Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
Nowadays, the usage of mobile devices is progressively increased. Until, delay\nsensitive applications (Augmented Reality, Online Banking and 3D Game)\nare required lower delay while executed in the mobile device. Mobile Cloud\nComputing provides a rich resource environment to the constrained-resource\nmobility to run above mentioned applications, but due to long distance between\nmobile user application and cloud server introduces hybrid delay (i.e. ,\nnetwork delay and process delay). To cope with the hybrid delay in mobile\ncloud computing for delay sensitive applications, we have proposed novel\nhybrid delay task assignment (HDWA) algorithm. The preliminary objective\nof the HDWA is to run the application on the cloud server in an efficient way\nthat minimizes the response time of the application. Simulation results show\nthat proposed HDWA has better performance as compared to baseline approaches....
This research work aims at modelling a framework for Private Cloud infrastructure\nDeployment for Information and Communication Technology\nCentres (ICTs) in tertiary institutions in Nigeria. Recent researches have indicated\nthat cloud computing will become the mainstream in computing\ntechnology and very effective for businesses. All Tertiary Institutions have\nICT units, and are generally charged with the responsibilities of deploying\nICT infrastructure and services for administration, teaching, research and\nlearning in higher institution at large. The Structured System Analysis and\nDesign Methodology (SSADM) is used in this research and a six-step framework\nfor a cost effective and scalable Private cloud infrastructure using server\nvirtualization is presented as an alternative that can guarantee total and independent\ncontrol of data flow in the institutions, while ensuring adequate security\nof vital information....
This paper proposed a multi-keyword ciphertext search, based on an improved-quality\nhierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to\nwork with encrypted data. It has improved search accuracy and can self-adapt when performing\nmulti-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document\nvectors are first generated by combining the term frequency-inverse document frequency (TF-IDF)\nweight factor and the vector space model (VSM). The improved quality hierarchical clustering\n(IQHC) algorithm then generates document vectors, document indices, and cluster indices, which\nare encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k\nsearch result. A series of experiments proved that the proposed method had better searching\nefficiency and accuracy in high-privacy sensor cloud network environments, compared to other\nstate-of-the-art methods....
The cloud-computing concept has emerged as a powerful mechanism for data storage by\nproviding a suitable platform for data centers. Recent studies show that the energy consumption of\ncloud computing systems is a key issue. Therefore, we should reduce the energy consumption to\nsatisfy performance requirements, minimize power consumption, and maximize resource utilization.\nThis paper introduces a novel algorithm that could allocate resources in a cloud-computing\nenvironment based on an energy optimization method called Sharing with Live Migration (SLM).\nIn this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs)\nbased on a novel algorithm that learns and predicts the similarity between the tasks, and then\nallocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services\n(QoS) constraints of the hosted applications by adopting a migration process. The experimental\nresults show that the algorithm exhibits better performance, while saving power and minimizing the\nprocessing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency\nand resource utilization compared to an adapted state-of-the-art algorithm for a similar problem....
Cloud computing systems are popular in computing industry for their ease of use and wide\nrange of applications. These systems offer services that can be used over the Internet. Due to their\nwide popularity and usage, cloud computing systems and their services often face issues resource\nmanagement related challenges. In this paper, we present v-Mapper, a resource consolidation\nscheme which implements network resource management concepts through software-defined\nnetworking (SDN) control features. The paper makes three major contributions: (1) We propose a\nvirtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for\ndata-intensive applications; (2) We propose a validation scheme that will ensure that a cloud service\nis entertained only if there are sufficient resources available for its execution and (3) We present a\nscheduling policy that aims to eliminate network load constraints. We tested our scheme with other\ntechniques in terms of average task processing time, service delay and bandwidth usage. Our results\ndemonstrate that v-Mapper outperforms other techniques and delivers significant improvement in\nsystemâ??s performance....
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