Intelligent query processing from biotechnological database using co-operating agents based on FIPA standards and hadoop, in a secure cloud environment

Author(s):  
R. Geetha ◽  
K. L. Shunmuganathan
2017 ◽  
Vol 256 ◽  
pp. 5-12 ◽  
Author(s):  
Jinhyun Ahn ◽  
Jae-Hong Eom ◽  
Sejin Nam ◽  
Nansu Zong ◽  
Dong-Hyuk Im ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak

IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud-based, geo-distributed, edge-based, and edge-cloud-based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.


2015 ◽  
Vol 115 (10) ◽  
pp. 1-3
Author(s):  
Lokesh. V ◽  
Anandajayam. P ◽  
Shanmugasundaram.S Shanmugasundaram.S

Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey

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, 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 using workflowsim simulator in JAVA.


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