Routing Protocols Evaluation Review in Simple and Cloud Environment

In the field of information technology there are many computer jargons like cloud computing Ad-hoc, Software Define Network (SDN), network function virtualization (NFV) , and virtual machine (VM), etc. This review paper is basically a blend of brief study and review of many routing protocols used for Mobile ad hoc Networks (MANET) in the cloud as well as in simple network environment i.e. without cloud computing. This paper would also suggest the different challenges that are facing in cloud computing. The description of the different network simulators used in networking like NS2 tool, Opnet and Cisco packet tracer. The different metrics that are used in the networking are briefly explained. MANET is a group of wireless nodes that do not need centralized controlling entity as it rapidly moveschanges and forms networks to the nearest networking nodes

A Literature Survey on Resource Management Techniques, Issues and Challenges in Cloud Computing

Cloud computing is a large scale distributed computing which provides on demand services for clients. Cloud Clients use web browsers, mobile apps, thin clients, or terminal emulators to request and control their cloud resources at any time and anywhere through the network. As many companies are shifting their data to cloud and as many people are being aware of the advantages of storing data to cloud, there is increasing number of cloud computing infrastructure and large amount of data which lead to the complexity management for cloud providers. We surveyed the state-of-the-art resource management techniques for IaaS (infrastructure as a service) in cloud computing. Then we put forward different major issues in the deployment of the cloud infrastructure in order to avoid poor service delivery in cloud computing

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Self-managing cloud-native applications : design, implementation and experience

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles

Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature

Policy-based Information Sharing using Software-Defined Networking in Cloud Systems

Cloud Computing is rapidly becoming a ubiquitous technology. It enables an escalation in computing capacity, storage and performance without the need to invest in new infrastructure and the maintenance expenses that follow. Security is among the major concerns of organizations that are still reluctant to adopt this technology: The cloud is dynamic, and with so many different parameters involved, it is a diffi cult task to regulate it. With an approach that blends Usage Management and Statistical Learning, this research yielded a novel approach to mitigate some of the issues arising due to questionable security, and to regulate performance (utilization of resources).This research also explored how to enforce the policies related to the resources inside a Virtual Machine(VM), apart from providing initial access control. As well, this research compared various encryption schemes and observed their behavior in the cloud. We considered various components in the cloud to deduce a multi-cost function, which in turn helps to regulate the cloud. While guaranteeing security policies in the cloud, it is essential to add security to the network because the virtual cloud and SDN tie together. Enforcing network-wide policies has always been a challenging task in the domain of communication networks. Software-defined networking (SDN) enables the use of a central controller to define policies, and to use each network switch to enforce policies. While this presents an attractive operational model, it uses a very low-level framework, and is not suitable for directly implement- ing high-level policies. Therefore, we present a new framework for defining policies and easily compiling them from a user interface directly into OpenFlow actions and usage management system processes. This demonstrated capability allows cloud administrators to enforce both network and usage polices on the cloud