A new approach to improve load balancing for increasing fault tolerance and decreasing energy consumption in cloud computing

Moghtadaeipour, Ali; Tavoli, Reza

doi:10.1109/kbei.2015.7436178

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Year Journal or conference name [52] 2014 Advances in engineering and technology [53] 2014 Recent trends in information technology [54] 2015 Computer science trends and technology [55] 2016 Knowledge-based engineering and innovation [56] 2017 Computer communications and networks [57] 2017…”

Section: Researchmentioning

confidence: 99%

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Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

et al. 2022

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Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to detect under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, response time, reliability, availability, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

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Section: Researchmentioning

confidence: 99%

“…Nevertheless, the proposed method has not been simulated. Moreover, a load balancing architecture using fuzzy logic to decrease the energy consumption and increase fault tolerance has been proposed by the authors of [55]. They have designed three fuzzy inference engines to prioritize VOLUME XX, 2017…”

Section: A Review Of Distributed Approachesmentioning

confidence: 99%

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

et al. 2022

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“…Unfortunately, it might undergo reduced performance over large, exceedingly dynamic network 52 . Energy and SLA‐centric load balancing was performed in Vig et al 53 and Moghtadaeipour and Tavoli 54 yet failed addressing local minima and convergence. Yakhchi et al 55 considered MMT information to select a VM for migration or allied load balancing.…”

Section: Related Workmentioning

confidence: 99%

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

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“…In [6], improved tolerability and fault detection in cloud computing based on two fuzzy systems were proposed. In this method, to increase the fault tolerance and load balance in the event of a fault, the techniques of re-requesting the task and migrating through the checkpoint are used.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

Hagshenas¹,

Mojarad²,

Arfaeinia³

2022

IJISA

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Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.

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A new approach to improve load balancing for increasing fault tolerance and decreasing energy consumption in cloud computing

Cited by 10 publications

References 7 publications

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

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