Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Load Balancing is essential for efficient operations indistributed environments. As Cloud Computing is growingrapidly and clients are demanding more services and betterresults, load balancing for the Cloud has become a veryinteresting and important research area. Many algorithms weresuggested to provide efficient mechanisms and algorithms forassigning the client's requests to available Cloud nodes. Theseapproaches aim to enhance the overall performance of the Cloudand provide the user more satisfying and efficient services. Inthis paper, we investigate the different algorithms proposed toresolve the issue of load balancing and task scheduling in CloudComputing. We discuss and compare these algorithms to providean overview of the latest approaches in the field.

A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms

With the growth in computing technologies, cloud computing has added a new paradigm to user services that allows accessing Information Technology services on the basis of pay-per-use at any time and any location. Owing to flexibility in cloud services, numerous organizations are shifting their business to the cloud and service providers are establishing more data centers to provide services to users. However, it is essential to provide cost-effective execution of tasks and proper utilization of resources. Several techniques have been reported in the literature to improve performance and resource use based on load balancing, task scheduling, resource management, quality of service, and workload management. Load balancing in the cloud allows data centers to avoid overloading/underloading in virtual machines, which itself is a challenge in the field of cloud computing. Therefore, it becomes a necessity for developers and researchers to design and implement a suitable load balancer for parallel and distributed cloud environments. This survey presents a state-of-the-art review of issues and challenges associated with existing load-balancing techniques for researchers to develop more effective algorithms.

Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey

In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.

A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment

Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Cloud computing stores the data and distributed resources in the open environment. The amount of data storage increases quickly in open environment. So, load balancing is a main challenge in cloud environment. Load balancing is helped to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improve the performance of the system. Many existing algorithms provide load balancing and better resource utilization. There are various types load are possible in cloud computing like memory, CPU and network load. Load balancing is the process of finding overloaded nodes and then transferring the extra load to other nodes.

Load Balancing in Cloud Computing

Due to extensive coverage and domains of cloud computing-based architecture, most users saves and access highly similar data because of user habits and available resources. Supposing the data stored in the storage devices is of high duplication and similarity, the system administrator cannot guarantee that every node (peer) in the cloud computing system can maintain the best status for data integration. Moreover, when the number of nodes keeps increasing, the number of files will be multiplied, which results to not only the system administrator's heavy workload, but also the waste of hardware resources. Finally, the flexibility and quickness of cloud storage system is gone, and what left is a lot of duplicate and redundant data. Therefore, in order to avoid the system burden caused by duplicate data, we propose novel data center management architecture: Index Name Server (INS), which integrates de-duplication and access point selection optimization techniques to enhance the performance of the cloud storage system. With the attempt to attain load balancing, we use several specific INS parameters to dynamically monitor the parameters, like IP information and busy level index, to avoid the network congestion or the long waiting time during data transmission.

Dynamic load balancing mechanism based on cloud storage

In Wireless Access in Vehicular Environments (WAVE), the terminal devices are usually highly mobile vehicles or transportation tools, which handoff much more frequently than those in wireless networks However, the quality of network services, like VoIP and multimedia streaming, will be seriously influenced by too frequent handoff or too long handoff latency As a result, traditional handoff mechanisms no longer satisfy the need of vehicular communications At present, most existing fast handoff mechanisms are based on the signal strength of the APs, which does not suit vehicular communications fully Generally speaking, the regional traffic in Vehicular Ad-hoc Networks (VANETs) keep changing and varying For example, on busy roads or in rush hours, the increase of number of vehicles may lead to the network congestion and degrade the performance of vehicular communications substantially Therefore, this paper presents the QualityScan scheme, an efficient pre-scanning method to improve the handoff performance in VANETs With the attempt to reduce the handoff latency and maintain the load balance among the APs, our proposed scheme regularly collects the loading states of the nodes by the pre-established AP controller (APC), and predicts the network traffic of the next moment Consequently, Mobile Nodes (MNs) can select the most suitable AP for the best QoS according to the parameters received by passive scanning, while more QoS parameters should participate in cooperation The simulation result proves that our proposed scheme not only maintains the QoS and the load balance among the nodes, but also further enhances the handoff performance in VANETs

Hung Lin Chan

Papers

Dynamic load balancing mechanism based on cloud storage

An efficient pre-scanning scheme for handoff in Cooperative Vehicular Networks