Department of Computer Science and Information Systems

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Subject: search.filters.subject.Algorithm

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Now showing 1 - 9 of 9
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    Efficient Service Utilization in Cloud Computing Exploitation Victimization as Revised Rough Set Optimization Service Parameters
    (Elsevier, 2015) Gupta, Shashank
    Cloud computing is an effort in delivering resources as a service. In cloud computing setting the role of service supplier is split into two parts as Cloud Broker and repair suppliers. The Cloud Brokermanages cloud platforms and lease resources in keeping with a usage-based evaluation model. The repair suppliers rent resources from one or several infrastructure suppliers to serve the top users. The plan of action of choosing a Cloud Service supplier is evaluated upon the premise of Which-Cloud Provider-Provides-What. Selecting qualification applicableService supplier is more durable as results of all CSPs cannot be counted for all non-stop Service. The aim of this analysis work is to traumatize the programming of the requests on the premise of twelve parameters that got higher best-known to comprehend the simplest best ways that of cloud service supplier allotment to the users. Apart from the implementation and compression purpose taken identical four parameters that unit of measure gift in ROSP recursive program. It uses rough math's to urge the mathematical model inside that the algorithmic program Rough set improvement Service Parameters is created on the premise of the economical resource Utilization in Cloud Computing practice Revised ROSP programming Technique. Then the algorithm is enforced within the cloud machine within that cloudlets, datacenters, and cloud brokers unit of measure wont to perform the algorithms. Some integral packages of Cloud machine unit of measure won’t to simulate the strategy. The strategy is completed combined at a lower place internet Beans and Sql. The results once the implementation of the ERROSP algorithm got unit of measure on high of theROSP algorithm in time taken and mainframe utilization.
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    A Survey of Deadlock Detection Algorithms
    (Springer, 2022-03) Dua, Amit
    A deadlock is a situation in which two processes which share the identical resource are dependent on one another and prevents each other from accessing resources, which results in both programs ceasing to function. The formation of deadlock reduces the system efficiency. Thus, to avoid performance degradation due to deadlock, it is necessary that a system should be exempted from deadlock or that deadlocks be quickly diagnosed and abolished. We propose a survey on different deadlock identification and resolution techniques. Various algorithms for identifying deadlocks and handling strategies have been proposed till now. In this paper, we represent a comparative study among different strategies used to diagnose and resolve deadlocks in various environments using different parameters.
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    Scheduling Algorithm for Asymmetric Multicore Platforms using Non-uniform Laxity based Clustering and Duplication
    (European Journal of Scientific Research, 2012-10) K., Pradheep Kumar
    In this paper, a new scheduling algorithm using task clustering and non-uniform laxity is proposed. This algorithm forms task clusters after analyzing the task dependencies. For each task cluster comprising of a parent and dependent tasks, a cluster parameter is computed. Based on this parameter, a rank is assigned to each cluster. The clusters are scheduled in the order of their rank, so as to minimize the communication cost and reduce the utilization of slack resources. Clusters are scheduled as far as possible on the same core. If a cluster cannot be scheduled completely on the same core, the parent task is either migrated or duplicated depending on the associated costs involved. The algorithm has been tested using 15 different programs as tasks.
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    Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications
    (IGI Global, 2019) K., Pradheep Kumar
    In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively
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    A Context-Based Performance Enhancement Algorithm for Columnar Storage in MapReduce with Hive
    (IGI Global, 2013) Sharma, Yashvardhan
    To achieve high reliability and scalability, most large-scale data warehouse systems have adopted the cluster-based architecture. In this context, MapReduce has emerged as a promising architecture for large scale data warehousing and data analytics on commodity clusters. The MapReduce framework offers several lucrative features such as high fault-tolerance, scalability and use of a variety of hardware from low to high range. But these benefits have resulted in substantial performance compromise. In this paper, we propose the design of a novel cluster-based data warehouse system, Daenyrys for data processing on Hadoop – an open source implementation of the MapReduce framework under the umbrella of Apache. Daenyrys is a data management system which has the capability to take decision about the optimum partitioning scheme for the Hadoop's distributed file system (DFS). The optimum partitioning scheme improves the performance of the complete framework. The choice of the optimum partitioning is query-context dependent. In Daenyrys, the columns are formed into optimized groups to provide the basis for the partitioning of tables vertically. Daenyrys has an algorithm that monitors the context of current queries and based on the observations, it re-partitions the DFS for better performance and resource utilization. In the proposed system, Hive, a MapReduce-based SQL-like query engine is supported above the DFS.
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    Designing self-adaptive websites using online hotlink assignment algorithm
    (ACM Digital Library, 2009-12) Goyal, Navneet; Goyal, Poonam
    An online hotlink assignment algorithm is proposed for designing adaptive websites. The objective is to reach desired pages on a website in minimum number of clicks, thereby reducing the load on the web server. As a consequence, the traffic on the internet is also reduced. The hotlinks are assigned based on the frequency of access of pages. We model a website as a single source directed graph. Optimal hotlink assignment problem is NP-hard for general graphs. The website graph is reduced to a Breadth First Search (BFS) tree which maintains the semantic relationships between web pages. The proposed online algorithm can place at most k hotlinks per page with a maximum of l hotlinks on the entire website, where k«l. The input stream is simulated using the Zipf distribution. The results presented in the paper compare the performance of the online algorithm with the optimal offline algorithm.
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    A concurrent k-NN search algorithm for R-tree
    (ACM Digital Library, 2015-10) Goyal, Navneet; Goyal, Poonam; Challa, Jagat Sesh
    k-nearest neighbor (k-NN) search is one of the commonly used query in database systems. It has its application in various domains like data mining, decision support systems, information retrieval, multimedia and spatial databases, etc. When k-NN search is performed over large data sets, spatial data indexing structures such as R-trees are commonly used to improve query efficiency. The best-first k-NN (BF-kNN) algorithm is the fastest known k-NN over R-trees. We present CBF-kNN, a concurrent BF-kNN for R-trees, which is the first concurrent version of k-NN we know of for R-trees. CBF-kNN uses one of the most efficient concurrent priority queues known as mound. CBF-kNN overcomes the concurrency limitations of priority queues by using a tree-parallel mode of execution. CBF-kNN has an estimated speedup of O(p/k) for p threads. Experimental results on various real datasets show that the speedup in practice is close to this estimate.
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    Spatial Locality Aware, Fast, and Scalable SLINK Algorithm for Commodity Clusters
    (IEEE, 2016) Goyal, Navneet; Goyal, Poonam
    Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. In this paper, we parallelize an efficient implementation of SLINK algorithm to leverage a commodity cluster of multicore workstations. We present, dGridSlink, a distributed algorithm, which outperforms the best existing parallel solution in literature for all the real datasets considered. We also propose a hybrid parallel algorithm hGridSLINK for a cluster of multicore nodes. The proposed parallel algorithms are scalable and can cluster (several) millions of data points efficiently, without compromising the quality of clustering.
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    Energy aware real time scheduling algorithm for mixed task set
    (IEEE, 2013-09) Mohan, Sudeept
    Energy consumption is one of the major limiting factors of battery operated real-time systems. Optimizing energy consumption without affecting performance and schedulability is the major topic to be researched. In this paper, an energy aware real time scheduling algorithm is proposed for a system with mixed task set consisting of both periodic and aperiodic tasks. Dynamic energy reduction techniques like Dynamic Voltage and Frequency Scaling (DVFS) is used for energy optimization without affecting the responsiveness of aperiodic tasks. Performance of the proposed algorithm is compared with non-DVS algorithm. Experimental evaluation reveals that the proposed algorithm saves 54.44% of energy in comparison with non-DVS algorithms. It achieves this with no degradation in responsiveness of the aperiodic tasks.