Hybrid SFLA-GA algorithm for an optimal resource allocation in cloud

Kayalvili, S.; Selvam, M.

doi:10.1007/s10586-018-2011-8

Cited by 27 publications

(12 citation statements)

References 9 publications

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“…Work ow scheduling, however, was an NPhard problem thus making solutions based on metaheuristics explored widely. Kaur and Mehta [7] had proposed yet another augmented SFLA (ASFLA) method used for resource allocation along with work ow scheduling used in IaaS. Its performance was compared to other SFLA and PSO algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Karpagam

2021

Preprint

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An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Karpagam

2021

Preprint

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“…Both the proposed DSOM and DNSFLA have a wide range of real application scenarios. For reference, the rudiment example of formulas included in the DSOM algorithm can be found in [45,46], and the rudiment example of formulas included in the DNSFLA algorithm can be found in [47,48]. To make it easier to follow the formulas proposed in this section, we still take the application of Smart Home described in reference [37] as an example.…”

Section: Analysis Of Msdgasmentioning

confidence: 99%

Multistage Dynamic Packet Access Mechanism of Internet of Things

Meng

Chen

Zhang

2018

Mobile Information Systems

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In the scenario of mass control commands requesting for network access, confined by the best-effort network service mode, it is easy to bring about resource competition and thus a phenomenon of access failure on major and urgent service request at the data access center for the Internet of ings. In this event, the dynamic diversification of control command is unable to access the necessary resources on a comparatively fair basis, causing low efficiency in heterogeneous resource utilization at the access center. is paper defines the problem of group request dynamic resource allocation and further converts it into the problem of 0-1 integer and linear programming and proposes a multistage dynamic packet access strategy. is strategy works first on dynamic group division on the users' mass control requests using the high ability of self-organizing feature maps and then searches for the optimized matching resources based on the frog-leaping algorithm which has a better capacity for global searching for the best resources. is paper analyzes the feasibility of this strategy and its astringency. e experimental results demonstrate that the strategy can effectively improve the success rate of access to the data center for the Internet of ings and reduce network blockage and response delay.

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“…Thus, the allocation of resources is done using cloud-based software services, which needs to make a tradeoff between QoS and occupied resources cost. [18][19][20] The allocation of resources in the spark cluster can be performed in three methods. The first method is the default method wherein the user failed to fix the limit on needed resources and utilizes all resources for the complete cluster.…”

Section: Introductionmentioning

confidence: 99%

Exponentially‐spider monkey optimization based allocation of resource in cloud

Kalpana

Prabhu²,

Polepally

et al. 2021

Int J of Intelligent Sys

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The increasing desire for distributed computing systems has attracted huge interest in memory and computing resources. The cloud provides on-demand access to provide a flexible allocation of resources for reliable services. Therefore, there should be a provision in which resources are accessible to request users to satisfy user needs. In classical techniques, the allocation of resources by satisfying power and Quality-of-Services is a challenging aspect. This paper devises a novel technique for optimal resource allocation, namely, Exponentially Spider Monkey Optimization (E-SMO). Here, the proposed E-SMO is devised by combining Exponential Weighted Moving Average and Spider Monkey Optimization (SMO). Besides, the fitness function is newly devised considering resource utilization and resource cost. After that, the cloud resources employ a switching strategy to reduce power consumption to prevent the switching of redundant servers. For optimal switching, the proposed E-SMO is utilized with other fitness factors that compute the number of applications assigned in the physical machine using the switching state is in OFF condition. Thus, the server switching model is incorporated to activate or deactivate the server when not in use for effective resources utilization. The proposed E-SMO algorithm outperformed other methods with the maximal resource

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Hybrid SFLA-GA algorithm for an optimal resource allocation in cloud

Cited by 27 publications

References 9 publications

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Multistage Dynamic Packet Access Mechanism of Internet of Things

Exponentially‐spider monkey optimization based allocation of resource in cloud

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