Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Cloud computing provides variety of services with the growth of their offerings. Due to efficient services, it faces numerous challenges. It is based on virtualization, which provides users a plethora computing resources by internet without managing any infrastructure of Virtual Machine (VM). With network virtualization, Virtual Machine Manager (VMM) gives isolation among different VMs. But, sometimes the levels of abstraction involved in virtualization have been reducing the workload performance which is also a concern when implementing virtualization to the Cloud computing domain. In this paper, it has been explored how the vendors in cloud environment are using Containers for hosting their applications and also the performance of VM deployments. It also compares VM and Linux Containers with respect to the quality of service, network performance and security evaluation.

Performance analysis of virtual machines and containers in cloud computing

With the growing popularity of cloud computing the number of cloud service providers and services have significantly increased. Thus selecting the best cloud services becomes a challenging task for prospective cloud users. The process of selecting cloud services involves various factors such as characteristics and models of cloud services, user requirements and knowledge, and service level agreement (SLA), to name a few. This paper investigates into the cloud service selection tools, techniques and models by taking into account the distinguishing characteristics of cloud services. It also reviews and analyses academic research as well as commercial tools in order to identify their strengths and weaknesses in the cloud services selection process. It proposes a framework in order to improve the cloud service selection by taking into account services capabilities, quality attributes, level of user's knowledge and service level agreements. The paper also envisions various directions for future research.

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Trends and Directions in Cloud Service Selection

The crucial role of networking in Cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both Cloud computing and networking enables a convergence of network and Cloud service provisioning. One of the key challenges to network -- Cloud convergence lies in QoS-aware composition of network and Cloud services. In this paper, we propose a QoS-aware service composition method to tackle this challenging issue. We first present a system model for network -- Cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then develop an algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-Cloud service composition. Performance of the proposed algorithm is evaluated through extensive simulation experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best currently available MCOP approach.

QoS-Aware Service Composition for Converged Network-Cloud Service Provisioning

The crucial role of networking in cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both cloud computing and networking enables a convergence of network and cloud service provisioning. One of the key challenges to high performance converged network-cloud service provisioning lies in composition of network and cloud services with end-to-end performance guarantee. In this paper, we propose a QoS-aware service composition approach to tackling this challenging issue. We first present a system model for network-cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-cloud service composition. Performance of the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best current MCOP approaches.

Converged Network-Cloud Service Composition with End-to-End Performance Guarantee

Routing with multiple quality-of-services constraints: An approximation perspective

Cloud computing is one of the most significant latest efforts in the field of information technology, which may change the way how information services are provisioned. In a Cloud environment, different types of resources need to be virtualized as a collection of Cloud services using virtualization technology. End-users in the Cloud are usually provided with customized Cloud services that involve not only different kinds of computing services but also the networks interconnecting those computing services. Therefore, a set of Cloud computing services and the networking services should be modeled as a composite customized Cloud service. In this paper, we present an improved model for Cloud service provisioning based on our previous Network-Cloud proposal, and propose a procedure with several QoS-aware service selection algorithms for composing different services offered by a Cloud. Our analysis with numerical experiments show that the presented algorithms can select services appropriately that deal with different requirements of service provisioning.

QoS-aware service selection in virtualization-based Cloud computing

Routing with end-to-end Quality-of-Service (QoS) guarantees is a key to the widespread deployment of recent emerged services. Developing QoS routing algorithm in the network is an important open topic. This paper investigates the QoS routing related problems and proposes a Fully Polynomial Time Approximation Scheme (FPTAS) for QoS routing. In the proposed FPTAS, a graph-extending based dynamic programming approach is developed, and an extended version of the proposed algorithm is studied. The theoretical analyses show that the proposed algorithms outperform the previous best-known studies.

QoS routing algorithms using fully polynomial time approximation scheme

On a high-dimensional objective genetic algorithm and its nonlinear dynamic properties

Last few years have witnessed the development of various multi-objective evolutionary algorithms since they allow the generation of the overall Pareto front for multi-objective optimizations. With the problems in the real-world becoming more and more complex, however, no reported work in the literature focuses on the high-dimensional objective optimizations (HOPs). In this paper, we propose an evolutionary algorithm named HOEA (high-dimensional objective evolutionary algorithm) for HOPs. By adopting the concept of nonlinear definition for optimizing object, HOPs can be solved by HOEA, while the well-known multi-objective evolutionary algorithms work poorly on HOPs. We further analyze the nonlinear dynamic properties of HOEA on the basis of martingale theoretical framework. The theoretical results indicate that this new algorithm is indeed capable of achieving convergence. We also conduct experiments on HOEA with two representative test instances. The experimental results either confirm our theoretical results or show that the proposed algorithm is efficient and effective for HOPs.

Jun Huang

Papers

Routing with multiple quality-of-services constraints: An approximation perspective

QoS-aware service selection in virtualization-based Cloud computing

QoS routing algorithms using fully polynomial time approximation scheme

On a high-dimensional objective genetic algorithm and its nonlinear dynamic properties

High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis