Provisioning Policies for Elastic Computing Environments

Marshall, Paul; Tufo, Henry M.; Keahey, Kate

doi:10.1109/ipdpsw.2012.132

Cited by 17 publications

(16 citation statements)

References 21 publications

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“…Resource management in clouds is a typical and crucial topic and a number of works have been published [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. 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].…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

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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“…(The baseline policy) On-Demand All (ODA): This is a simple, commonly used policy [8,10,25,27,41]. It leases the required number of VMs for all the queued jobs whenever there are available VMs.…”

Section: The Policy Portfoliomentioning

confidence: 99%

“…Similarly to our previous work [41], we divide the scheduling policy into three parts: resource provisioning, job selection, and VM selection. For the first part, we surveyed recent study on resource provisioning and allocation in the cloud, and selected the policies that both perform well and are suitable for parallel job scheduling for the construction of policy portfolio [8,10,25,27,41,43]. The selected policies are also limited to identical type of on-demand VMs, and we refer to our prior work [31] for policies using multiple instance types.…”

Section: Related Workmentioning

confidence: 99%

“…Genaud et al [10,27] study bin-packing strategies for scheduling independent, sequential grid workloads in IaaS clouds. Marshall et al [25] and Wang et al [43] present resource provisioning policies for parallel jobs in order to balance monetary cost and job wait time. Other efforts focused on cost-efficient execution of applications such as Bags-ofTasks (BoTs) [2,28] and scientific workflows [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

Deng

Song

Ren

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads.

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Survey of Elasticity Management Solutions in Cloud Computing

Najjar

Serpaggi

Gravier

et al. 2014

Computer Communications and Networks

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International audienceApplication Service Providers (ASPs) are increasingly adopting the cloud computing paradigm to provision remotely available resources for their applications. In this context, the ability of cloud computing to provision resources on-demand in an elastic manner is of the utmost practical interest for them. As a consequence, the field of cloud computing has witnessed the development of a large amount of elasticity management solutions deeply rooted in works from distributed systems and grid computing research communities. This chapter presents some solutions that differ in their goals, in the actions they are able to perform and in their architectures. In this chapter, we provide an overview of the concept of cloud elasticity and propose a classification of the mechanisms and techniques employed to manage elasticity. We also use this classification as a common ground to study and compare elasticity management solutions

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Provisioning Policies for Elastic Computing Environments

Cited by 17 publications

References 21 publications

QoS-Aware Resource Allocation for Video Transcoding in Clouds

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

Survey of Elasticity Management Solutions in Cloud Computing

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