A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms

Rana, Madhurima; Bilgaiyan, Saurabh; Kar, Utsav

doi:10.1109/iccicct.2014.6992964

Cited by 26 publications

(7 citation statements)

References 16 publications

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“…Moreover, meta-heuristic approaches are employed to provide near-optimal schedules in a reasonable time [28][29][30][31]. Such approaches are typically used because the various characteristics and SLA obligations of clients make tackling the scheduling problem a complex task that often cannot be effectively addressed in a polynomial time.…”

Section: Background and Related Workmentioning

confidence: 99%

SLA-driven Load Scheduling in Multi-tier Cloud Computing: Financial Impact Considerations

Suleiman¹,

Basir²

2020

IJCCSA

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A cloud service provider strives to effectively provide a high Quality of Service (QoS) to client jobs. Such jobs vary in computational and Service-Level-Agreement (SLA) obligations, as well as differ with respect to tolerating delays and SLA violations. The job scheduling plays a critical role in servicing cloud demands by allocating appropriate resources to execute client jobs. The response to such jobs is optimized by the cloud service provider on a multi-tier cloud computing environment. Typically, the complex and dynamic nature of multi-tier environments incurs difficulties in meeting such demands, because tiers are dependent on each others which in turn makes bottlenecks of a tier shift to escalate in subsequent tiers. However, the optimization process of existing approaches produces single-tier-driven schedules that do not employ the differential impact of SLA violations in executing client jobs. Furthermore, the impact of schedules optimized at the tier level on the performance of schedules formulated in subsequent tiers tends to be ignored, resulting in a less than optimal performance when measured at the multi-tier level. Thus, failing in committing job obligations incurs SLA penalties that often take the form of either financial compensations, or losing future interests and motivations of unsatisfied clients in the service provided. Therefore, tolerating the risk of such delays on the operational performance of a cloud service provider is vital to meet SLA expectations and mitigate their associated commercial penalties. Such situations demand the cloud service provider to employ scalable service mechanisms that efficiently manage the execution of resource loads in accordance to their financial influence on the system performance, so as to ensure system reliability and cost reduction. In this paper, a scheduling and allocation approach is proposed to formulate schedules that account for differential impacts of SLA violation penalties and, thus, produce schedules that are optimal in financial performance. A queue virtualization scheme is designed to facilitate the formulation of optimal schedules at the tier and multi-tier levels of the cloud environment. Because the scheduling problem is NPhard, a biologically inspired approach is proposed to mitigate the complexity of finding optimal schedules. The reported results in this paper demonstrate the efficacy of the proposed approach in formulating costoptimal schedules that reduce SLA penalties of jobs at various architectural granularities of the multi-tier cloud environment.

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Section: Background and Related Workmentioning

confidence: 99%

SLA-driven Load Scheduling in Multi-tier Cloud Computing: Financial Impact Considerations

Suleiman¹,

Basir²

2020

IJCCSA

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“…They have included time taken to stage in the input files and stage out the output files to desired location. Load balancing in cloud computing done by various algorithm, tabular representation of research done on load balancing using various evolutionary and swarm based algorithm is summarized in [26]. Cloud users are categorized on the basis of job-length, nature of user (domestic or commercial) and time of execution (peak or off-peak).…”

Section: Related Researchmentioning

confidence: 99%

Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Jena

Mohanty

2016

IJECE

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Paradigm need to shifts from cloud computing to intercloud for disaster recoveries, which can outbreak anytime and anywhere. Natural disaster treatment includes radically high voluminous impatient job request demanding immediate attention. Under the disequilibrium circumstance, intercloud is more practical and functional option. There are need of protocols like quality of services, service level agreement and disaster recovery pacts to be discussed and clarified during the initial setup to fast track the distress scenario. Orchestration of resources in large scale distributed system having muli-objective optimization of resources, minimum energy consumption, maximum throughput, load balancing, minimum carbon footprint altogether is quite challenging. Intercloud where resources of different clouds are in align, plays crucial role in resource mapping. The objective of this paper is to improvise and fast track the mapping procedures in cloud platform and addressing impatient job requests in balanced and efficient manner. Genetic algorithm based resource allocation is proposed using pareto optimal mapping of resources to keep high utilization rate of processors, high througput and low carbon footprint. Decision variables include utilization of processors, throughput, locality cost and real time deadline. Simulation results of load balancer using first in first out and genetic algorithm are compared under similar circumstances.

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“…Normally there are two main type of load balancing which are task scheduling and resource scheduling in a distributed environment. One of the main elements in task scheduling is to provide user demand on time [1]. In a cloud environment virtual machine manage all the activity in the data center for load balancing activity.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony algorithm used for load balancing in cloud computing: Review

Ullah

Nawi

Uddin

et al. 2019

IJ-AI

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<p>Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed workload among various nodes equally in a situation where some nodes are under load and some are overload. Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005. It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This survey paper is a comprehensive study about load balancing in cloud computing using the ABC algorithm. It also defines some basic concept about swarm intelligent and its property<strong>.</strong></p>

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A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms

Cited by 26 publications

References 16 publications

SLA-driven Load Scheduling in Multi-tier Cloud Computing: Financial Impact Considerations

SLA-driven Load Scheduling in Multi-tier Cloud Computing: Financial Impact Considerations

Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Artificial Bee Colony algorithm used for load balancing in cloud computing: Review

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