L. D. Dhinesh Babu

Bio: L. D. Dhinesh Babu is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Social network analysis. The author has an hindex of 9, co-authored 32 publications receiving 734 citations.

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Abstract: Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.

Versatile time-cost algorithm VTCA for scheduling non-preemptive tasks of time critical workflows in cloud computing systems

Abstract: In cloud computing environments, resources and infrastructure are provided as a service over internet on demand. The users are interested in reducing the service cost provided by the cloud service providers. Scheduling tasks of workflows play a vital role in determining performance of cloud computing systems. Workflows have many tasks in it and are interdependent on each other. Time critical workflows comprise of a collection of tasks which should be completed as early as possible so that other workflows get its turn. The budget involved in executing the time critical tasks is very high. The execution cost increases whenever we try to reduce the execution time. In this paper, we propose a method called versatile time-cost algorithm VTCA to schedule time critical workflows with minimum cost. VTCA will schedule the tasks to complete in earliest possible time as well as optimise the cost involved in resource provisioning. The results of experiments conducted using CloudSim simulator show that our scheduling policy minimises the completion time of workflows than other existing algorithms like min-min and fair max-min by 5% to 30% and it also reduces the costs by 5% to 35%.

A decision-based pre-emptive fair scheduling strategy to process cloud computing work-flows for sustainable enterprise management

Abstract: In cloud computing, clients comply a policy of pay-as-you go, i.e., they only pay for the resources they use. So, the processing power of the clouds has to be optimised to reduce the cost at client's side. Using the resources optimally ensures enterprise sustainability of cloud service providers. Workflow is a set of tasks that are interdependent on each other. Scheduling these workflows is one of the most important challenges to optimally utilise the cloud resources and ensure better quality of service (QoS) to clients. Existing works on scheduling in cloud computing mainly focus on scheduling independent tasks rather than (inter)dependent tasks. In this paper, we propose a strategy to schedule dependent tasks called pre-emptive fair scheduling algorithm (PFSA). This is fair scheduling strategy also aims to ensure higher utilisation of virtual machines (VMs) by reducing the idle time and to minimise the number of times a pre-empted task is submitted to the virtual machine. In both cases, this algorithm tries to effectively reduce the overall processing time of dependent tasks at virtual machine, thus minimising the cost involved in processing of tasks. This economically viable decision-based strategy will be helpful for cloud service providers in ensuring sustainability.

An Analysis of Security Related Issues in Cloud Computing

Abstract: Over the past two decades, the scenario in the computing world has evolved from client-server to distributed systems and then to central virtualization called as cloud computing Computing world is moving towards Cloud Computing and it remains as buzzword of the current era Earlier, users had complete control over their processes and data stored in personal computer where as in cloud, cloud vendor provides services and data storage in remote location over which the client has no control or information As application and data processing takes place in public domain outside the designated firewall, several security concerns and issues arise The main objective of the paper is to provide an overall security perspective in cloud Computing and highlight the security concerns and other issues The paper also highlights few technical security issues in cloud computing

Trust management for the Semantic Web

Abstract: Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

User Modeling and User-Adapted Interaction

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