Simultaneous Cost and QoS Optimization for Cloud Resource Allocation

Mireslami, Seyedehmehrnaz; Rakai, Logan; Far, Behrouz H.; Wang, Mea

doi:10.1109/tnsm.2017.2738026

Cited by 42 publications

(21 citation statements)

References 25 publications

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“…Langhnoja and Joshiyara [13] propose a novel scheduling algorithm called multi-objective based Integrated Task scheduling which aims to solve task scheduling problem of cloud computing, considering three optimization objectives: execution time, execution cost and load balancing. Mireslami et al [14] propose a multi-objective resource allocation model when deploying a Web application in cloud, considering deployment cost and quality of service (QoS) simultaneously. The algorithm aims to minimize cost, maximize QoS and get a balanced trade-off between the two conflicting objectives.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective optimization of container-based microservice scheduling in edge computing

Fan

Chen

et al. 2021

ComSIS

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Edge computing provides physical resources closer to end users, becoming a good complement to cloud computing.With the rapid development of container technology and microservice architecture, container orchestration has become a hot issue. However, the container-based microservice scheduling problem in edge computing is still urgent to be solved. In this paper, we first formulate the containerbased microservice scheduling as a multi-objective optimization problem, aiming to optimize network latency among microservices, reliability of microservice applications and load balancing of the cluster. We further propose a latency, reliability and load balancing aware scheduling (LRLBAS) algorithm to determine the container-based microservice deployment in edge computing. Our proposed algorithm is based on particle swarm optimization (PSO). In addition, we give a handling strategy to separate the fitness function from constraints, so that each particle has two fitness values. In the proposed algorithm, a new particle comparison criterion is introduced and a certain proportion of infeasible particles are reserved adaptively. Extensive simulation experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm compared with other related algorithms.

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Section: Related Workmentioning

confidence: 99%

Multi-objective optimization of container-based microservice scheduling in edge computing

Fan

Chen

et al. 2021

ComSIS

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“…Several models have been proposed for the cost-efficient SLA-aware allocation of VM resources in a traditional cloud environment [110,112,126,132]. Salah et al for example presented an analytical model based on Markov chains, to predict the minimal number of VMs required for satisfying a given SLO performance requirement [132].…”

Section: Application Elasticity and Provisioningmentioning

confidence: 99%

“…The model not only returns the minimal number of VMs required for the workload, but also the required number of load balancers needed for achieving proper elasticity. Mireslami et al presented a multi-objective costeffective algorithm for minimizing the deployment cost while meeting the QoS performance requirements [110]. The proposed algorithm offers the cloud user an optimal choice when deploying a web application in a traditional cloud environment.…”

Section: Application Elasticity and Provisioningmentioning

confidence: 99%

“…Awada & Barker[30] 2017 Awada & Barker[31] 2017 Babaioff et al[33] 2017 Chard et al[43] 2017 Chi et al[45] 2017 Dalmazo et al[51] 2017 Hai & Nguyen[66] 2017 Hoque et al[72] 2017 Jin et al[80] 2017 Khasnabish et al[84] 2017 Li et al[90] 2017 Li et al[91] 2017 Lloyd et al[96] 2017 Maenhaut et al[97] 2017 Mebrek et al[105] 2017 Mechtri et al[106] 2017 Merzoug et al[108] 2017 Mireslami et al[110] 2017 Nardelli et al[116] 2017 Nitu et al[118] 2017 Paya & Marinescu[121] 2017 Rankothge et al[128] 2017 Tang et al[143] 2017 Xu et al[157] 2017 Yang et al[159] 2017 Yi et al[162] 2017 Yu & Pan[165] 2017 Zhang et al[171] …”

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confidence: 99%

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Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

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“…Evaluation results show that the proposed methods can generate efficient schedules for Virtual Machines, achieving low operational cost reducing the performance degradation of applications in cloud data centers. [6]: Cloud computing offers resources and services for Web applications. In cloud resource allocation, Selection of optimal cloud resources is the important goal.…”

Section: Joint Optimization Of Operational Cost and Performance Intermentioning

confidence: 99%

Scheduling Algorithms in Cloud Computing - An Extensive Survey

Kumar¹,

Rajkumar²,

Devi³

2017

IJTSRD

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In any computing environment, the availability of resources is finite, so effective utilization of resources is very important. To achieve effective management of the available resources in a scalable environment optimal mechanism is required. In order to do this, choosing of scheduling algorithm is important. The scheduling techniques should be selected in such a way that the various user demands are satisfied. In this paper, an overview of different scheduling techniques like cost and energy aware scheduling, cloud based workflow scheduling, an improved ant colony algorithm in cloud computing environment are analysed.

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Simultaneous Cost and QoS Optimization for Cloud Resource Allocation

Cited by 42 publications

References 25 publications

Multi-objective optimization of container-based microservice scheduling in edge computing

Multi-objective optimization of container-based microservice scheduling in edge computing

Resource Management in a Containerized Cloud: Status and Challenges

Scheduling Algorithms in Cloud Computing - An Extensive Survey

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