Market-Based Load Balancing for Distributed Heterogeneous Multi-Resource Servers

Yang, Chih-Chiang; Chen, Kun-Ting; Chen, Chien; Chen, Jingying

doi:10.1109/icpads.2009.96

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Such a metric was finally used to select the target physical server for VM placement. A market-based solution for multiple-resource load balancing was presented in [9] in the context of job scheduling. For such a scenario, a Market Mechanism (MM) scheme was introduced with a selection policy built on top of a pricing model to calculate the cost per resource of a job.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of determining when a server is overloaded (i.e., a hot spot) or underloaded (i.e., a cold spot), then migrating the VMs from this server to others has been studied in the literature [4][5][6][7][8][9]. In this context, several metrics have been proposed to rank physical servers.…”

Section: Introductionmentioning

confidence: 99%

“…However, the major limitation of the BG scheme is that it does not support migrating underloaded servers. Multi-resource VM placement has been also studied in the literature [8][9][10]. A vector-based scheme was employed by the HARMONY system for load balancing [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers

Hieu

Francesco

Ylä-Jääski

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited tradeoff among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers

Hieu

Francesco

Ylä-Jääski

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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“…Max-BRU makes an efficient use of these resources and reduces the number of required active physical servers in comparison with the greedy FFA proposed in [34], [39], the Load-aware policy used in [66], the VectorDot used in [14] and the market mechanism approach proposed in [17].…”

Section: B Number Of Activated Nodes 1) Online Vmp To Reduce the Nummentioning

confidence: 99%

A Survey on the Placement of Virtual Resources and Virtual Network Functions

Laghrissi

Taleb

2019

IEEE Commun. Surv. Tutorials

229

110

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Cloud computing and network slicing are essential concepts of forthcoming 5G mobile systems. Network slices are essentially chunks of virtual computing and connectivity resources, configured and provisioned for particular services according to their characteristics and requirements. The success of cloud computing and network slicing hinges on the efficient allocation of virtual resources (e.g. VCPU, VMDISK) and the optimal placement of Virtualized Network Functions (VNFs) composing the network slices. In this context, this paper elaborates issues that may disrupt the placement of VNFs and VMs. The paper classifies the existing solutions for VM Placement (VMP) based on their nature, whether the placement is dynamic or static, their objectives, and their metrics. The paper then proposes a classification of VNF Placement (VNFP) approaches, first, regarding the general placement and management issues of VNFs, and second, based on the target VNF type.

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“…Resources are allocated priorities/weights according to the system load-distribution, where higher priorities are given to underutilized resources. Furthermore, a market-based load balancing algorithm is proposed by Yang et al [14] to distribute workloads between resources. The cost of a job is directly related to the resource loads, as well as resources continuously exchange state load-information to decide on the redistribution and allocation of jobs.…”

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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Market-Based Load Balancing for Distributed Heterogeneous Multi-Resource Servers

Cited by 7 publications

References 20 publications

A Multi-resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers

A Multi-resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers

A Survey on the Placement of Virtual Resources and Virtual Network Functions

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

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