Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Utilization of map-reduce for parallelization of resource scheduling using MPI: PRS(2011) Thomas, L.; Annappa, B.Scheduling for speculative parallelization is a problem that remained unsolved despite its importance [2]. In the previous work scheduling was done based on Fixed-Size Chunking (FSC) technique which needed several'dry-runs' before an acceptable finalized chunk size that will be scheduled to each processors is found. There are many other scheduling methods which were originally designed for loops with no dependences, but they were primarily focused in the problem of load balancing. In this work we address the problem of scheduling tasks with and without dependences for speculative execution. We have found that a complexity between minimizing the number of re-executions and reducing overheads can be found if the size of the scheduled block of iterations is calculated at runtime. We introduce here a scheduling method called Parallelization of Resource scheduling (PRS) in which we first analyze the processing speed of each worker based on that further division of the actual task will be done. The result shows a 5% to 10% speedup improvement in real applications with dependences with respect to a carefully tuned PRS strategy. Copyright © 2011 ACM.Item Capturing Node Resource Status and Classifying Workload for Map Reduce Resource Aware Scheduler(Springer Verlag service@springer.de, 2015) Mude, R.G.; Betta, A.; Debbarma, A.There has been an enormous growth in the amount of digital data, and numerous software frameworks have been made to process the same. Hadoop MapReduce is one such popular software framework which processes large data on commodity hardware. Job scheduler is a key component of Hadoop for assigning tasks to node. Existing MapReduce scheduler assigns tasks to node without considering node heterogeneity, workload type, and the amount of available resources. This leads to overburdening of node by one type of job and reduces the overall throughput. In this paper, we propose a new scheduler which capture the node resource status after every heartbeat, classifies jobs into two types, CPU bound and IO bound, and assigns task to the node which is having less CPU/IO utilization. The experimental result shows an improvement of 15-20 % on heterogeneous and around 10 % of homogeneous cluster with respect to Hadoop native scheduler. © Springer India 2015.Item Determination of task scheduling mechanism using computational intelligence in Cloud Computing(Institute of Electrical and Electronics Engineers Inc., 2016) George, N.; Chandrasekaran, K.; Binu, A.Cloud Computing delivers computational services through the internet. The services availed can vary according to the user requirements. The services are basically provided using virtualization technique. One of the services that are provided is the computational services for the client tasks. The client provides the Service Provider with various sized tasks that need to be executed. The execution of tasks is done using the resources present within the Provider of the services. The service provider checks for available resources, and allocates the jobs to these resources in such a manner as to minimize execution time, and various other factors that affect the performance of the Cloud. The process of allocating resources to the tasks is known as scheduling, and various scheduling mechanisms are present. A single scheduling strategy may not be always optimal in performing scheduling. In this paper, an improved mechanism for choosing the scheduling strategy is explained, which aims at addressing the problems associated with choosing the right scheduling mechanism according to the previously exhibited performances. Experimental results demonstrate the importance of using such a mechanism in selecting the right scheduling strategy. © 2015 IEEE.Item An objective study on improvement of task scheduling mechanism using computational intelligence in cloud computing(Institute of Electrical and Electronics Engineers Inc., 2016) George, N.; Chandrasekaran, K.; Binu, A.Cloud Computing facilitates delivery of various types of computational services through the internet. These services can be availed according to the user demand. The resource scarcity problems within the Service Providers are met using Virtualization technique, which allows scalability of resources and thereby helps to meet the client requirements. Allocation of resources to client tasks is an issue that is being addressed for a long time. Due to the increased complexity in the area, there has not yet been a perfect scheduling mechanism. Practices have been done profusely in order to find solutions for scheduling that nears optimality. A single scheduling mechanism may not always give the expected outcome. The task scheduling mechanisms are designed in a manner as to optimize some metrics related to the Cloud. This paper overviews various literature associated with task scheduling and resource scheduling in Cloud Computing. An examination of the techniques is done and a proposal is made, which will allow to further improve the scheduling mechanism. © 2015 IEEE.Item Software Based Solution for Efficient Energy Utilization of an IoT Node PSoC6-Dual Core Using a Scheduler(Institute of Electrical and Electronics Engineers Inc., 2022) Geetha, V.; Shrinidhi, M.Embedded systems are an integral part of today's time with a wide range of specific applications. Increasing design requirements has emphasized the need to focus on sectors such as power consumption, flexibility, cost, performance, and robustness. Power consumption is the major issue in embedded systems that needs to be addressed to sustain long term functionality of the devices. PSoC 6 MCU is a System-on-chip, specifically designed for Internet of Things with enhanced computation and communication capabilities. However, the task to be executed on dual core need scheduling to further improve on energy efficiency. This work proposes a solution based on queuing techniques for task scheduling based on complexity of the job. © 2022 IEEE.