LBBSRT: An efficient SDN load balancing scheme based on server response time

Zhong, Hong; Fang, Yaming

doi:10.1016/j.future.2016.10.001

Cited by 63 publications

(29 citation statements)

References 10 publications

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“…Network congestion usually caused packet loss. 36,49,54,55 3 | RELATED WORK In this section, we describe the number of existing survey articles in SDN and determine some defects of them. 6,50 Throughput: During a given period of time, the amount of data that has been correctly transmitted from one place to another is called throughput.…”

Section: Parameters Of Load Balancingmentioning

confidence: 99%

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

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The growth of the networks has difficult network management. Recently, a concept called software-defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load-balancing problem can be solved using many different natureinspired meta-heuristic techniques because it has the NP-complete nature.Hence, for solving load balancing problem in SDN, nature-inspired metaheuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature-inspired meta-heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature-inspired meta-heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.

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Section: Parameters Of Load Balancingmentioning

confidence: 99%

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

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show abstract

“…These are collected by the switch's ports traffic collector and server's response time collector subcomponents. In line 7, the weighted average metric for each server is calculated using (1). Finally, the most appropriate server with the minimum value of weighted average in the server farm is selected, and the appropriate rules for this flow are injected to the switch (line 8).…”

Section: The Best Server Selector Componentmentioning

confidence: 99%

“…The historical data include the recent server response time and switch's port traffic extracted using Algorithms 1 and 2 and stored in the database. To allocate a new flow, which enters a switch, to the best server, m recent response times for each server and m recent switch's port traffic are read from the database according to (1). The best server is selected to process the new flow.…”

Section: The Best Server Selector Componentmentioning

confidence: 99%

“…Software‐defined Networking (SDN) [,] is a new paradigm in networking, which provides manageable, scalable, and cost‐effective network architecture. Software‐Defined Networking decouples a network's control plane from the data plane; therefore, the data plane includes only forwarding devices and the control plane is implemented in a logically centralized entity called controller (or network operating system).…”

Section: Introductionmentioning

confidence: 99%

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SD‐WLB: AnSDN‐aided mechanism for web load balancing based on server statistics

2019

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Software‐defined networking (SDN) is a modern approach for current computer and data networks. The increase in the number of business websites has resulted in an exponential growth in web traffic. To cope with the increased demands, multiple web servers with a front‐end load balancer are widely used by organizations and businesses as a viable solution to improve the performance. In this paper, we propose a load‐balancing mechanism for SDN. Our approach allocates web requests to each server according to its response time and the traffic volume of the corresponding switch port. The centralized SDN controller periodically collects this information to maintain an up‐to‐date view of the load distribution among the servers, and incoming user requests are redirected to the most appropriate server. The simulation results confirm the superiority of our approach compared to several other techniques. Compared to LBBSRT, round robin, and random selection methods, our mechanism improves the average response time by 19.58%, 33.94%, and 57.41%, respectively. Furthermore, the average improvement of throughput in comparison with these algorithms is 16.52%, 29.72%, and 58.27%, respectively.

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“…Load balancing can be performed in two ways namely, distributed and nondistributed load balancing. In distributed balancing, load from the overloaded virtual machine gets distributed to the other virtual machines, whereas in the non-distributed load balancing, only one virtual machine is involved in load balancing [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Polepally¹,

Chatrapati²

2017

IJIES

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Cloud computing is a trending topic in the field of science and technology since the internet dependent services have been growing rapidly. In this environment, there are a lot of immense infrastructures and resources to satisfy the internet users. When a large number of service requests reach at a particular time, load balancing becomes a necessity. Load balancing involves the effective migration of the resources from the loaded physical machine to the other physical machine. For the effective migration, a method named Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM) has been proposed that uses the gravitational concepts for performing the frequency-based velocity computations. MEGSA algorithm is the integration of the gravitational search algorithm and exponential weighted moving average theory. Also, the qualityof-Service (QoS) constraints considered for VM migration are migration cost, migration time, resource usage and energy. Simulation of the proposed method and the comparison of the results obtained, with the traditional methods like Ant-Colony Optimization (ACO), Gravitational Search Algorithm (GSA) and Exponential Gravitational Search Algorithm (EGSA) is performed. The proposed method is found to achieve an optimum migration with a minimum energy at a rate of 0.26 and minimum migration cost at a rate of 0.015.

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LBBSRT: An efficient SDN load balancing scheme based on server response time

Cited by 63 publications

References 10 publications

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

SD‐WLB: AnSDN‐aided mechanism for web load balancing based on server statistics

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

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