High Adaptive Fault Tolerance in Real Time Cloud Computing

2014

Cloud Hospital Shared access to resources and Remote computing service for users creates. Cloud to provide services to many users at the same time, the strategy plan should be developed for multiple tasks. In cloud computing, the processing is done on the remote computer, the greater the chance of errors due to Defined latency-free control there are calculations on the node. The remote computer must be highly reliable. Need to increase to achieve fault tolerance and reliability for real-time computing, cloud infrastructure there. This paper mainly deals with e check model for cloud computing and fault tolerance fault-tolerance model for cloud computing (FTMC) are described. FTMC the model is based on reliability, fault tolerance, each node is calculated. Your calculator to calculate a node is selected based on its reliability or if it is not done well for applications that can be removed.

Cloud computing has emerged as a platform that grants users with direct yet shared access to remote computing resources and services. Cloud must provide services to many users at the same time; the scheduling strategy should be developed for multiple tasks. In cloud computing processing is done on remote computer hence there are more chances of errors, due to the undetermined latency and loose control over computing node. Hence remote computers should be highly reliable. This is reason why a cloud computing infrastructure should be fault tolerant as well scheduling properly to performing tasks. This paper mainly deals with a fault tolerance model for cloud computing & Paper describes model for Fault Tolerance in Cloud computing (FTMC) FTMC model tolerates the faults on the basis of reliability of each computing node. A Computing node is selected for computation on the basis of its reliability and can be removed, if does not perform well for applications.

Cloud Computing is the environment which can be used with minimum efficiency way that provides on-demand access of the network to a computing resources like networks, storage, applications, servers and the other services. There are many issues of cloud computing system which are discussed in this paper in brief. The main aim of this paper is to focus on fault tolerance in cloud computing and recover fault with less processing time. In this, when the node which is performing task moves from its original position then that tasks are assigned to other nodes. The major problem in this is task scheduling, if one slave node get failed the task allocated by master node will not get completed and fault occurred. In this paper, we will discuss about technique which helps to reduce fault tolerance of the system and increase performance of the system.

Cloud computing has emerged as a platform that grants users with direct yet shared access to remote computing resources and services. Cloud must provide services to many users at the same time; the scheduling strategy should be developed for multiple tasks. In cloud computing processing is done on remote computer hence there are more chances of errors, due to the undetermined latency and loose control over computing node. Hence remote computers should be highly reliable. This is reason why a cloud computing infrastructure should be fault tolerant as well scheduling properly to performing tasks. This paper mainly deals with a fault tolerance model for cloud computing & Paper describes model for Fault Tolerance in Cloud computing (FTMC) FTMC model tolerates the faults on the basis of reliability of each computing node. A Computing node is selected for computation on the basis of its reliability and can be removed, if does not perform well for applications.

Information and Communication Technology for Competitive Strategies

Nowadays, to a large extent, clients look at cloud not just as service provider but also as partner. So, they want cloud to deliver timely and accurate services. Cloud nodes must be reliable in order to provide quality of services as per the customer requirements. Further, physical size of high-performance computing environment is also increasing day by day. Larger the system, more failures are likely to occur that eventually results in the poor reliability of the system which is highly undesirable for the time-critical applications. To deal with the reliability, service provider must know the failure characteristics of the cloud computing nodes in order to better handle the failure using fault-tolerance-aware techniques at the time of scheduling the application tasks. Thus, in this paper, we presented the survey of fault-tolerance-aware techniques which are classified as proactive and reactive fault tolerance. This survey provides the foundation for the researchers to work in the area of fault-tolerance-aware scheduling in order to have better scheduling decisions with the aim to enhance the performance and reliability of application execution. Keywords Reliability ⋅ Fault tolerance ⋅ Virtualization 1 Introduction Cloud is an Internet-based computing paradigm that provides basic services as Infrastructure as a Service (IaaS), Software as a Service (SaaS), Platform as a Service (PaaS) [1]. Different types of cloud providers, i.e., public, private, or hybrids, are responsible for providing above services to user. Nowadays, usage of

IEEE Transactions on Cloud Computing, 2016

The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach. Index Terms-Cloud data center, cloud service reliability, fault tolerance (FT), particle swarm optimization (PSO), virtual cluster Ç 1 INTRODUCTION C LOUD computing is widely adopted in current professional and personal environments. It employs several existing technologies and concepts, such as virtual servers and data centers, and gives them a new perspective [1]. Furthermore, it enables users and businesses to not only use applications without installing them on their machines but also access resources on any computer via the Internet [2]. With its pay-per-use business model for customers, cloud computing shifts the capital investment risk for under-or overprovisioning to cloud providers. Therefore, several leading technology companies, such as Google, Amazon, IBM, and Microsoft, operate large-scale cloud data centers around the world. With the growing popularity of cloud computing, modern cloud data centers are employing tens of thousands of physical machines (PMs) networked via hundreds of routers/switches that communicate and coordinate to deliver highly reliable cloud computing services. Although the failure probability of a single device/link might be low [3], it is magnified across all the devices/links hosted in a cloud data center owing to the problem of coordination of PMs. Moreover, multiple fault sources (e.g., software, human errors, and hardware) are the norm rather than the exception [4]. Thus, downtime is common and seriously affects the service level of cloud computing [5]. Therefore, enhancing cloud service reliability is a critical issue that requires immediate attention. Over the past few years, numerous fault tolerance (FT) approaches have been proposed to enhance cloud service reliability [6], [7]. It is well known that FT consists of fault detection, backup, and failure recovery, and nearly all FT approaches are based on the use of redundancy. Currently, two basic mechanisms, namely, replication and checkpointing, are widely adopted. In the replication mechanism, the same task is synchronously or asynchronously handled on several virtual machines (VMs) [8], [9], [10]. This mechanism ensures that at least one replica is able to complete the task on time. Nevertheless, because of its high implementation cost, the replication mechanism is more suitable for real time or critical cloud services. The checkpointing mechanism is categorized into two main types: independent checkpoint mechanisms that only consider a whole application to perform on a VM, and coordinated checkpoint mechanisms that consider multiple VMs (i.e., a virtual cluster) to jointly execute parallel applications [11], [12], [13], [14], [15], [16]. The two types of mechanisms periodically save the

Cloud computing is the next generation computing. The GUI which controls the cloud computing makes is directly controlling the hardware resource and application. This paper based on a survey of different kind of faults and their prevention methods. There are several methods used to avoiding faults in real time distributed systems and cloud computing, that survey is devoted to study of introductory concepts and techniques for designing and analyzing fault tolerant systems. Fault tolerant system is one that can provide continue correct performance of its specified tasks in the presence of hardware and/or software faults.

Journal of Telecommunication, Electronic and Computer Engineering, 2017

Based on pay-as-per-usage policy, there is a tremendous use of cloud computing in scientific society like bio-medical, healthcare and online financial applications. Fault tolerance is one of the biggest challenges to guarantee the reliability and availability of critical services. We must make the system to avail by minimizing the impact of failure. In this paper, we conducted a comparative analysis of various approaches for tolerating faults through scheduling in cloud computing environment based on their policies. The goal of this paper is not only used to analyze the existing methods, but also to identify the areas needed for future research.

2024

The capital cost of purchasing hardware, software and setting up and maintaining on-site data centers-including the server racks, round-the-clock electricity for power and cooling, and the IT professionals for managing the infrastructure-is eliminated by using cloud computing. Cloud computing allows IT to respond to business needs faster. If an application needs more resources or capacity, It can be rapidly and simply modified in the cloud. Another essential aspect of cloud computing is fault tolerance, which measures how well a system can continue to function in the face of mistakes or failures. This survey will help acknowledge the main fault tolerance issues and the loopholes in the traditional fault tolerance techniques that still need attention. This survey gives us insight into several methods that can be used to cater the challenges regarding fault tolerance.

Due to emergence of cloud computing, many traditional issues have been eliminated or reduced. Cloud computing is on demand computing in which processing is done on remote computer hence the chances of errors is more due to communication delay, latency or loss of control over computing node. These are the important issue that's why cloud computing infrastructure should be fault tolerant as well as designed to schedule tasks must be properly handled. The present paper deals with the understanding of fault tolerance techniques in cloud environments and comparison with various models on various parameters have been done. Fault tolerance technique is studied with the help of well known object-oriented language Unified Modeling Language and state diagrams are designed and validated through the concepts of Finite State Machine.