Towards Operational Cost Minimization in Hybrid Clouds for Dynamic Resource Provisioning with Delay-Aware Optimization

Li, Song; Zhou, Yangfan; Jiao, Lei; Yan, Xinya; Wang, Xin; Lyu, Michael R.

doi:10.1109/tsc.2015.2390413

Cited by 47 publications

(14 citation statements)

References 19 publications

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“…It is considered as a sharing architecture of the IT trends, in which a third party provides highly scalable, reliable on demand software, hardware, and infrastructure services with agile management capabilities [13], [14]. Cloud computing is divided into three major types of services; public, private and hybrid [15]- [17].…”

Section: A Cloud Computingmentioning

confidence: 99%

A Secured Interoperable Data Exchange Model

Bahaa¹,

Sayed²,

El-Fangary³

2018

ijacsa

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Abstract-Interoperabilityenables peer systems to communicate with each other and use the functionality of peer systems effectively. It improves ability for different systems to exchange information between cooperative systems. It plays a vital role in educational information system institutions. Practically, there are two main technical reasons that restrain the interoperability of the system. First, these systems may be developed under various operating systems, programming languages and different database management systems. Second, the obsessions of security greatly impact the execution of interoperability among various educational institutions. This paper proposes a new RESTful secured interoperable model for data exchange among different information system. This will help educational information system to exchange data among them with a pre-defined standard format of messages. Additionally, this paper designed Cross Platform Web Application Interoperability Protocol (CPWAIP) to facilitate the interaction among components of the proposed model.

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Section: A Cloud Computingmentioning

confidence: 99%

A Secured Interoperable Data Exchange Model

Bahaa¹,

Sayed²,

El-Fangary³

2018

ijacsa

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“…is model optimized the QoS of a video online service in order to reduce the queue length and time delay. To reduce the cost of a hybrid cloud computing platform, Li et al [23] proposed minimizing the communication costs with an online dynamic provision algorithm based on a queueing model. Khazaei et al [24] used an M/G/m/m+r queueing model to evaluate the performance of a cloud computing online service.…”

Section: Related Workmentioning

confidence: 99%

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Gao

Jin

2018

Scientific Programming

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Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.

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“…Basically, these concerns can be classified into two categories, i.e., concerns of cloud users and those of cloud providers. From the perspective of cloud users, there are two major concerns in VM scheduling, i.e., successful execution rate of the VM requests (SERoV) (e.g., [11][12][13][14]), and the combined cost (which is the total execution cost of all users' job requests) incurred (e.g., [3,7,11,15]). These two concerns are important ones since cloud users generally hope to successfully complete their submitted job requests (i.e., to meet their respective deadlines and budgets), and at the same time, at the lowest possible cost.…”

Section: Introductionmentioning

confidence: 99%

Incentive-aware virtual machine scheduling in cloud computing

Liu

Wei

2018

J Supercomput

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As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multiobjective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful B Yang Liu

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Towards Operational Cost Minimization in Hybrid Clouds for Dynamic Resource Provisioning with Delay-Aware Optimization

Cited by 47 publications

References 19 publications

A Secured Interoperable Data Exchange Model

A Secured Interoperable Data Exchange Model

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Incentive-aware virtual machine scheduling in cloud computing

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