• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.9
• /
• pp.4320-4333
• /
• 2017

Energy-efficient virtual resource allocation algorithm has become a hot research topic in cloud computing. However, most of the existing allocation schemes cannot ensure each type of resource be fully utilized. To solve the problem, this paper proposes a virtual machine (VM) allocation algorithm on the basis of multi-dimensional resource, considering the diversity of user's requests. First, we analyze the usage of each dimension resource of physical machines (PMs) and build a D-dimensional resource state model. Second, we introduce an energy-resource state metric (PAR) and then propose an energy-aware multi-dimensional resource allocation algorithm called MRBEA to allocate resources according to the resource state and energy consumption of PMs. Third, we validate the effectiveness of the proposed algorithm by real-world datasets. Experimental results show that MRBEA has a better performance in terms of energy consumption, SLA violations and the number of VM migrations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5234-5251
• /
• 2018

It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

• Journal of Digital Convergence
• /
• v.14 no.10
• /
• pp.303-310
• /
• 2016

Due to the ossification of the Internet, it is difficult to accommodate variety services. One of the efficient solution to this problem is network virtualization. It allows multiple parallel virtual networks to run on the shared physical infrastructure. It needs new resource allocation mechanism to share efficient physical resources. In this paper, we present efficient bandwidth allocation algorithm for virtual network request with high service priority. Our proposed algorithm can withdraw allocated bandwidth from low-level priority virtual network and maintain low-level virtual network service. We evaluated the performance of our proposed algorithm using simulation and found the improvement of approximately 8% acceptance rate.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.8
• /
• pp.3841-3861
• /
• 2017

Virtualized small cell network is a promising architecture which can realize efficient utilization of the network resource. However, conventional full duplex self-backhauls lead to residual self-interference, which limits the network performance. To handle this issue, this paper proposes a virtual resource allocation, in which the residual self-interference is fully exploited by employing a physical-layer network coding (PNC) aided self-backhaul scheme. We formulate the features of PNC as time slot and information rate constraints, and based on that, the virtual resource allocation is formulated as a mixed combinatorial optimization problem. To solve the problem efficiently, it is decomposed into two sub problems, and a two-phase iteration algorithm is developed accordingly. In the algorithm, the first sub problem is approximated and transferred into a convex problem by utilizing the upper bound of the PNC rate constraint. On the basis of that, the convexity of the second sub problem is also proved. Simulation results show the advantages of the proposed scheme over conventional solution in both the profits of self-backhauls and utility of the network resource.

• The KIPS Transactions:PartA
• /
• v.19A no.3
• /
• pp.161-168
• /
• 2012

The study of Cloud based system is emerging to become the core technology in IT field due to the tremendous growth of Cloud Computing. Researches to deliver applications to Thin-Client based mobile virtual machine and Desktop as a Service(DaaS) using Cloud Computing are conducted actively. In this paper, we propose a Cloud system to run the mobile application in the mobile Thin-Client device and resource allocation mechanism Dynamic Resource Allocation Manager for Mobile Application(DRAMMA). Thus, through performance check, we show DRAMMA has improved the utilization of Cloud system, less migration of virtual machines and decreased the error rate of resource allocation. Also our proposed system delivers service more efficiently than the previous resource allocation algorithm.

• Journal of KIISE:Computer Systems and Theory
• /
• v.35 no.5
• /
• pp.187-198
• /
• 2008

This paper develops a framework for efficient resource management within the grid service environment. Resource management is the core element of the grid service; therefore, GridRMF(Grid Resource Management Framework) is modeled and developed in order to respond to such variable characteristics of resources as accordingly as possible. GridRMF uses the participation level of grid resource as a basis of its hierarchical management. This hierarchical management divides managing domains into two parts: VMS(Virtual Organization Management System) for virtual organization management and RMS(Resource Management System) for metadata management. VMS mediates resources according to optimal virtual organization selection mechanism, and responds to malfunctions of the virtual organization by LRM(Local Resource Manager) automatic recovery mechanism. RMS, on the other hand, responds to load balance and fault by applying resource status monitoring information into adaptive performance-based task allocation algorithm.

• Journal of Internet Computing and Services
• /
• v.18 no.4
• /
• pp.51-59
• /
• 2017

With the growth of Cloud computing, it is important to consider resource management techniques to minimize the overall costs of management. In cloud environments, each host's utilization and virtual machine's request based on user preferences are dynamic in nature. To solve this problem, efficient allocation method of virtual machines to hosts where the classification of virtual machines and hosts is undetermined should be studied. In reducing the number of active hosts to reduce energy consumption, thresholds can be implemented to migrate VMs to other hosts. By using Fuzzy logic in classifying resource requests of virtual machines and resource utilization of hosts, we proposed an adaptive VM allocation and migration approach. The allocation strategy classifies the VMs according to their resource request, then assigns it to the host with the lowest resource utilization. In migrating VMs from overutilized hosts, the resource utilization of each host was used to create an upper threshold. In selecting candidate VMs for migration, virtual machines that contributed to the high resource utilization in the host were chosen to be migrated. We evaluated our work through simulations and results show that our approach was significantly better compared to other VM allocation and Migration strategies.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.5
• /
• pp.1146-1154
• /
• 2010

A virtual machine lease service in the cloud computing environment has been implemented using the open source cloud computing platform, OpenNebula. In addition, a web-based cloud user interface is developed for both convenient resource management and efficient service access. The present virtual machine allocation scheme adopted in OpenNebula has performance reduction problem because of not considering CPU allocation scheduler of the virtualization software. In order to address this problem we have considered both the priority of the idle CPU resources of the cluster and credit scheduler of Xen, which resulted in performance improvement of the OpenNebula virtual machine scheduler. The experimental results showed that the proposed allocation scheme provided more virtual machine creations and more CPU resource allocations for cloud service.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.5
• /
• pp.443-450
• /
• 2013

We consider user pairing and resource allocation for the uplink of cellular systems employing virtual multiple input multiple output (MIMO). As a multiple access scheme, discrete Fourier transform spread orthogonal frequency division multiple access (DFTS-OFDMA) is adopted for more flexible resource allocation than single carrier (SC)-OFDMA adopted in the Long Term Evolution (LTE) system. We formulate the optimization problems of user pairing and resource allocation to maximize the throughput of the DFTS-OFDMA system under different constraints. The DFTS-OFDMA allowing non-contiguous subcarrier allocation and redundant user assignment provides a better throughput than the SC-FDMA at lower complexity in finding the optimal solution but at the cost of the increased control information indicating the allocated resources.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.8
• /
• pp.1825-1842
• /
• 2013

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.