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Many-Objective Resource Allocation for Elastic Infrastructures in Overbooked Cloud Computing Datacenters Under Uncertainty

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  • Universidad Internacional Tres Fronteras
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With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks facing them have become more and more extensive. The frequent occurrence of network security incidents has caused huge losses. Facing an increasingly severe situation, it is necessary to adopt various network security technologies to solve the problem. Intrusion detection technology can detect internal and external network attacks, respond before the intrusion occurs, and send out alarm information for timely and effective processing. This article mainly introduces the research of cloud computing intrusion detection technology based on BP neural network (BP-NN), and intends to provide ideas and directions for the development of cloud computing intrusion detection technology based on BP-NN. This paper proposes research methods of cloud computing intrusion detection technology based on BP-NN, including BP-NN algorithm, neural network cloud computing intrusion detection technology and artificial bee colony optimization algorithm, which are used to conduct cloud computing intrusion detection technology experiment based on BP-NN; Proposed an artificial bee colony optimization neural network algorithm; designed a cloud computing intrusion detection system based on BP-NN. Experimental result shows that the average detection rate of the ABC-BP network algorithm is 92.67 %, which can effectively distinguish normal data from abnormal data.
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Cloud computing datacenters dynamically provide millions of virtual machines in real-world cloud computing environments. A large number of research challenges have to be addressed toward an efficient resource management of these cloud computing infrastructures. In the resource allocation field, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and a large number of existing optimization criteria, considering solutions with high economical and ecological impact. Based on systematic reviews of the VMP literature, a taxonomy of VMP problem environments is presented to understand different possible environments where a VMP problem could be considered, from both provider and broker perspectives in different deployment architectures. Additionally, another taxonomy for VMP problems is presented to identify existing approaches for the formulation and resolution of the VMP as an optimization problem. Finally a detailed view of the VMP problem is presented, identifying research opportunities to further advance in cloud computing resource allocation areas.
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Resource allocation in cloud computing datacenters presents several research challenges, where the Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations considering a large number of existing optimization criteria. This chapter presents the main contributions that studied for the first time Many-Objective VMP (MaVMP) problems for cloud computing environments. In this context, two variants ofMaVMP problems were formulated and different algorithms were designed to effectively address existing research challenges associated to the resolution of Many-Objective Optimization Problems (MaOPs). Experimental results proved the correctness of the presented algorithms, its effectiveness in solving particular associated challenges and its capabilities to solve problem instances with large numbers of physical and virtual machines for: (1) MaVMP for initial placement of VMs (static) and (2) MaVMP with reconfiguration of VMs (semi-dynamic). Finally, open research problems for the formulation and resolution of MaVMP problems for cloud computing (dynamic) are discussed.
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Infrastructure as a Service (IaaS) providers must support requests for virtual resources in highly dynamic cloud computing environments. Due to the randomness of customer requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. This work presents a novel two-phase optimization scheme for the resolution of VMP problems for cloud computing under uncertainty of several relevant parameters, combining advantages of online and offline formulations in dynamic environments considering service elasticity and overbooking of physical resources. In this context, a formulation of a VMP problem is presented, considering the optimization of the following four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. The proposed two-phase optimization scheme includes novel methods to decide when to trigger a placement reconfiguration through migration of virtual machines (VMs) between physical machines (PMs) and what to do with VMs requested during the placement recalculation time. An experimental evaluation against state-of-the-art alternative approaches for VMP problems was performed considering 400 scenarios. Experimental results indicate that the proposed methods outperform other evaluated alternatives, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.
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Infrastructure as a Service (IaaS) providers must support requests for virtual resources in complex dynamic cloud computing environments, taking into account service elasticity and overbooking of physical resources. Due to the randomness of customer requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. This work proposes an experimental evaluation of a two-phase optimization scheme for VMP problems, studying different (i) online heuristics, (ii) overbooking protection factors and (iii) objective function scalarization methods. The proposed experimental evaluation considers an uncertain VMP formulation for the optimization of the following three objective functions: (i) power consumption, (ii) economical revenue, and (iii) resource utilization. Experiments were performed considering 96 different scenarios, representing complex cloud computing environments. Experimental results shows that Best-Fit and Best-Fit Decreasing heuris-tics are recommended in the incremental VMP (iVMP) phase working with the considered Memetic Algorithm in the VMP reconfiguration (VMPr) phase, adjusting protection factors to 0.00 and 0.75 in low and high CPU load scenarios respectively, while scalarazing the proposed three objective functions considering an Euclidean distance to the origin.
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The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.
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Cloud computing datacenters provide millions of virtual machines (VMs) in actual cloud markets. Nowadays, efficient location of these VMs into available physical machines (PMs) represents a research challenge, considering the large number of existing formulations and optimization criteria. Several techniques have been studied for the Virtual Machine Placement (VMP) problem. However, each article performs experiments with different datasets, making difficult the comparison between different formulations and solution techniques. Considering the absence of a highly recognized and accepted benchmark to study the VMP problem, this work proposes and implements a Workload Generator to enable the generation of different instances of the VMP problem for cloud computing environments, based on different configurable parameters. Additionally, this work also provides a set of pre-generated instances of the VMP that facilitates the comparison of different solution techniques of the VMP problem for the most diverse dynamic environments identified in the state-of-the-art.
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Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of fi�ve diff�erent online deterministic heuristics against an offl�ine memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offl�ine memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement recon�gurations.
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In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and drawbacks in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area. There are few research work reported in the literature for VM placement. Those methods might look fine at a glance, but a deeper scrutiny can expose various anomalies and drawbacks which might affect the performance of the system. Most of them devise a metric, which is a function of resource utilizations of individual resource types. They use this metric for placement and migration of VMs as well as for load balancing and consolidation of servers. In this paper, we present various methodologies used in the literature for VM placement, server load balancing and server consolidation and point out the drawbacks and anomalies in those methodologies and discuss the root cause of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some inter- esting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.