migration process to migrate the task to the underloaded VM from the overloaded VM thereby balance the load on VMs. The main use of the proposed load balancer is to balance the load on VMs and to control the overhead of the VM. This paper presents a dynamic load balancing approach using Enhanced Throttled Load Balancer that aids to reduce the delay time and the response time of the service.
G. Justy Mirobi and L. Arockiam
IEEE
The cloud computing system complies the customer to access data and programs outside of the user's computing environment. Rather than storing the user's data and software on the user's personal computer or server, it is stored in the cloud. These cloud services comprise of applications, email, databases and file services. To access the cloud services, the requests are given as workflow. A workflow is a series of steps comprised of achieving a well-defined objective in a cloud environment. These steps are in a specific order to enhance the execution process and ensure efficiency. The main issue in executing the workflows is the uncertainty of the request process period and response period. The existing algorithms are not suitable to handle the above-mentioned issue. An efficient workflow scheduler is necessary to schedule the requests, select the requests and map the selected tasks to the appropriate VMs for handling their execution procedure while satisfying all dependencies, constraints and objective functions. The goal of the proposed workflow scheduler is to offer cloud services in a quite short time. The workflow scheduler is used to provide the cloud services with minimum process time and response time as defined in the Service Level Agreement (SLA).
G. Justy Mirobi and L. Arockiam
IEEE
The virtual machine is a virtual model of a physical computer. Virtualization technology supports to generate multiple Virtual Machines (VMs) on a physical server; every VM is generated with their private OS and application. VM is a fundamental unit of cloud computing that is used to execute various types of applications successfully in a cloud environment. The propagation time and the response time are increased according to the distance between the customer region and the cloud server. Therefore, to solve the above-mentioned issue and for the efficient cloud services, the VM scheduling approach is necessary to manage the distance, resources and to reduce the propagation time and response time. In this paper, the Distance Aware VM Scheduler is proposed for mapping the VMs to the physical core processors and to provide the resources dynamically from the nearest data center. The proposed Distance Aware VM Scheduler is used to provide the resources from the nearest data center; thereby it decreases the propagation time and response time. Therefore, the cloud services are provided quickly with improved performance. The motivation of the proposed Distance Aware VM Scheduler is to generate an efficient VM allocation policy to physical servers and provide the resources from the nearest data center.
G. Justy Mirobi and L. Arockiam
IEEE
The Service Level Management [SLM] is the process of managing the cloud resources and services. Also, it is the process of managing and deploying the recourses, providing the services based on demand, control the service, monitor the service and report the service. The Service Level Management defines the process of allocating the resources, managing the resources, SLA negation, controlling the service, reporting and monitoring the service levels with the predefined standard service parameters. In cloud computing, the effective cloud service is considered that providing the required adequate service as mentioned in the SLA, and quickly redress the issues for the satisfaction of the customers. The capacity of an entity is to provide and maintain the appropriate service level which heavily dependent on building the committed service and managing the service levels. Operationally, SLM can be a difficult process because the IT management service focus towards the technology centric measurements which is specific to respective domain. Commonly in cloud computing the customers do not hire the physical components, logical infrastructure or applications from the authorized one. At the same time, avoiding the capital expenditure by the leasing usage through the third party provider. The leasing usage can impact on costing techniques by block time, remote access or sharing the time. But the service consumption plans for payment are based on utilization of resources or the subscription rates mentioned in the provided business model. Hence, SLM is the process of controlling the service levels and negotiating the SLA. In services, the entities that highly depend on the IT service management system, process, procedure and task for ensuring that whether the defined Quality of service is provided or not, as well as the financial expectations are fulfilled for the selected configuration.
G. Justy Mirobi and L. Arockiam
IEEE
Cloud computing is an internet based computing, providing the on demand services through the internet such as servers, storage disk, different platforms and applications to any business level company or organization. The cloud computing services are `Pay based on usage' based on the agreement between Service Provider of the Cloud and customer. Service Level Agreement(SLA) is a contract, contracted between provider of the service and the third party such as purchaser of service, dealer(agent) or monitor(agent), where service is formally defined. Practically the term Service Level Agreement(SLA) is used to mention the delivery period of the contracted service, and to evaluate the performance of service. The cloud computing is a recent technology providing numerous services for critical commercial applications, trustworthy and flexible mechanisms for managing the contracts. Therefore SLA is very important for conducting the cloud business in a smooth way. This paper presents an overview of SLA, benefits of cloud computing, the necessity of cloud computing, the importance of SLA, classification of Service Level Agreement, SLA-based cloud framework of cloud computing.