Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis

Saif, Mufeed Ahmed Naji; Niranjan, S. K.; Al-ariki, Hasib Daowd Esmail

doi:10.1007/s11276-021-02614-1

Cited by 17 publications

(5 citation statements)

References 271 publications

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“…The constraint (11) ensures the assignment of a container to only one server which is optimal to satisfy all the requirements in every dimension.…”

Section: Constraintsmentioning

confidence: 99%

“…Autonomic management of resources in multi‐tenant cloud enables automatic sharing of resources to cloud users. Also, the self‐management properties help in elastic execution of business processes (BPs) 11,12 …”

Section: Introductionmentioning

confidence: 99%

“…Also, the self-management properties help in elastic execution of business processes (BPs). 11,12 Container in cloud is a virtualization technology that is mainly developed for resource sharing and to overcome the problems of program independence. As a result, the users can easily access the cloud resources for executing their workload efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Saif¹,

Aradhya²,

Murshed

et al. 2023

Concurrency and Computation

Self Cite

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Cloud multi-tenancy has a variant requirement, due to its resource sharing nature, satisfying such requirements and maintaining a balance between the resources and the business workloads of multiple tenants is a challenging task, and also the communication between scheduled containers leads to high power consumption. To address these issues, this article proposes an autonomic approach to ensure the elasticity of BPM in multi-tenant cloud. Where it employs the autonomic computing capabilities for scheduling the containers into the available servers then regulates the communication between the containers using multi-path routing. For the container scheduling, a multi-objective crow search optimization algorithm is proposed to schedule the containers into appropriate servers. Then, the discrete wolf search algorithm based multipath routing is proposed to route the communication flows between the containers by finding the optimal path with an objective to minimize the energy consumption.The optimal path is constructed as a multi-tenancy graph with bandwidths determining the shortest distance between the servers and containers. The overall simulations shows that the proposed algorithm outperformed the other compared approaches in terms of make-span, resource utilization, execution cost, execution time, and energy consumption.

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“…The constraint (11) ensures the assignment of a container to only one server which is optimal to satisfy all the requirements in every dimension.…”

Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Saif¹,

Aradhya²,

Murshed

et al. 2023

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…While provisioning and allocating cloud resources, availability was frequently used as the determining factor without considering the other essential factors like resource utilization or the server's thermal properties [9]. A cloud system's ability to manage its resources autonomously and adaptively based on the workload changes is known as autonomous RM [20]. Elastic resource management encompasses a wide range of processes, including balanced virtual machine and application scheduling, server over/under-load control utilizing VM migration, etc.…”

Section: Introductionmentioning

confidence: 99%

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

Karimunnisa¹,

Yellamma²

2023

IJACSA

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Cloud systems by virtue characterize ultimate resource utilization with ever evolving user requirements facilitating adaptivity. With a scope of enhancing the QoS needs of user applications, numerous factors are considered for tunning among which Task scheduling promises to grab focus. The Task Scheduling mechanism ascertains improvement by distributing the subtasks to specific set of resources pertaining to prevailing Quality models. The work emphasizes the need for effective task scheduling and optimizing resource allocation by modelling a modified AHP (Analytical Hierarchy Process) driven approach. The proposed method guarantees the functionality in two phases pertaining to Task ranking and pipelined with Optimized scheduling algorithms resulting in maximization of resource utilization. The former phase of task ranking is aided by improved AHP with substantial usage of fuzzy clustering followed by an enhanced CUCMCA (Chimp Updated and Cauchy Mutated Coot Algorithm) algorithm for optimal resource allocation of cloud applications. The contributed model promises leveraged performance of 32% for memory usage, 33.5% for execution time, 29% for makespan and 18% for communication cost over pre-existing conventional models considered.

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“…A formal procedure is followed in cloud computing that if there is any SLA violation encountered in the workload execution process, then penalties are provided to the service providers [27]. When there are no violations of SLA for different workload executions, then rewards are provided either to the customers or service providers after evaluations [28]. One of the major problems arising here is with the dynamic execution of workloads where there are a huge number of workloads arriving in the cloud for executions.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Singh¹,

Goraya²

2023

IJACSA

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Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced technology is growing abruptly with the increase in the number of users. One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions. While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud. Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA. To fill this gap, this article proposes an automatic multiagent framework that ensures the minimization of the SLA violation rate in workload execution. The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent. All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature. With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres. The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization. The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes. The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works. The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.

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Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis

Cited by 17 publications

References 271 publications

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

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