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IMPULSE_TECHNOLOGY

Education

Impulse Technologies
Beacons U to World of technology
044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
A Distributed Control Law for Load Balancing in Content Delivery
Networks
Abstract
In this paper, we face the challenging issue of defining and implementing an
effective law for load balancing in Content Delivery Networks (CDNs). We base
our proposal on a formal study of a CDN system, carried out through the
exploitation of a fluid flow model characterization of the network of servers.
Starting from such characterization, we derive and prove a lemma about the
network queues equilibrium. This result is then leveraged in order to devise a novel
distributed and time-continuous algorithm for load balancing, which is also
reformulated in a time-discrete version. The discrete formulation of the proposed
balancing law is eventually discussed in terms of its actual implementation in a
real-world scenario. Finally, the overall approach is validated by means of
simulations.

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An Efficient Distributed Control Law for Load Balancing in Content Delivery N...

IJMER

Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.

Cloud Computing Load Balancing Algorithms Comparison Based Survey

INFOGAIN PUBLICATION

Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.

Modified Active Monitoring Load Balancing with Cloud Computing

ijsrd.com

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International Journal of Engineering Research and Development (IJERD)

IJERD Editor

Stochastic modeling and performance analysis of migration enabled and error-p...

ieeepondy

Cloud computing is a new technology that brings new challenges to all organizations around the world. Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development of the scale and number of applications, this access is often threatened by overload. Therefore, this paper our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud computing to improve performance for end-user. We have simulated the proposed algorithm with the CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the cloud data center.

PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...

IJCNCJournal

Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

DYNAMIC SDN CONTROLLER PLACEMENT BASED ON DEEP REINFORCEMENT LEARNING

josephjonse

Dynamic SDN Controller Placement based on Deep Reinforcement Learning

josephjonse

The stability and the predictability of a computer network algorithm's performance are as important as the main functional purpose of networking software. However, asserting or deriving such properties from the finite state machine implementations of protocols is hard and, except for singular cases like TCP, is not done today. In this paper, we propose to design and study run-time environments for networking protocols which inherently enforce desirable, predictable global dynamics. To this end we merge two complementary design approaches: (i) A design-time and bottom up approach that enables us to engineer algorithms based on an analyzable (reaction) flow model. (ii) A run-time and top-down approach based on an autonomous stack composition framework, which switches among implementation alternatives to find optimal operation configurations. We demonstrate the feasibility of our self-optimizing system in both simulations and realworld Internet setups

Designing Run-Time Environments to have Predefined Global Dynamics

IJCNCJournal

In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.

Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud Computing

Eswar Publications

Cloud computing is growing in popularity and it has been continuously updated with more improvements. Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks whenever a new part is added to a certain stable system. In this paper, we consider how existing load balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with an auto scaler and a load balancer and implementing simulations based on the constructed model. Also based on the results from the computer simulations we proposed about choosing load balancers for subscribed cloud system with auto scaling service.

Load Balancing in Auto Scaling Enabled Cloud Environments

neirew J

LOAD BALANCING IN AUTO SCALING-ENABLED CLOUD ENVIRONMENTS

Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one. In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is JAVA based simulator.

Load Balancing in Cloud using Modified Genetic Algorithm

IJCSIS Research Publications

Rui aguiarphd proposal

KellyCheah

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...

IJCNCJournal

This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation platform and all details relevant to all software used are described step-by-step in detail. The main performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95% confidence interval. According to the simulation results, it is obvious that our proposed class-based adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP) admittable with QoS while the other evaluation metrics are maintained at the same level.

A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...

IJCNCJournal

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