Towards autonomic workload provisioning for enterprise Grids and clouds

Quiroz, Andres; Kim, Hyun‐Joo; Parashar, Manish; Gnanasambandam, Nathan; Sharma, Naveen Kumar

doi:10.1109/grid.2009.5353066

Cited by 146 publications

(76 citation statements)

References 12 publications

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“…In this case, the cost of reactively creating a new VM type is also incurred. In [4], we showed how VM provisioning with this approach can achieve an over 90% reduction in overprovisioning for CPU and memory in requests from a job trace from the Los Alamos National Labs (LANL) Connection Machine [5].…”

Section: B Autonomic Workload and Vm Provisioningmentioning

confidence: 99%

Autonomic management of distributed systems using online clustering

Quiroz

Parashar

Rodero

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Section: B Autonomic Workload and Vm Provisioningmentioning

confidence: 99%

Autonomic management of distributed systems using online clustering

Quiroz

Parashar

Rodero

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

Self Cite

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“…b) Uncoordinated Policies: Many research works focus on improving resource management on the customer side [18], [6], [9]. These works aim at improving the workload prediction and the allocation of VMs for replication.…”

Section: Related Work A) Memory Footprint Improvementsmentioning

confidence: 99%

Cooperative Resource Management in a IaaS

Tran¹,

Tchana

Hagimont

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-Virtualized IaaS generally rely on a server consolidation system to pack virtual machines (VMs) on as few servers as possible, for energy saving. However, two situations are not taken into account, and could enhance consolidation. First, since the managed VMs can be of various sizes (small, medium, large, etc.), VMs packing can be obstructed when sizes don't fit available spaces on servers. Therefore, we would need to "split" such VMs. Second, two VMs which host replicas of the same application server (for scalability) could be "fusionned" when they are located on the same physical server, in order to reduce virtualization overhead and VMs memory footprint. Split and fusion operations lead to the management of elastic VMs and requires cooperation between the application level and the provider level, as they impact management at both levels. In this paper, we propose a IaaS resource management system which implements elastic VMs based on split/fusion operations and cooperative management. We show its benefit with a set of experiments.

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“…For there are Qi VMs on host Li, so we can get Qi correlation coefficients. Then the maximum correlation coefficient can be calculated in the equation (9 For determining under-loaded hosts simple approach is used. First, all the overloaded hosts are found using the selected overload detection algorithm, and the VMs selected for migration are allocated to the destination hosts using vm placement algorithm.…”

Section: ) the Maximum Correlation Policymentioning

confidence: 99%

CloudSim-A Survey on VM Management Techniques

Vahora¹,

Patel²

2015

International Journal of Advanced Research in Computer and Comm

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Abstract:With the initiation of internet in the 1990s to the present day facilities of universal computing, the internet has reformed the computing world in a drastic way. It has travelled from the concept of parallel computing to distributed computing to cluster computing to grid computing to utility computing to virtualization and recently to cloud computing, in future Internet of Things. Virtualization and utility computing can be stated as key concept of cloud. As cloud computing can be specified as a realization of utility computing. Although the idea of cloud computing has been around for quite some time, it is an evolving field of computer science. Since the evolution of cloud computing: Load balancing, energy management, VM migration, server consolidation, cost modelling and security issues are the popular research topic in this field. Deploying real cloud for testing or for commercial use is very costly. Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable and controllable manner under varying system and user configurations and requirements is difficult to accomplish. To overcome this challenge, cloud simulator is needed. In this paper basic of cloud simulator is discussed, and major focus is on cloudsim-a simulator for management of vm. The CloudSim toolkit supports both system and behaviour modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). In this paper how cloudsim work, its architectural design, highlighting important features and give brief overview of its functionalities is presented.

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Towards autonomic workload provisioning for enterprise Grids and clouds

Cited by 146 publications

References 12 publications

Autonomic management of distributed systems using online clustering

Autonomic management of distributed systems using online clustering

Cooperative Resource Management in a IaaS

CloudSim-A Survey on VM Management Techniques

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