Disaster recovery (DR) is one of most important use cases that we hear from our customers. Having your own DR site in the cloud ready and on standby, without having to pay for the hardware, power, bandwidth, cooling, space and system administration and quickly launch resources in cloud, when you really need it (when disaster strikes in your datacenter) makes the AWS cloud the perfect solution for DR. You can quickly recover from a disaster and ensure business continuity of your applications while keeping your costs down.ĭisaster recovery is about preparing for and recovering from a disaster. This could be hardware or software failure, a network outage, a power outage, physical damage to a building like fire or flooding, human error, or some other significant disaster.ĪWS cloud not only makes it cost-effective to do DR in the cloud but also makes it easy, secure and reliable.Īny event that has a negative impact on your business continuity or finances could be termed a disaster. With APIs and right automation in place, you can fire up and test whether your DR solution really works and be prepared ahead of time. You can reduce your recovery times by quickly provisioning pre-configured resources (AMIs) when you need them or cut-over to an already provisioned DR site (and then scaling gradually as you need). You can bake the necessary security best practices into an AWS CloudFormation template and provision the resources in an Amazon Virtual Private Cloud (VPC). < 99.95% but equal to or greater than 99.0% Benefits of Using of AWS Disaster Recovery All at the fraction of the cost of conventional DR. Experimental results show that Fuzzy-Q&E out performs Dyna QoS both in terms of the ability of meeting the QoS level of the application, and of the amount of physical capacity allocated to each VM.The AWS cloud supports many popular disaster recovery (DR) architectures from “pilot light” environments that may be suitable for small customer workload data center failures to “hot standby” environments that enable rapid failover at scale. We implement a prototype of our controller on a Xen-based testbed, and we perform a set of experiments using an E-Commerce benchmark in which we compare Fuzzy-Q&E against Dyna QoS, a state-of-the-art fuzzy controller for virtualized resources. 0 Comments Times Group is using the service under its own brand, running in their own local datacentre, but managed remotely by SMX. In this way, the number of physical resources that must be switched-on at any given time is reduced with respect to the case in which physical machines are statically provisioned and, consequently, less energy is required to run a given cloud workload. We propose a fuzzy controller, Fuzzy-Q& E, that is able to allocate to the VMs of each cloud application the minimum amount of physical capacity needed to meet its QoS requirements. We address the problem of managing cloud applications, consisting of a set of virtual machines (VMs), characterized by bursty and dynamic workloads, in such a way to provide guarantees on their Quality-of-Services (QoS) and, at the same time, to minimize the energy consumption of the physical infrastructure running them. As subscription's status is not active, so that is the reason you are unable to cancel it. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described. Hi Alyssemarie, We have checked your account information and found that the subscription (Export PDF) registered to your account is no longer active. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. This chapter presents an overview of the infra-structure resource management systems and technologies, and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastage. Intelligent workload placement and relocation is one of the primary means to address these issues. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. In order to meet the increasing demand of computing and storage resources, infra-structure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. With immense success and rapid growth within the last few years, cloud computing has been established as the dominant paradigm of IT industry.
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