scholarly journals Availability-Aware Multi-Objective Cluster Allocation Optimization in Energy-Efficient Datacenters

2021 ◽  
Vol 9 (VI) ◽  
pp. 3498-3507
Author(s):  
Mr. N. B. Kadu
Keyword(s):  
Virtual Machines ◽  
Network Virtualization ◽  
Virtual Cluster ◽  
Network Resources ◽  
Slowing Down ◽  
Cloud Application ◽  
Service Oriented ◽  
Elastic Computing ◽  
Virtual System ◽  
Cluster Allocation

With increasing network virtualization, data centre's workloads are modified in depth to serve various service-oriented applications, often defined by a time-bound service response, which, in turn, places a heavy demand on data center networks. Network virtualization in computing is the technique of integrating network resources and network functions in hardware and software into one virtual network, the software-based administration entity. Number of people ask for the server simultaneously, thereby slowing down the service.It is so costly to buy a new server that we developed a virtual system by creating a virtual system. With a trend to increase the number of cloud apps in the datacenter. There are numerous physical machines (PMs) linked via switches in the datacenter. Hardware PM resources for adaptable and elastic computing capabilities are usually shared via virtualization technology. Usually a cloud application is implemented in a virtual cluster that includes many virtual machines which occupy PM resources on request.

TESTING AND FORMAL VERIFICATION OF SERVICE ORIENTED ARCHITECTURES

2009 ◽  
Vol 16 (02) ◽  
pp. 137-162 ◽  
Author(s):  
JOHN C. SLOAN ◽  
TAGHI M. KHOSHGOFTAAR
Keyword(s):  
Formal Verification ◽  
Software Agents ◽  
Third Party ◽  
Software Quality Assurance ◽  
Middle Ground ◽  
Network Resources ◽  
Service Oriented Architectures ◽  
Engineering Problems ◽  
Service Oriented

We examine two open engineering problems in the area of testing and formal verification of internet-enabled service oriented architectures (SOA). The first involves deciding when to formally and exhaustively verify versus when to informally and non-exhaustively test. The second concerns scalability limitations associated with formal verification, to which we propose a semi-formal technique that uses software agents. Finally, we assess how these findings can improve current software quality assurance practices. Addressing the first problem, we present and explain two classes of tradeoffs. External tradeoffs between assurance, performance, and flexibility are determined by the business needs of each application, whether it be in engineering, commerce, or entertainment. Internal tradeoffs between assurance, scale, and level of detail involve the technical challenges of feasibly verifying or testing an SOA. To help decide whether to exhaustively verify or non-exhaustively test, we present and explain these two classes of tradeoffs. Identifying a middle ground between testing and verification, we propose using software agents to simulate services in a composition. Technologically, this approach has the advantage of assuring the quality of compositions that are too large to exhaustively verify. Operationally, it supports testing these compositions in the laboratory without access to source code or use of network resources of third-party services. We identify and exploit the structural similarities between agents and services, examining how doing so can assure the quality of service compositions.

scholarly journals A Multiserver User-space Unikernel for a Distributed Virtualization System

2019 ◽  
Author(s):  
Pablo Pessolani
Keyword(s):  
Service Oriented Architecture ◽  
Virtual Machines ◽  
Management Tools ◽  
Resource Requirements ◽  
Service Oriented ◽  
Small Footprint ◽  
Memory Network ◽  
User Space ◽  
Attack Surface ◽  
And Performance

Nowadays, most Cloud applications are developed using Service Oriented Architecture (SOA) or MicroService Architecture (MSA). The scalability and performance of them is achieved by executing multiple instances of its components in different nodes of a virtualization cluster. Initially, they were deployed in Virtual Machines (VMs) but, they required enough computational, memory, network and storage resources to hold an Operating System (OS), a set of utilities, libraries, and the application component. By deploying hundreds of these application components, the resource requirements increase a lot. To minimize them, usually small footprint OS are used. Later, as management tools were improved, the application components began to be deployed in Containers which require even less resources than VMs. Another way to reduce the resource requirements is integrating the application components in a Unikernel. This article proposes a Unikernel called MUK, based on a multiserver OS, to be used as a tool to integrate Cloud application components. MUK was built to run in user-space inside a Container of a Distributed Virtualization System. Both technologies facilitate the scattering of application components in a virtualization cluster keeping the isolation properties and minimal attack surface of a Unikernel.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

The Business Model Analysis of Service-Oriented Enterprise

2014 ◽  
Vol 701-702 ◽  
pp. 1257-1262
Author(s):  
Li Wang ◽  
Jing Zhang
Keyword(s):  
Business Model ◽  
Resource Integration ◽  
Value Proposition ◽  
Network Resources ◽  
Customer Demand ◽  
Profit Model ◽  
Related Literature ◽  
Value Network ◽  
Model Based ◽  
Service Oriented

In this paper, we proposed value proposition, value network, resources, services and profit model as business model elements of Service-oriented Enterprise from the perspective of system by combing the related literature about business model elements. Based on this, we put forward four kinds of building ideas: business model based on customer demand, business model based on resource integration, business model based on service content, business model based on profit model and then explained by cases. Finally, the application of environmental of business model was compared. This study is help to guide other service-oriented enterprises to construct business model effectively.

scholarly journals Interoperable Resource Management for Establishing Federated Clouds

2012 ◽  
pp. 18-35 ◽  
Author(s):  
Gabor Kecskemeti ◽  
Attila Kertesz ◽  
Attila Marosi ◽  
Peter Kacsuk
Keyword(s):  
Virtual Machine ◽  
Virtual Machines ◽  
Research Areas ◽  
Computing Paradigm ◽  
Virtual Appliance ◽  
Service Deployment ◽  
Cloud Providers ◽  
Service Oriented ◽  
Cloud Infrastructures ◽  
Privately Owned

Cloud Computing builds on the latest achievements of diverse research areas, such as Grid Computing, Service-oriented computing, business process modeling and virtualization. As this new computing paradigm was mostly lead by companies, several proprietary systems arose. Recently, alongside these commercial systems, several smaller-scale privately owned systems are maintained and developed. This chapter focuses on issues faced by users with interests in Multi-Cloud use and by Cloud providers with highly dynamic workloads. The authors propose a Federated Cloud Management architecture that provides unified access to a federated Cloud that aggregates multiple heterogeneous IaaS Cloud providers in a transparent manner. The architecture incorporates the concepts of meta-brokering, Cloud brokering, and on-demand service deployment. The meta-brokering component provides transparent service execution for the users by allowing the interconnection of various Cloud brokering solutions. Cloud-Brokers manage the number and the location of the Virtual Machines performing the user requests. In order to decrease Virtual Machine instantiation time and increase dynamism in the system, the service deployment component optimizes service delivery by encapsulating services as virtual appliances allowing their decomposition and replication among IaaS Cloud infrastructures. The architecture achieves service provider level transparency through automatic virtual appliance replication and Virtual Machine management of Cloud-Brokers.

SAMEVED

2013 ◽  
Vol 5 (2) ◽  
pp. 27-42 ◽  
Author(s):  
Shao-Jui Chen ◽  
Jing-Ying Huang ◽  
Cheng-Ta Huang ◽  
Wei-Jen Wang
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Web Interface ◽  
Computing Paradigm ◽  
Elastic Computing ◽  
Virtual Routers ◽  
User Friendly ◽  
Cloud Applications ◽  
Remote Invocation

Cloud computing is an emerging computing paradigm that provides all kinds of services through the Internet. Existing elastic computing approaches are popular in cloud computing. They can fulfill the requirements of some cloud applications, but usually fail to provide an isolated computing environment consisting of connected virtual machines over a user-defined network topology. This paper presents a system architecture, namely SAMEVED, which exposes a cloud service that can allocate and manage a private, virtual elastic datacenter by integrating VPN and virtual routers into existing virtualization technologies. Authentication is required by user login while using SAMEVED. A user-friendly web interface and remote invocation interface are provided to support different operations for different users with different privileges.

Network Slice Provisioning Approaches for Industry Verticals

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Business Models ◽  
Network Virtualization ◽  
Use Cases ◽  
Use Case ◽  
Costs And Benefits ◽  
Network Resources ◽  
Network Slicing ◽  
Strategic Approach ◽  
New Business ◽  
New Business Models

Network slicing is widely studied as an essential technological enabler for supporting diverse use case specific services through network virtualization. Industry verticals, consisting of diverse use cases requiring different network resources, are considered key customers for network slices. However, different approaches for network slice provisioning to industry verticals and required business models are still largely unexplored and require further work. Focusing on technical and business aspects of network slicing, this article develops three new business models, enabled by different distributions of business roles and management exposure between business actors. The feasibility of the business models is studied in terms of; the costs and benefits to business actors, mapping to use cases in various industry verticals, and the infrastructure costs of common and dedicated virtualization infrastructures. Finally, a strategic approach and relevant recommendations are proposed for major business actors, national regulatory authorities, and standards developing organizations.

Cloud-Based Platforms and Infrastructures

2019 ◽  
pp. 84-126
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

Performance Analysis of Virtual Computer Network Based on Cisco Cloud Services Router 1000V in a Private Cloud Environment

2015 ◽  
Vol 7 (2) ◽  
pp. 117-132
Author(s):  
Stanislaw Lota ◽  
Marcin Markowski
Keyword(s):  
Virtual Machines ◽  
Computer Network ◽  
New Technology ◽  
Network Virtualization ◽  
Cloud Services ◽  
Virtual Network ◽  
Production Networks ◽  
Private Cloud ◽  
Testing And Debugging ◽  
Virtual Platform

Abstract Virtualization of physical network devices is a relatively new technology, that allows to improve the network organization and gives new possibilities for Software Defined Networking (SDN). Network virtualization is also commonly used for testing and debugging environments, before implementing new designs in production networks. Important aspect of network virtualization is selecting virtual platform and technology, that offer maximal performance with minimal physical resource utilization. This article presents a comparative analysis of performance of the virtual network created by the virtual CSR1000v and virtual machines running Windows 8.1 on two different virtual private cloud platforms: VMware vSphere 5.5 and Microsoft Hyper-V Server 2012 R2. In such prepared testbed we study the response time (delay) and throughput of virtual network devices.

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