Percentile Performance Estimation of Unreliable IaaS Clouds and Their Cost-Optimal Capacity Decision

Zheng, Wanbo; Zhou, MengChu; Wu, Lei; Xia, Yunni; Luo, Xin; Pang, Shanchen; Zhu, Qingsheng; Wu, Yueshen

doi:10.1109/access.2017.2666793

Cited by 33 publications

(4 citation statements)

References 33 publications

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“…Moreover, the CPU performance of VMs and the bandwidth of the cloud cannot always be assumed constant, and fluctuations will cause uncertainty in the task execution time and the data transfer time. The task execution time is always regarded as a stochastic variable with independent and normal distributions [43] [44]. For an expected value and variance 2 , ( , 2 ) can denote the normal distribution of the variable task execution time.…”

Section: Problem Formulationmentioning

confidence: 99%

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

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With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

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Section: Problem Formulationmentioning

confidence: 99%

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

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“…In addition, several studies [24][25][26][27] proposed some task scheduling algorithms from the view of economic principle. Recently, some related work on clouds [28][29][30][31][32][33][34] is done. On the other hand, the BSA 35 allocates tasks according to the abilities of computational node and often serves as a benchmark.…”

Section: Related Workmentioning

confidence: 99%

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Wang

Liu

2020

Int J Communication

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Summary Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.

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“…But the advent of cloud computing has emphatically altered the scenario of information technology. By getting rid of such factors like proportional expenses, scalability, getting rid of upfront IT investment etc., it has brought in substantial advantages for IT users [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Fog Computing Architecture, Applications and Security Issues: A Survey

Neware¹

2019

Preprint

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The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

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Percentile Performance Estimation of Unreliable IaaS Clouds and Their Cost-Optimal Capacity Decision

Cited by 33 publications

References 33 publications

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Fog Computing Architecture, Applications and Security Issues: A Survey

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