Cloud Environment

With the rapid development and popularization of information technology, cloud computing technology provides a good environment for solving massive data processing. Hadoop is an open-source implementation of MapReduce and has the ability to process large amounts of data. Aiming at the shortcomings of the fault-tolerant technology in the MapReduce programming model, this paper proposes a reliability task scheduling strategy that introduces a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment, establishes a trustworthiness model, and avoids task allocation to low reliability node, causing the task to be re-executed, wasting time and resources. Finally, the simulation platform CloudSim verifies the validity and stability of the task scheduling algorithm and scheduling model proposed in this paper.

MapReduce is a distributed computing model for cloud computing to process massive data. It simplifies the writing of distributed parallel programs. For the fault-tolerant technology in the MapReduce programming model, tasks may be allocated to nodes with low reliability. It causes the task to be reexecuted, wasting time and resources. This paper proposes a reliability task scheduling strategy with a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment and builds a trustworthiness model. By using the simulation platform CloudSim, the stability of the task scheduling algorithm and scheduling model are verified in this paper.

Service-oriented architectures are widely considered to be the determining trend in software engineering. Vendors of software products want to benefit by migrating to cloud environments. However, when transforming an existing software system from the Software as a Product model to the Software as a Service model the software engineering process changes. While the process in general has been researched suf iciently, very low effort has been put into understanding the impact on requirements elicitation. This paper investigates the necessary changes in the requirements engineering process and provides a systematic approach for a successful transformation. Furthermore, it discusses the new benefits in requirements elicitation that are inherent in a cloud environment. The paper then discusses the identified problems and developed solutions with regards to deduced guidelines and best practices. We conclude that the requirements engineering process profits from a systematic transformation when migrating a traditional software product to the Software as a Service model.

Cloud computing is the new generation technology provides the way of sharing of resources, memory, software anything in the form of service using internet. Subsequently, Cloud computing provides several advantages over the traditional network philosophy Involvement of public network make it sensitive for several security threat such replay attack, eavesdropping, man-in-middle attack. Basic function of any file system is to provide a long time reliable storage. Similar to a local based file system, Cloud file system stores files on one or more computers called servers, and makes them accessible to other computers called as clients, on the intranet in form of service. Most cloud computing services fall into three broad categories: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (Saas). These are sometimes called the cloud computing stack, because they build on top of one another. Security is an important and unique phenomenon gives safe and isolated environment. Security model and principles are defined to implement security features with any applications. Confidentiality, authentication and integrity are primary principles for trust establishment. Existing work only concentrates on confidentiality concept during communication and does not imposes for integrity and privacy during storage. A Secure storage can help to improve trust of user on cloud servers as well CSP and others. This work will implement security service architecture Create safe and secure storage environment for cloud computing using web services and java technology.

Energy conservation in data centers has been an active research area in cloud computing in recent times. Effective energy conservation can be achieved using server consolidation, which aims at utilizing server resources efficiently and minimizing the number of active Physical Machines (APMs) running in a data center. Effective placement of virtual machines is necessary to optimize server consolidation. Virtual machine placement techniques provide a suitable mapping of hosts to VMs to reduce energy consumption and minimize SLA violation in data centers. This paper presents a comprehensive survey of different Virtual Machine placement techniques utilized in cloud computing, revealing the advantages and limitations of the algorithms. I. INTRODUCTION Cloud computing provides access to on demand computing resources to the users in a pay-as-you-use pricing model. Three different models are being offered by cloud service providers such as IaaS (Infrastructure as a service), PaaS (Platform as a service) and SaaS (Software as a service). One of the significant challenges for cloud service providers is to reduce energy consumption in data centers. The cloud service providers spend a significant amount in setting up data centers in the beginning. They have to incur data center management costs later to maintain data centers. This includes power costs, software and hardware maintenance costs etc. According to a recent study [1], 13% of the overall data center management cost is incurred by power consumption. So, it is essential to optimize power consumption in data centers to reduce the operational cost for cloud service providers. To prevent wastage of resources in data centers, Virtual Machines (VM) are packed on to the fewest possible physical machines and idle physical machines are later shut down, thereby reducing energy consumption. This process of consolidating VMs on to the servers is called server consolidation. It comprises of 4 steps: 1) Host underload detection, where hosts with utilization under a certain threshold are selected, all the VMs on the host are migrated to other servers, and underloaded hosts are shutdown. 2) Host overload detection, where hosts with utilization greater than a certain threshold are detected and some of the VMs are migrated to other hosts. 3) VM selection, where appropriate VMs are selected for migration from over utilized hosts. 4) VM placement, where VMs selected for migration in the 3 rd step is mapped to different Physical Machines (PM). In this paper, we focus on Virtual Machine Placement algorithms. Virtual machine placement (VMP) is the process of mapping Virtual machines to Physical machines in order to reduce energy consumption and minimize SLA violation in data centers. VMP has been an active research area in cloud computing throughout the last decade. Many VMP algorithms have been proposed to maximize utilization and to reduce power consumption, in turn reducing operational costs in data centers. VMP algorithms can be traffic-aware, load-aware, application-aware, power-aware or a combination of these. To achieve better performance, VMs are migrated to other hosts when servers become over utilized or underutilized. So, when the resource demands of a Virtual machine cannot be fulfilled by the physical machine on which the VM is hosted, VMs are migrated to another PM for the fulfillment of the demands.. VMs are migrated from over utilized hosts to prevent Service level Agreement violation. In the case of underutilized hosts, all the VMs hosted on the PM are migrated and the host is shut down. The remainder of this paper is organized as follows. Section II describes the classification of VM placement algorithms. Section III presents a detailed discussion of different approaches used in VM placement algorithms and Section IV presents concluding remarks and future research directions. II. VM PLACEMENT CLASSIFICATION A. Power and Quality of Service 1) Power based: The objective of power based virtual machine placement algorithm is to map virtual machines to physical machines in a manner to reduce energy consumption in data centers. Virtual machines are aggressively packed in physical machines and underutilized physical machines are shut down to reduce power consumption [2]. 2) QoS based: The objective of this approach is to meet the quality of service guaranteed by cloud service providers. Service Level Agreement (SLA) is signed between the user and cloud service provider when users opt for cloud services. Service provider will have to pay the penalty when they fail to deliver quality of service. QoS based approaches are used to minimize SLA violation, in turn ensure quality of service to the customers [3].