A Hierarchical Approach for the Resource Management of Very Large Cloud Platforms

Addis, Bernardetta; Ardagna, Danilo; Panicucci, Barbara; Squillante, Mark S.; Zhang, Li

doi:10.1109/tdsc.2013.4

Cited by 79 publications

(72 citation statements)

References 41 publications

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“…An example of cloud management solutions exploiting queueing network models is [55], where the cloud service center is modeled as an open queueing network of multiclass single-server queues. PS scheduling is assumed at the resources to model CPU sharing.…”

Section: Performance Modelsmentioning

confidence: 99%

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Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

Self Cite

211

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Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

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Section: Performance Modelsmentioning

confidence: 99%

“…In [55] a VM placement problem for a PaaS is solved at multiple time-scales through a hierarchical optimization framework. Authors in [132] provide a solution for trafficaware VM placement minimizing also network latencies among deployed applications.…”

Section: Infrastructure-provider Capacity Allocationmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

Self Cite

211

View full text Add to dashboard Cite

show abstract

“…In [38] the VM placement problem is solved at multiple time scales through a hierarchical optimization framework. The paper [39] aims at optimizing the cost and the utilization of a set of applications through vertical auto-scaling mechanism, i.e., proposing a new set of instances to minimize cost and maximize utilization, or increase performance efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero

Ciavotta

Gibilisco

et al. 2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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Recently we witnessed a deep transformation in the the design, development and management of modern applications, which have grown in scope and size becoming distributed and service-oriented. A big part in this metamorphosis is played by the Cloud with the availability of almost-infinite resources, high availability and outsourced maintenance. This has led to the emergence of new software development methodologies to effectively deal with this paradigm shift in the field of software engineering. DevOps is one of them, it advocates for a greater level of collaboration and convergence between developers and other IT professionals. Consequently, new tools, purposely designed to ease this process, are required. In this scenario, we present SPACE4Cloud, a DevOps integrated environment for model-driven design-time quality of service assessment and optimization, and runtime capacity allocation of Cloud applications.

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“…al. [23] pointed out that there are five main problem areas which must be considered in system allocation policy design:1) application/VM placement, 2) admission control,3) capacity allocation,4) load balancing, and 5) energy consumption. They integrated all five problem areas within a unifying framework, providing very efficient and robust solutions at multiple time-scales.…”

Section: A Review On Resource Management In Cloud Computingmentioning

confidence: 99%

Dynamic Consolidation of Virtual Machines with Multi-Agent System

Barlaskar¹,

Singh²

2014

IJCA

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Cloud computing systems supply massive infrastructures for high-performance computing that are flexible as they are able to acclimate to user and application requirements. Cloud Computing offers on demand services which are used by virtue of a service-oriented interface that execute the anything-as-a-service archetype. The enlargement of Cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of compute nodes and these data centers consume excessive amounts of electrical energy resulting in high operating costs. Therefore, to cut down the cost of energy consumption the cloud providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an effective way to improve energy efficiency in cloud data center. The problem of VM consolidation can be split into four sub-problems namely physical machine overload detection; physical machine under-load detection; VM selection and VM placement. Each of the afore-stated subproblems must operate in an optimized manner to maintain the tradeoff between energy and performance. In this research paper a new multi-agent system (MAS) for dynamic consolidation of VMs is proposed with the aim of making the cloud system smarter by incorporating the five traits of multiagent systems which are ubiquity, interconnection, intelligence, delegation and human orientation. The Cloud Computing systems require intelligent and perceptive based software with progressive, elastic, self-ruling style which can be provided by MASs. The proposed method has significantly reduced energy consumption and at the same time ensures a high level of constancy to the Service Level Agreements (SLA).

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A Hierarchical Approach for the Resource Management of Very Large Cloud Platforms

Cited by 79 publications

References 41 publications

Quality-of-service in cloud computing: modeling techniques and their applications

Quality-of-service in cloud computing: modeling techniques and their applications

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Dynamic Consolidation of Virtual Machines with Multi-Agent System

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