An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers

Khamse-Ashari, Jalal; Lambadaris, Ioannis; Kesidis, George; Urgaonkar, Bhuvan; Zhao, Yiqiang Q.

doi:10.1109/tpds.2018.2841915

Cited by 39 publications

(38 citation statements)

References 35 publications

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“…To fulfill the trade-off between fairness and efficiency, the resource allocation was done by maximizing per-server utility functions using particular classes. Similar to other works, the algorithm in [19] meets certain good fairness features. The main point of the work conducted is to calculate the dominant resources based on each server, including the virtual dominant resource.…”

Section: Related Worksupporting

confidence: 55%

“…Although this work demonstrates improvements in terms of overall resource utilization, the results also show that RAM utilization has not reached desired utilization point. In [19] the authors proposed a new server-based algorithm to overcome the existing issues in DRF. To fulfill the trade-off between fairness and efficiency, the resource allocation was done by maximizing per-server utility functions using particular classes.…”

Section: Related Workmentioning

confidence: 99%

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FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

Hamzeh

Meacham

Khan

et al. 2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

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The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for a while. Cloud computing seen as a promising technology, offers usage-based payment, scalable and on-demand computing resources. However, during the past decade, the growing complexity of the IT world has resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, the fair allocation of resources in the cloud becomes particularly interesting when many users submit several tasks which require multiple resources. Research in this area has been increasing since 2012 by introducing the Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the fair resource allocation problem in the cloud. Although DRF meets a sort of desirable fairness properties, it has been proven to be inefficient in certain conditions. Noticeably, DRF and other works in its extension are not intuitively fair after all. Those implementations have been unable to utilize all the resources in the system, leaving the system in an imbalanced situation with respect to each specific system resource. In order to address those issues, we propose in this paper a novel algorithm namely a Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments (FFMRA) which allocates resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments conducted in CloudSim show that FFMRA provides approximately 100% recourse utilization, and distributing them fairly among the users while meeting desirable fairness features.

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Section: Related Worksupporting

confidence: 55%

Section: Related Workmentioning

confidence: 99%

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

Hamzeh

Meacham

Khan

et al. 2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

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“…Note how PS-DSF's performance under RRR is comparable to when frameworks and servers are jointly selected [17], and with low variance in allocations. We also found that RRR-rPS-DSF performed just as rPS-DSF over 200 trials.…”

Section: Illustrative Numerical Study Of Fair Scheduling By Progrmentioning

confidence: 97%

“…This question has received much attention in the recent past. Proposed fair schedulers include Dominant Resource Fairness (DRF) [12] extended to multiple servers 1 , Task Share Fairness (TSF) [31], Per Server Dominant Share Fairness (PS-DSF) [18], [16], [17], among others, e.g., [5].…”

Section: Introductionmentioning

confidence: 99%

Scheduling Distributed Resources in Heterogeneous Private Clouds

Kesidis¹,

Shan

Jain

et al. 2018

2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

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We first consider the static problem of allocating resources to (i.e., scheduling) multiple distributed application frameworks, possibly with different priorities and server preferences, in a private cloud with heterogeneous servers. Several fair scheduling mechanisms have been proposed for this purpose. We extend prior results on max-min and proportional fair scheduling to this constrained multiresource and multiserver case for generic fair scheduling criteria. The task efficiencies (a metric related to proportional fairness) of max-min fair allocations found by progressive filling are compared by illustrative examples. They show that "server specific" fairness criteria and those that are based on residual (unreserved) resources are more efficient.

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“…However, considering that cloud environment is heterogeneous, multiple resources should be considered to provide a fair allocation [12]. Any fair resource allocation mechanism is subjected to have at least some important fairness properties [13] as followings:…”

Section: Fairness In Resource Allocationmentioning

confidence: 99%

MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

Hamzeh

Meacham

Virginas

et al. 2019

2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)

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Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a naïve extension form in the presence of multiple servers as well.

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An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers

Cited by 39 publications

References 35 publications

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

Scheduling Distributed Resources in Heterogeneous Private Clouds

MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

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