Infrastructure as a Service and Cloud Technologies

Serrano, Nicolás; Gallardo, Gorka; Hernantes, Josune

doi:10.1109/ms.2015.43

Cited by 62 publications

(24 citation statements)

References 4 publications

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“…EC2 has been chosen due to both, its popularity in existing benchmarking research and its high industrial relevance. Similarly, Azure was chosen because it is seen as another current market leader [Serrano et al 2015]. GCE, on the other hand, was interesting for us as the service went out of beta only 1 month prior to the start of our data collection (on May 15th, 2014), and has, to the best of our knowledge, so far only been used in a single performance study [Li et al 2013b].…”

Section: Data Collectionmentioning

confidence: 99%

“…GCE, on the other hand, was interesting for us as the service went out of beta only 1 month prior to the start of our data collection (on May 15th, 2014), and has, to the best of our knowledge, so far only been used in a single performance study [Li et al 2013b]. [Serrano et al 2015] lists GCE as a "visionary" in the current cloud market. Finally, we have chosen SL as a smaller, less established, provider, who is nonetheless backed by an established IT company.…”

Section: Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Leitner

Cito

2016

ACM Trans. Internet Technol.

173

155

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Benchmarking the performance of public cloud providers is a common research topic. Previous work has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and experiment setups. In this paper, we present a principled, large-scale literature review to collect and codify existing research regarding the predictability of performance in public Infrastructure-as-a-Service (IaaS) clouds. We formulate 15 hypotheses relating to the nature of performance variations in IaaS systems, to the factors of influence of performance variations, and how to compare different instance types. In a second step, we conduct extensive real-life experimentation on four cloud providers to empirically validate those hypotheses. We show that there are substantial differences between providers. Hardware heterogeneity is today less prevalent than reported in earlier research, while multi-tenancy indeed has a dramatic impact on performance and predictability, but only for some cloud providers. We were unable to discover a clear impact of the time of the day or the day of the week on cloud performance.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Leitner

Cito

2016

ACM Trans. Internet Technol.

173

155

View full text Add to dashboard Cite

show abstract

“…For any new generated node, the estimation of its idle power is computed as follows. First, the difference between the amount of power consumed by reference node's CPU, and the new node's CPU is computed as shown in Equation (2). Then, the difference between the amount of power consumed by reference node's memory and new node's is computed as given in Equation (4).…”

Section: Power Estimation Modelmentioning

confidence: 99%

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

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Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many-Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)-based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.

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“…In cloud data centres [11], the energy utilization of physical machines is mostly determined by processor, memory, disk storage, and network interface controllers. The energy model as represented in this study is based on the fact that processor utilization holds a linear relationship with energy utilization.…”

Section: Energy Efficiency Scenario In Virtual Machinementioning

confidence: 99%

Job Scheduling for Energy Efficiency Using Artificial Bee Colony through Virtualization

Sultanpure¹,

Reddy²

2018

IJIES

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Abstract:Virtualization is used for saving energy, cost and is run for multiple applications using various operating systems on the same server at the same time. Thus, it increases the CPU utilization, efficiency and flexibility of the computer hardware. This paper presents the concept of job scheduling for energy efficiency using Artificial Bee Colony (ABC) by utilizing virtual migration concept in addition to broadcasting of requests. Due to the fact that the virtual migration concept helps a lot in management of the scheduling in cloud computing, so, the validations of job scheduling and service level agreement (SLA) violations with and without virtual migrations are opted in this work. In the initial phase, when server gets the requests for task execution, then the sub-servers (S1, S2, …, Sn), executes that requests (R1, R2,…Rn) irrespective of the main server. When the load at sub-servers increased, then the usage of virtual migration machines is executed, so that the jobs can be implemented equally. Two types of energy has been obtained in the work, first, which is utilized for the execution of the task and second, when virtual migrations creates energy reduction of overall energy (E= E1, E2…En). So, for given interval of time, the creation energy for virtual migrations came out to be less than the allocation energy of virtual migrations for jobs, to have better accuracy. At last, the comparison of proposed work, with existing work is provided. In the existing work, we have considered the papers that evaluate SLA violation and job scheduling by using neural network (NN) and genetic algorithm (GA). The SLA violation of ABC algorithm with respect to GA is reduced by 10% whereas SLA violation of ABC algorithm with respect to NN is reduced by 15%. Job scheduling of ABC algorithm with respect to NN is improved by 15%.

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Infrastructure as a Service and Cloud Technologies

Cited by 62 publications

References 4 publications

Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Job Scheduling for Energy Efficiency Using Artificial Bee Colony through Virtualization

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