Improving Virtual Machine live migration via application-level workload analysis

Baruchi, Artur; Midorikawa, Edson Toshimi; Netto, Marco A. S.

doi:10.1109/cnsm.2014.7014153

Cited by 8 publications

(9 citation statements)

References 22 publications

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“…Diferente do que foi apresentado em Baruchi et. al [6], o presente trabalho detalha os algoritmos utilizados para a identificação dos períodos cíclicos e apresenta novos resultados obtidos a partir de experimentos em cenários mais completos para análise de características relevantes da solução proposta.…”

Section: Introductionunclassified

Reducing Virtual Machine Live Migration Overhead via Workload Analysis

Baruchi

Midorikawa

Sato

2015

IEEE Latin Am. Trans.

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A key feature in virtualization technology is the Live Migration, which allows a Virtual Machine (VM) to be moved from a physical host to another without execution interruption. This feature enables the implementation of more sophisticated policies inside a cloud environment, such as energy and computational resources optimization, and improvement of quality-of-service. However live migration can impose severe performance degradation for the VM application and cause multiple impacts in service provider infrastructure, such as network congestion and colocated VM performance degradation. Different of several studies we consider the VM workload an important factor and we argue that carefully choosing a proper moment to migrate a VM can reduce the live migration penalties. This paper introduces a method to identify the workload cycles of a VM and based on that information it can postpone a Live Migration. In our experiments, using relevant benchmarks the proposed method was able to reduce up to 43% of network data transfer and reduce up to 74% of live migration time when compared to traditional consolidation strategies that perform live migration without considering the VM workload.

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Section: Introductionunclassified

Reducing Virtual Machine Live Migration Overhead via Workload Analysis

Baruchi

Midorikawa

Sato

2015

IEEE Latin Am. Trans.

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“…The migration of Virtual Machine is of highest significance in implementing resource management schemes for enhancement of performance parameters like utilization of resource, energy consumption and QoS. These parameters are critical for any VM migration in terms of server downtime and network usage, as discussed in ref [78].…”

Section: Comparison Of Various Scheduling Schemes Based On Energy Conmentioning

confidence: 99%

“…Some authors [78] have made an attempt to determine suitable time for VM migration, which explores the application level data, in combination with the hi-tech system level metric. The result of this research has produced an application-oriented live migration model.…”

Section: Comparison Of Various Scheduling Schemes Based On Virtualizamentioning

confidence: 99%

A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters

Anitha

Raj

2018

IJERMT

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Abstract:loud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios. Keywords: Cloud Computing, Energy-Aware Resource Allocation, VM Migration, Load Balancing, Price Prediction I. INTRODUCTIONCloud computing came in as a game changer for modern IT enterprises. Instead of constructing and maintaining an in-house IT infrastructure, the cloud computing enabled trouble-free outsourcing of IT infrastructure to third party service providers for their computational needs [1]. The clouds refer to a set of large scale out-sourcing hardware infrastructure, known as datacenters, which has the ability to accommodate several thousands of servers. More often, these servers will be run with the help of virtualized operating systems and thus, they are known as virtual machines (VMs). Thus, these cloud based datacenters supports wide array of system application and offers a concept of unlimited computing resources to its users which are later billed as per usage or subscription plans. Although these cloud based server provide numerous benefits to its users through its virtualized infrastructures, they are prone to few overhead and maintenance costs. Even though, the cloud datacenters have several in built management techniques, like on-demand resource scheduling, energy management, etc, they suffer from few issues which restricts their wide spread acceptance [2], [3].Essentially, the cloud based datacenters must guarantee superior Quality of Service (QoS) and reliability in order to make them commercially successful in the field of computing infrastructure. The assurance and guarantees made by these cloud datacenters are recorded on paper, known as Service Level Agreements (SLAs). The SLA are quite essen...

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“…In contrast to single-cloud deployment, the proposed multi-cloud placement algorithms yielded improvement in performance with lower costs during an evaluation against commercial clouds. An attempt to decide the appropriate time for VM migration has been made by the authors [84] in which they have articulated an application-oriented live migration model exploring the application level information, in conjunction to the state-of-art system level metric. The experiment was conducted on three real applications with due consideration to the application specific to catalyze the VM live migration.…”

Section: Scheduling Based On Virtualizationmentioning

confidence: 99%

“…The primer challenge for VM migration in terms of service downtime and high network utilization is discussed [84]. In [81], the authors put forward a novel model for optimization which laid its basis on linear programming along with an automatic approach for VM migration in self-managing virtualized environments.…”

Section: Scheduling Based On Virtualizationmentioning

confidence: 99%

Resource Scheduling in Cloud Environmet: A Survey

Mangla¹,

Singh²,

Rana³

2016

Adv. Sci. Technol. Res. J.

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Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

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Improving Virtual Machine live migration via application-level workload analysis

Cited by 8 publications

References 22 publications

Reducing Virtual Machine Live Migration Overhead via Workload Analysis

Reducing Virtual Machine Live Migration Overhead via Workload Analysis

A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters

Resource Scheduling in Cloud Environmet: A Survey

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