3,899 research outputs found

    Performance analysis of server selection schemes for Video on Demand servers

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    Web Services have gained considerable attention over the last few years. This is due to increase in use of the Internet which results in increased web traffic. Web servers find applications in E-commerce and Video-on-Demand(VoD) systems which have resulted in speedy growth of the web traffic. Therefore the concept of load balancer aimed to distribute the tasks to different Web Servers to reduce response times was introduced. Each request was assigned a Web Server decided by the load balancer in such a way that tasks were uniformly distributed among the available servers. Server selection algorithms are aimed to meet the QoS for interactive VoD.This thesis attempts to analyze the performance of FCFS, Randomized, Genetic algorithms and Heuristics algorithms for selecting server to meet the VoD requirement . Performance of these algorithms have been simulated with parameters like makespan and average resource utilization for different server models. This thesis presents an efficient heuristic called Ga-max-min for distributing the load among different servers. Heuristics like min-min and max-min are also applied to heterogeneous server farms and the result is compared with the proposed heuristic for VoD Servers. Ga-max-min was found to provide lower makespan and higher resource utilization than the genetic algorithm.Extensive simulations have been carried out by the simulator designed using MATLAB R2010a

    A Survey on Scheduling the Task in Fog Computing Environment

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    With the rapid increase in the Internet of Things (IoT), the amount of data produced and processed is also increased. Cloud Computing facilitates the storage, processing, and analysis of data as needed. However, cloud computing devices are located far away from the IoT devices. Fog computing has emerged as a small cloud computing paradigm that is near to the edge devices and handles the task very efficiently. Fog nodes have a small storage capability than the cloud node but it is designed and deployed near to the edge device so that request must be accessed efficiently and executes in time. In this survey paper we have investigated and analysed the main challenges and issues raised in scheduling the task in fog computing environment. To the best of our knowledge there is no comprehensive survey paper on challenges in task scheduling of fog computing paradigm. In this survey paper research is conducted from 2018 to 2021 and most of the paper selection is done from 2020-2021. Moreover, this survey paper organizes the task scheduling approaches and technically plans the identified challenges and issues. Based on the identified issues, we have highlighted the future work directions in the field of task scheduling in fog computing environment

    Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

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    Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

    Load Balancing in Cloud Computing Systems

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    Cloud computing" is a term, which involves virtualization, distributed computing, networking, software and web services. A cloud consists of several elements such as clients, datacenter and distributed servers. It includes fault tolerance, high availability, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services etc. Central to these issues lies the establishment of an effective load balancing algorithm. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. This technique can be sender initiated, receiver initiated or symmetric type (combination of sender initiated and receiver initiated types). Our objective is to develop an effective load balancing algorithm using Divisible load scheduling theorm to maximize or minimize different performance parameters (throughput, latency for example) for the clouds of different sizes (virtual topology depending on the application requirement)
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