Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds

Wei, Lei; Foh, Chuan Heng; He, Bingsheng; Cai, Jianfei

doi:10.1109/tcc.2015.2481400

Cited by 62 publications

(27 citation statements)

References 30 publications

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“…Resource provisioning 4 has a wide range of options for resource selection. Cloud o®ers Virtual Machine Instances with varying con¯guration in terms of computer memory, storage and networking performance.…”

Section: Work°ow Scheduling Architecturementioning

confidence: 99%

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Jaybhaye

Attar

2020

Vietnam J. Comp. Sci.

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Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

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Section: Work°ow Scheduling Architecturementioning

confidence: 99%

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Jaybhaye

Attar

2020

Vietnam J. Comp. Sci.

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“…Initially, most of the research works have been done for single-resource environments such as works in [10] [11] where there are only one type of resource such as CPU. 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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“…These works show their advantages on specific aspects of cloud resource management such as heuristic bin-packing of virtual machine (VM) placement in data centers [6], [7], balancing the cost and the deadline of jobs in clouds [8], [9], [10], [11], handling bursting workloads [12], dynamic cluster resizing [13], [15], [16], fairness between tenants in private clouds [17] and resource provisioning for heterogeneous workloads [14], [18]. All these works are designed for general workloads in clouds but not covering the unique system demands of video transcoding services.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei

Cai

Foh

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-As the "biggest big data", video data streaming in the network contributes the largest portion of global traffic nowadays and in future. Due to heterogeneous mobile devices, networks and user preferences, the demands of transcoding source videos into different versions have been increased significantly. However, video transcoding is a time-consuming task and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-time applications which hold strict delay requirement such as live TV. In this paper, we propose a cloud-based online video transcoding system (COVT) aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes performance profiling technique to obtain the different performance of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that COVT effectively provisions minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large scale video data, we further perform simulation evaluation which again shows that COVT is capable to achieve economical and QoS-aware video transcoding in cloud environment.

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Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds

Cited by 62 publications

References 30 publications

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

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

QoS-Aware Resource Allocation for Video Transcoding in Clouds

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