Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Jena, Tamanna; Mohanty, Jnyana Ranjan

doi:10.11591/ijece.v6i4.9956

Cited by 5 publications

(8 citation statements)

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“…(3) For (t � 0; t < G/2; t++) (4) { (5) If (t. Bestfitness � (t− 1). Bestfitness) (6) { (7) cat � cat− − ; (8) } (9) If (cat � 0) (10) e first third variation; (11) Else (12) Continue circulation; (13) } ALGORITHM 2: Catastrophic operation. 6 Scientific Programming better average fitness.…”

Section: Discussionmentioning

confidence: 99%

“…Task scheduling methods in the cloud center can be divided into heuristic algorithms (such as RR and SJF), metaheuristic algorithms (based on biological incentives and swarm intelligence), and hybrid task scheduling algorithms [9]. In the scheduling process, various performance-based performance indicators such as system utilization, execution time, load balance, network communication cost, delay, and the like are used [10].…”

Section: Introductionmentioning

confidence: 99%

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A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. e algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. e experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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“…Cloud computing data centers offer thousands of physical servers networked via high bandwidth network infrastructures that communicate with one another to provide highly available and flexible services. 2 Although cloud computing services suggest many benefits, there are also many issues and open research problems to provide these services such as load balancing solutions, [3][4][5][6][7] security challenges, [8][9][10][11] task scheduling, [12][13][14] and high availability/reliability challenges. [15][16][17][18][19] The large-scale heterogeneity nature of cloud services leads to frequent failures in these systems.…”

Section: Introductionmentioning

confidence: 99%

Dependability analysis for characterizing Google cluster reliability

Mesbahi

Rahmani

Hosseinzadeh

2019

Int J Communication

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Cloud solutions are emerging as a new suitable way of transforming traditional IT data centers to highly available and reliable computing resources for hosting critical applications and data. However, software and hardware failures are a common problem in cloud datacenters that can lead to harmful damages. In this paper, we analyze the physical server failures in the Google cloud datacenter. We study the Google cluster properties to investigate the relationship among physical servers' failure rate and jobs failure events. The failure rate of Google cluster executed jobs and servers is taken into consideration during a 29-day period. We present a reliability model for Google cluster physical machines using the continuous time Markov chains according to this observation. We attempt to analyze the obtained model through SHARPE software packages to improve the understanding of failure events in the Google cloud cluster. We also explore the cluster availability based on parameters like steady-state availability, steady-state unavailability, mean time to failure, and mean time to repair in the Google cluster.

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“…Other related research in terms of maintaining quality and network performance presented in [8]- [9]. Yahya et.al present Extended Dijkstra's with (edges and nodes weights) parameters to find the shortest server [8].…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm is used for SDN applications. The use of a genetic algorithm load balancer for service recovery on the intercloud network is done by Jena et al [9].…”

Section: Introductionmentioning

confidence: 99%

Link Aggregation Control Protocol on Software Defined Network

Irawati¹,

Hadiyoso²,

Hariyani³

2017

IJECE

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A physical connection of computer network must be made reliably. Breaking connection will cause communication between nodes (for example routers, switches, hosts) can be disconnected. One of the solutions is implemention of link aggregation (LA). LA integrates several of physical ports together to make a single logical communication link. Accordingly, there is load sharing traffic among the member port of the group, high-throughput increasing via a single link, and redundancy providing for broken links. We present the implementation of link aggregation using Ryu controller on Software Defined Network (SDN [4]. OpenFlow is developed by Open Networking Foundation (ONF) Today, the performance of computer networks is a great concern by the customers. Network performance is influenced by the reliability of the physical network topology. When the physical connection between two nodes is failed, then the packet can not be sent through the link. Otherwise, if a physical connection is a good condition with limited bandwidth, it can cause congestion since several nodes using the link for delivery. This problem can be solved by Link Aggregation (LA). LA is a method that aggregates multiple network connections acts as a single logical interface using software. LA provides redundancy for link failure, for example: if one or more physical interface on a logical aggregate loss, the other physical connection still up and operate. Moreover, LA increases throughput passing through a single link which can reduce bottleneck by allowing packets to be traversed over multiple interfaces. LA is defined in IEEE 802.3ad that allows to group Ethernet interface at the physical layer to form a single link interface, namely Link Aggregation Group (LAG). Link Aggregation Control Protocol (LACP) is a mechanism for changing the port to preserve LAG bundles. LACP provides a dynamic configuration that means the other end can handle link aggregation. In addition, it handles failover automatically when a link failed [1].Software Defined Network (SDN) is a new architecture for a computer network that separates network control and forwarding functions, which allowing the controller can connect directly programmable. SDN's characteristics are manageable, adaptable, scalable, appropriate for high bandwidth, and dynamic features of recent applications compared to traditional network architecture [2]. Hence, users can assign the logical network topology using software, regardless of underlying network structure [3]. In general, SDN

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Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Cited by 5 publications

References 0 publications

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Dependability analysis for characterizing Google cluster reliability

Link Aggregation Control Protocol on Software Defined Network

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