Papers by Chinmaya Kumar Swain
International Journal of Computer Applications, 2013
The explosive growth of the textual information on the web in the past few decades has brought ra... more The explosive growth of the textual information on the web in the past few decades has brought radical change in human life. In the web, people share their opinions and views (sentiments) in many forms about products or services they are aware of. This creates a large collection of opinions and views in the form of texts, which needs to be analysed to know the efficacy of the product or service. Opinions are usually subjective expressions that describe person's sentiment, feelings towards the object or service. The sentiment can be positive or negative. This survey is a summary of the work on sentiment analysis, covering the new challenges which appear in sentiment analysis as compared to traditional fact based analysis. Currently there are four research challenges for sentiment analysis. Those are subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction. This survey discusses related issues of sentiment analysis and main approaches to those problems.
Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment
IEEE Systems Journal, 2021
Computing, 2020
The extensive use of cloud services in different domains triggers the efficient use of cloud reso... more The extensive use of cloud services in different domains triggers the efficient use of cloud resources to achieve maximum profit. The heterogeneous nature of data centers and the heterogeneous resource requirement of user applications create a scope of improvement in task scheduling. The resource requirements in terms of task constraints must be fulfilled for the tasks to be admitted to the system. Once a task admitted to the system, it may violate service level agreement and incurs penalty due to the disproportionate resource allocation at run time. The latency-sensitive and short-lived workloads need effective scheduling to gain more profit. In this work, we propose Heuristic of Ordering and Mapping for Constraint Aware Profit Maximization (HOM-CAPM) problem for efficient scheduling of tasks with constraints and deadlines to gain maximum profit. The HOM-CAPM approach considers estimation of task execution time in a heterogeneous environment, efficient task ordering, and profit-based task allocation to maximize the overall profit of the cloud system. To gain maximum profit the proposed heuristic considers two cases, (a) not allowing the tasks for execution if it expected to miss its deadline and (b) allowing the task which earns substantial profit even though it is expected to miss its deadline. The results of the extensive simulation using Google trace data as input show that our proposed HOM-CAPM approach generates more profit than other state-of-the-art approaches.
Computing, 2019
Cloud environment uses data center with a huge number of computational resources, and the probabi... more Cloud environment uses data center with a huge number of computational resources, and the probability of failing any of the resources increases with scale. Failures cause unavailability of services, which affects the reliability of the system. It is essential to consider the reliability issue for application deployment in the cloud, considering the failure of the resources. In this work, we address the reliability aware scheduling of tasks with hard deadlines in the cloud environment. We design, analyze and provide solutions for two special cases of the problem where (a) tasks have a common deadline on the machines with equal failure rate, and (b) tasks with equal execution time. For the general case of the problem, we propose two-phase heuristic approaches, one is the task ordering, and other is tasks mapping to machines. The performance of different task orderings and task mapping approaches is evaluated through simulation using synthetic and real traces. Based on the simulation result, the earliest due date ordering of tasks and mapping of the current task to the most reliable machine along with long task dropping performs better in general settings. We observe that task repetition and replication further improve the performance of the heuristics.
Interference Aware Scheduling of Real Time Tasks in Cloud Environment
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018
Cloud environment uses virtualization technology to increase the resource utilization and scalabi... more Cloud environment uses virtualization technology to increase the resource utilization and scalability. Due to the concurrent running of co-allocated tasks with virtual machines on top a physical machine, performance of some tasks suffers significantly because of interference. To improve the quality of service and reduce the service level agreement violation between user and cloud service provider, we presented an interference aware scheduling approach. Interference aware scheduling approach predicts the effect of interference for execution of a task with respect to the resource usage by co-scheduled tasks in a virtualized environment. This prediction model is being used to schedule tasks to achieve better quality of service in term of task guarantee ratio and priority guarantee ratio. We implemented and validated our approach and the results show that our approach performs significantly better than other approaches.
Efficient Welfare Maximization in Fog-Edge Computing Environment
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
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Papers by Chinmaya Kumar Swain