A Pattern-Based Prediction Model for Dynamic Resource Provisioning in Cloud Environment

Kim, Hyukho

doi:10.3837/tiis.2011.10.002

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Resource allocation and Scheduling is required when multiple nodes participate in the offloading process [22], [23]. When the workload is heavier or when there is a dynamic change in the wireless environment due to mobility, then proper provisioning of the resources to the available dedicated servers requires a scheduling algorithm [5].…”

Section: Issues and Challengesmentioning

confidence: 99%

Computational Analytics of Client Awareness for Mobile Application Offloading with Cloud Migration

2014

KSII TIIS

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Smartphone applications like games, image processing, e-commerce and social networking are gaining exponential growth, with the ubiquity of cellular services. This demands increased computational power and storage from mobile devices with a sufficiently high bandwidth for mobile internet service. But mobile nodes are highly constrained in the processing and storage, along with the battery power, which further restrains their dependability. Adopting the unlimited storage and computing power offered by cloud servers, it is possible to overcome and turn these issues into a favorable opportunity for the growth of mobile cloud computing. As the mobile internet data traffic is predicted to grow at the rate of around 65 percent yearly, even advanced services like 3G and 4G for mobile communication will fail to accommodate such exponential growth of data. On the other hand, developers extend popular applications with high end graphics leading to smart phones, manufactured with multicore processors and graphics processing units making them unaffordable. Therefore, to address the need of resource constrained mobile nodes and bandwidth constrained cellular networks, the computations can be migrated to resourceful servers connected to cloud. The server now acts as a bridge that should enable the participating mobile nodes to offload their computations through Wi-Fi directly to the virtualized server. Our proposed model enables an on-demand service offloading with a decision support system that identifies the capabilities of the client's hardware and software resources in judging the requirements for offloading. Further, the node's location, context and security capabilities are estimated to facilitate adaptive migration.

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Section: Issues and Challengesmentioning

confidence: 99%

Computational Analytics of Client Awareness for Mobile Application Offloading with Cloud Migration

2014

KSII TIIS

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“…Searching the similar patterns each time over the entire set of historical data is time consuming. To solve this problem, in 2011, Kim et al [11] proposed a prediction model that gives more weightage to recent user requests than older ones. Their work is based on current user request patterns.…”

Section: Introductionmentioning

confidence: 99%

Function points‐based resource prediction in cloud computing

Sood

2014

Concurrency and Computation

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As a result of varying demands of computing resources by the users on cloud, resource provisioning in cloud computing has come out as a prominent topic of research. Many researchers have focused exclusively on the technical and security aspects of cloud computing, thereby neglecting the efficient provisioning of resources and the necessity of cloud services to be cost effective. Cloud consists of a large number of resources that are allocated to cloud customer's on-demand. As demands cannot be static and constantly change with time, cloud service providers cannot adopt static provisioning of resources as there are chances of overprovisioning or under-provisioning. Therefore, to achieve efficient resource utilization, an optimized strategy that can deploy virtual machines on different physical machines according to resource requirements is the current need of cloud computing. That is, there must be a mechanism by which the total number of active physical nodes can be dynamically changed corresponding to their resource usage rate, thereby providing the efficient utilization of resources. In this paper, a linear regression-based prediction model is proposed to predict the resource usage based on the number of function points computed from the users' requests. Thereafter, the artificial neural network is also used to predict the future resource requirements more accurately. The predicted resource usage results are used by a resource pool manager to manage the resources and allocate them to the users. The resource pool manager also uses an efficient load-balancing algorithm to balance the load on each cloud service provider as well as to optimize cloud usage cost. With the help of this prediction model, the decision to allocate or release a virtual machine can be made proactively, thus making the cloud effective in terms of both cost and performance. capital costs. Moreover, it eliminates the overhead of procuring traditional infrastructure resources, which typically takes several months [1,2].Cloud can provision its resources dynamically as per the requirements of the end user. It provides virtually unlimited computational resources to end users, and the users have to pay only for the resources they actually use at any given time interval. Cloud providers offer resources to the users on demand using various schemes based on on-demand instances, reserved instances and spot instances. Cloud services such as Amazon EC2 and Google App Engine are designed to take the advantage of their already existing infrastructure [3].The major contribution in the success of cloud computing is of virtualization technology. Virtualization technologies allow one to create multiple virtual machines (VMs) on one or more physical machines. Infrastructure-as-a-service providers run as many as possible VMs to fully utilize the capacity of their resources. Once VMs are assigned on physical machines, they are ready to run applications, which provide services to the cloud customers through the Internet. As the number of user requests cannot rema...

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“…데이터센터는 다수의 VM(Virtual Machine)으로 구 성되어 있고 VM은 클라우드 시스템에서 사용자에게 할당 할 수 있는 자원의 최소단위이다 [2,5,6,7] (이하 자원 이라 칭함). 데이터센터에 도착한 서비스 처리를 위하 여 클라우드 시스템은 다수의 자원을 할당 할 수 있 다.…”

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Dynamic Resource Allocation in Distributed Cloud Computing

Ahn

Kim

Lee

2013

The Journal of Korea Information and Communications Society

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A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.※ 이 논문은 2013년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임

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A Pattern-Based Prediction Model for Dynamic Resource Provisioning in Cloud Environment

Cited by 6 publications

References 29 publications

Computational Analytics of Client Awareness for Mobile Application Offloading with Cloud Migration

Computational Analytics of Client Awareness for Mobile Application Offloading with Cloud Migration

Function points‐based resource prediction in cloud computing

Dynamic Resource Allocation in Distributed Cloud Computing

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