A Heuristic for Mapping Virtual Machines and Links in Emulation Testbeds

Calheiros, Rodrigo N.; Buyya, Rajkumar; Rose, César A. F. De

doi:10.1109/icpp.2009.7

Cited by 31 publications

(17 citation statements)

References 10 publications

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“…Since QoS mechanisms for bandwidth control have to be used in order to cope with the heterogeneity of the clients [11], [20], the degree constraint is related to the maximal number of TCP connections that a server can handle using QoS and the capacity of the server is defined as its overall outgoing bandwidth. This resource allocation problem also has applications in the context of Cloud Computing [31], [28], [12]. In this case, servers represent physical machines and clients represent services, which can be deployed on the servers by using one or more virtual machines (VMs).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Resource Allocation under Degree Constraints

Beaumont

Eyraud-Dubois

Thraves

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In this paper, we consider the problem of assigning a set of clients with demands to a set of servers with capacities and degree constraints. The goal is to find an allocation such that the number of clients assigned to a server is smaller than the server's degree and their overall demand is smaller than the server's capacity, while maximizing the overall throughput. This problem has several natural applications in the context of independent tasks scheduling or virtual machines allocation. We consider both the offline (when clients are known beforehand) and the online (when clients can join and leave the system at any time) versions of the problem. We first show that the degree constraint on the maximal number of clients that a server can handle is realistic in many contexts. Then, our main contribution is to prove that even if it makes the allocation problem more difficult (NP-Complete), a very small additive resource augmentation on the servers degree is enough to find in polynomial time a solution that achieves at least the optimal throughput. After a set of theoretical results on the complexity of the offline and online versions of the problem, we propose several other greedy heuristics to solve the online problem and we compare the performance (in terms of throughput) and the cost (in terms of disconnections and reconnections) of proposed algorithms through a set of extensive simulation results.

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Section: Introductionmentioning

confidence: 99%

Heterogeneous Resource Allocation under Degree Constraints

Beaumont

Eyraud-Dubois

Thraves

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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“…Calheiros et al [5] have investigated the problem of mapping VMs on physical nodes optimizing network communication between VMs, however, the problem has not been explored in the context of energy consumption minimization. Recently, a number of research works have been done on thermal efficient resource management in data centers [14,9].…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Resource Management in Virtualized Cloud Data Centers

Beloglazov

Buyya

2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

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Rapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. We propose an energy efficient resource management system for virtualized Cloud data centers that reduces operational costs and provides required Quality of Service (QoS). Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. We present first results of simulation-driven evaluation of heuristics for dynamic reallocation of VMs using live migration according to current requirements for CPU performance. The results show that the proposed technique brings substantial energy savings, while ensuring reliable QoS. This justifies further investigation and development of the proposed resource management system.

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“…In the case of CloudSim [4], several research articles have been published with results obtained by using it [2,5,6,15]. This tool was initially based on a Grid simulator [4] (this being GridSim [27]).…”

Section: Introductionmentioning

confidence: 99%

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

et al. 2012

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Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastruc-tures with remarkable features such as flexibility, scalability, performance and usability. Further-more, the iCanCloud simulator has been built on the following design principles: (1) it's targeted to conduct large experiments, as opposed to oth-ers simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.Keywords Cloud computing · Cloud computing simulator · Cloud hypervisor · Validation · Scalability 1 to solve a given computational problem. If the same software and configurations are needed, the VMs may be started using the same image. This way, a machine offered by a computing cloud may become whatever the user needs, from a standalone computer to a cluster or Grid node.Nowadays, cloud computing systems are increasing their role due to the fast (r)evolution of computer networks and communication technologies. A very clear proof of this fact is that very important companies like Amazon, Google, Dell, IBM, and Microsoft are investing billions of dollars in order to provide their own cloud solutions [28].As soon as the scientific community had access to cloud production infrastructures, the first applications started to run on the cloud [26,34]. In many Research areas, the leap from traditional cluster and Grid computing to this new paradigm has been mandatory, being the main reason an evolution in the computational needs of the applications [10]. A remarkable fact from this evolution is that in a pre-cloud environment, hardware defines the level of parallelism of an application. In cloud computing, the level of parallelism is defined by the application itself, as there is no restriction in the number of machines, and CPU availability is 100% guaranteed by standard.There

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A Heuristic for Mapping Virtual Machines and Links in Emulation Testbeds

Cited by 31 publications

References 10 publications

Heterogeneous Resource Allocation under Degree Constraints

Heterogeneous Resource Allocation under Degree Constraints

Energy Efficient Resource Management in Virtualized Cloud Data Centers

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

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