The main aim of long term evolution is to provide very high rate for downlink and uplink, better multimedia services and more user flexibility than the current cellular networks. There is a very important role of reso...
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CPU scheduling plays a vital role in Operating Systems for undergraduate students. Understanding the CPU scheduling concepts and algorithms will positively affect students' further study on the course. However, te...
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CPU scheduling plays a vital role in Operating Systems for undergraduate students. Understanding the CPU scheduling concepts and algorithms will positively affect students' further study on the course. However, teaching and learning CPU scheduling algorithms using conventional lectures and textbooks is faced with difficulties by many teachers and students. First, textbooks illustrate the CPU scheduling algorithms in an incomplete and unclear manner. Second, students solve problems manually. They don't receive any immediate feedback on their solutions. Third, due to time restriction, the teacher has to select a few small problems. To overcome these problems, we developed a simple visual educational simulator, which can be used as an efficient tool for teaching and learning CPU scheduling algorithms for one processor. Although this simulation tool is similar to others, it has its own unique features. In this paper, the educational impact, functional capabilities and features for this simulator are discussed in details.
Cloud computing delivers a computing environment where different resources are delivered as a service to the customer or multiple tenants over the internet. Task scheduling is an essential and most important part in a...
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ISBN:
(纸本)9781467397452
Cloud computing delivers a computing environment where different resources are delivered as a service to the customer or multiple tenants over the internet. Task scheduling is an essential and most important part in a cloud computing environment. The task scheduling mainly focuses to enhance the efficient utilization of resources and hence reduction in task completion time. Task scheduling is used to allocate certain tasks to particular resources at a particular time instance. Many different techniques have been proposed to solve the problems of task scheduling. Task scheduling improves the efficient utilization of resource and yields less response time so that the execution of submitted tasks takes place within a possible minimum time. This paper discusses about the study of various scheduling algorithms in a cloud computing environment.
Network navigation is a promising paradigm for providing accurate location-awareness in wireless environments, where mobile nodes estimate their locations based on inter-and intra-node measurements. In the presence of...
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ISBN:
(纸本)9781509013289
Network navigation is a promising paradigm for providing accurate location-awareness in wireless environments, where mobile nodes estimate their locations based on inter-and intra-node measurements. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements. Therefore, it is crucial to design efficient scheduling algorithms for selecting node pairs at different times for inter-node measurements. This paper develops a framework for the design of scheduling algorithms based on random access for network navigation. The proposed algorithms are suitable for practical operation of wireless navigation networks due to their distributed nature, and the optimized access probabilities of the agents lead to significant performance improvement.
Hadoop is an open-source framework developed by Apache software foundation. It works on large datasets, as the data is been stored and processed across cluster in distributed environment. It consist of two components ...
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ISBN:
(纸本)9781509007745
Hadoop is an open-source framework developed by Apache software foundation. It works on large datasets, as the data is been stored and processed across cluster in distributed environment. It consist of two components - HDFS and MapReduce. HDFS is used for storing the data and MapReduce is used to process those stored datasets. In current era large amount of data is generated by many websites, the data generated is enormous which is simply stated as "big data". These datasets are then processed in Hadoop framework. As the processing is done parallely in Hadoop framework appropriate scheduling of the resources is required to process large datasets in-order to gain better performance. Mainly scheduling algorithms are used to minimize completion time of a parallel application. Among users the resource allocation will be guaranteed by schedulers. In this paper we study about the performance of various scheduling algorithms like fair, fifo, capacity of Hadoop platform.
Cloud computing enables users to access computing resources over internet. Organizations that manage and provide these resources are called as cloud service providers. There are several such providers in the market of...
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ISBN:
(纸本)9781450347563
Cloud computing enables users to access computing resources over internet. Organizations that manage and provide these resources are called as cloud service providers. There are several such providers in the market offering large range of services to their users and charge them only for what they have consumed. Workflow is of a set of interdependent tasks that need to be performed in a coordinated way to complete a job. Any business or scientific application can be modelled as a workflow. An effective scheduling algorithm will optimize the resource utilization and in turn the amount spent for executing the workflow. Due to the numerous advantages in cloud, workflow scheduling in cloud seems to be an eminent way to minimize the execution time and cost. There are plenty of existing algorithms and many new algorithms are in the row for workflow scheduling in cloud. A complete understanding of these algorithms will help future researchers to propose novel ideas. Hence this paper provides an analysis of some dominant workflow scheduling algorithms and their performance in standard workflow data sets. The analysis is carried out in WorkflowSim environment with the identical set of Virtual Machines (VM) having similar configuration.
On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data it...
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ISBN:
(纸本)9781467399531
On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.
This work studies online scheduling algorithms for buffer management, develops new algorithms, and analyzes their performances. Packets arrive at a release time r, with a non-negative weight w and an integer deadline ...
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ISBN:
(数字)9783319388519
ISBN:
(纸本)9783319388502;9783319388519
This work studies online scheduling algorithms for buffer management, develops new algorithms, and analyzes their performances. Packets arrive at a release time r, with a non-negative weight w and an integer deadline d. At each time step, at most one packet is scheduled. The modified greedy (MG) algorithm is 1.618-competitive for the objective of maximizing the sum of weights of packets sent, assuming agreeable deadlines. We analyze the empirical behavior of MG in a situation with arbitrary deadlines and demonstrate that it is at a disadvantage when frequently preferring maximum weight packets over early deadline ones. We develop the MLP algorithm, which remedies this problem whilst mimicking the behavior of the offline algorithm. Our comparative analysis shows that, although the competitive ratio of MLP is not as good as that of MG, it performs better in practice. We validate this by simulating the behavior of both algorithms under a spectrum of parameter settings. Finally, we propose the design of three additional algorithms, which may help in improving performance in practice.
Most existing wireless networking solutions are best-effort and do not provide any delay guarantee required by important applications such as the control traffic of cyber-physical systems. Recently, Hou and Kumar prov...
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ISBN:
(纸本)9781467399531
Most existing wireless networking solutions are best-effort and do not provide any delay guarantee required by important applications such as the control traffic of cyber-physical systems. Recently, Hou and Kumar provided the first framework for analyzing and designing delay-guaranteed network solutions. While inspiring, their idle-time-based analysis appears to apply only to flows with a special traffic (arrival and expiration) pattern, and the problem remains largely open for general traffic patterns. This paper addresses this challenge by proposing a new framework that characterizes and achieves the complete delay-constrained capacity region with general traffic patterns in singlehop downlink access-point wireless networks. We first formulate the timely capacity problem as an infinite-horizon Markov Decision Process (MDP) and then judiciously combine different simplification methods to convert it to an equivalent finite-size linear program (LP). This allows us to characterize the timely capacity region of flows with general traffic patterns for the first time in the literature. We then design three timely-flow scheduling algorithms for general traffic patterns. The first algorithm achieves the optimal utility but suffers from the curse of dimensionality. The second and third algorithms are inspired by our MDP framework and are of polynomial-time complexity. Simulation results show that both achieve near-optimal performance and outperform other existing alternatives.
A novel algorithm is presented for quickly balancing responsibilities across a distributed tracking network that requires no explicit coordination between network members. Previous load balancing methods reliably achi...
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