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Parallel Implementation of K-Means on Multi-Core Processors
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ژورنال:Computer Networks
سال:December 2015
قیمت اصلی:35.59$
The growth of cloud computing and the need to support the ever increasing number of applications introduces new challenges and gives rise to various optimization problems, such as calculating the number and location of virtual machines instantiating cloud services to minimize a well-defined cost function. This paper introduces a novel cloud computing network architecture that allows for the formulation of the optimization as an Uncapacitated Facility Location (UFL) problem, where a facility corresponds to an instantiation of a particular service (e.g. a virtual machine). Since UFL is not only difficult (NP-hard and requires global information), but also its centralized solution is non-scalable, the approach followed here is distributed and elastic, and relays local information to improve scalability. In particular, virtual machine replication and merging are proposed and analyzed ensuring overall cost reduction. In addition, a policy that employs virtual machine replication and merging along with migration is proposed to reduce the overall cost for using a service. The efficiency of this policy and its limitations are analyzed and discussed, with simulation results supporting the analytical findings and demonstrating a significant overall cost reduction when the proposed policy is implemented.
ژورنال: Computer Standards & Interfaces
سال: November 2015
قیمت اصلی:35.95
Identify the risk of cloud computing operations.
Identify the risk audit of cloud computing operations.
Identify the audit standards of cloud computing operations.
Integrating the risk and audit process of cloud computing practice.
Cloud computing has gained mass popularity in the business environment. However, this technology also imposes some risk concerns, such as weak protection to security and privacy. Since its nature of distant and remote connectivity, the auditing process to this technology becomes challengeable. This paper focuses on issues related to cloud computing risk and audit tasks.
ژورنال:Computer Networks
سال: November 2015
قیمت اصلی:35.95$
Cloud computing offers on-demand network access to the computing resources through virtualization. This paradigm shifts the computer resources to the cloud, which results in cost savings as the users leasing instead of owning these resources. Clouds will also provide power constrained mobile users accessibility to the computing resources. In this paper, we develop performance models of these systems. We assume that jobs arrive to the system according to a Poisson process and they may have quite general service time distributions. Each job may consist of multiple numbers of tasks with each task requiring a virtual machine (VM) for its execution. The size of a job is determined by the number of its tasks, which may be a constant or a variable. The jobs with variable sizes may generate new tasks during their service times. In the case of constant job size, we allow different classes of jobs, with each class being determined through their arrival and service rates and number of tasks in a job. In the variable case a job generates randomly new tasks during its service time. The latter requires dynamic assignment of VMs to a job, which will be needed in providing service to mobile users. We model the systems with both constant and variable size jobs using birth–death processes. In the case of constant job size, we determined joint probability distribution of the number of jobs from each class in the system, job blocking probabilities and distribution of the utilization of resources for systems with both homogeneous and heterogeneous types of VMs. We have also analyzed tradeoffs for turning idle servers off for power saving. In the case of variable job sizes, we have determined distribution of the number of jobs in the system and average service time of a job for systems with both infinite and finite amount of resources. We have presented numerical results and any approximations are verified by simulation. The results of the paper may be used in the dimensioning of cloud computing centers.
ژورنال:Big Data Research
سال:November 2015
قیمت اصلی:27.95$
Critical infrastructure systems perform functions and missions that are essential for our national economy, health, and security. These functions are vital to commerce, government, and society and are closely interrelated with people's lives. To provide highly secured critical infrastructure systems, a scalable, reliable and robust threat monitoring and detection system should be developed to efficiently mitigate cyber threats. In addition, big data from threat monitoring systems pose serious challenges for cyber operations because an ever growing number of devices in the system and the amount of complex monitoring data collected from critical infrastructure systems require scalable methods to capture, store, manage, and process the big data. To address these challenges, in this paper, we propose a cloud computing based network monitoring and threat detection system to make critical infrastructure systems secure. Our proposed system consists of three main components: monitoring agents, cloud infrastructure, and an operation center. To build our proposed system, we use both Hadoop MapReduce and Spark to speed up data processing by separating and processing data streams concurrently. With a real-world data set, we conducted real-world experiments to evaluate the effectiveness of our developed network monitoring and threat detection system in terms of network monitoring, threat detection, and system performance. Our empirical data indicates that the proposed system can efficiently monitor network activities, find abnormal behaviors, and detect network threats to protect critical infrastructure systems.