2 The Challenge More and more companies decide to dynamically allocate resources in the cloud. In many cases, this is the so-called public cloud. Typically, it takes only a few minutes to allocate and use the required computing power, and only the resources actually utilized are charged. Alternatively, it is possible to obtain processing resources from a private cloud or an enterprise / corporate cloud. As opposed to the public cloud, the private providers are able to offer greater security, in particular as concerns data protection as well as monitoring and auditing capabilities. On the downside, disadvantages include a loss in flexibility and higher costs. Also, the offering in the private cloud is usually not available within a few minutes and cannot be booked for the short term. This is why companies have to decide on a suitable cloud product based on their specific security needs and application scenario. In many cases, a hybrid cloud strategy that allows customers to combine different cloud offerings according to their needs represents the best solution. In order to ensure that resources can be controlled in a manner best suited for the task at hand, companies require an intelligent and largely automated control tool. Our Solution Figure 1: Cloud typology StoneOne s Cloud Extender expands the functional range of the Web Service Factory, making the technology available for heterogeneous cloud infrastructures. For each server, one or multiple Enterprise Service Bus (ESB) instances are executed. Every single instance (typically a VM) is administrated internally by means of a bus controller. This controller interconnects the various instances by allocating each instance a bus and a customized selection of components. Within this distributed system landscape, each subsystem has one controller that acts as the master controller. When requested, this master controller draws on the Cloud Extender to connect to a selected cloud. In general, the Cloud Extender offers various functions, such as providing access to the various clouds, allocating resources there, deploying ESB instances for the corresponding resource and, lastly, connecting this instance with the active system.
3 In this context, various other components of the Web Service Factory also come into play: Bus Controller, Master Controller, Billing&Tracking and, if applicable, Payment as well. Technical Workflows An ESB application comprises various components that process technical tasks according to a defined workflow. During each processing step, these tasks are placed in a queue and are then processed by the corresponding component one by one. In general, it does not matter where the components are located, so a logical application can be distributed across multiple ESB instances. The ESB instances themselves can also be distributed across different providers, thus establishing the basis for dynamic distribution and scaling. An ESB controller monitors the queues and throughput times of its allocated ESB instance and communicates with an ESB master controller. The ESB master controller receives an alert from an ESB controller in the event a specified threshold is exceeded. The master controller is aware of whether there is at least one ESB instance with a suitable configuration and will request it to help carry out the given task. Figure 2: Load overview / trends
4 This is generally implemented as a pull process, which means that the addressed instance decides whether it can/ wants to offer processing capacity as requested. This (automatically) ensures consensual cooperation for processes involving multiple providers. As already mentioned, instances can be distributed among various providers. At present, there is, however, no uniform standard to specify the interfaces required for resource allocation. The Cloud Extender delivers the specific interface to the respective cloud provider. It connects with the master controller and allocates according to the relevant thresholds and selected policy one or multiple new virtual machines that help to process jobs. The cloud support takes place in several stages: Figure 3: Load distribution process for two ESB instances Integrate an ESB executed on a different machine Allocate/Power up a machine that provides a preconfigured ESB Allocate/Power up a generic machine that executes the ESB Depending on the use of additional resources, individual machines or instances are powered down again if the load drops below a certain level.
5 This is controlled using defined criteria (policies). For example, they can ensure stable, fast response times for applications with rapidly growing numbers of demo accounts. Basically, the question of how many machines are requested at what time is determined based on technical stipulations (e.g. guaranteed response times) as well as economic factors (e.g. profits generated from processing additional requests on time exceed the costs for using the cloud). The ESB controller monitors the size of the queues, the processing times of individual components, and possibly other parameters as well. The ESB master controller regulates the expansion of the ESB into the hybrid public or private cloud according to the policies in place. If the demand drops, the Cloud Extender downsizes the system by terminating the use of some resources. In this context, cloud is referring both to public clouds, such as Amazon EC2, as well as to private clouds or IaaS solutions based on VMware, VirtualBox etc. The policies are flexible and can be quickly adjusted to the market situation, allowing users to operate as economically as possible at all times. Figure 4: Policies for applications Policies enable the Cloud Extender as part of the master controller to choose between multiple defined cloud providers. For this to happen, relevant profile information is captured for each provider and for each offered machine class of the provider. This information can then be referenced accordingly in the policy definitions. The parameters of a profile can include factors such as performance, cost, spatial proximity or even ecological parameters like energy consumption, temperature etc.
6 Economic Parameters Possible parameters for regulation according to economic considerations include: Available base budget per time unit (e.g. $500 per month) Possible budget increase (transactions collected via payment) based on percentage for cross-financing (e.g. 5% of earnings from CorDesk application are added to the operating budget) Maximum budget per month (upper limit also applies to cross-financing) Boundary parameters for resource allocation (e.g. data only in home country) Simple spending policy, such as - Minimum reserve for remaining days of the month, per day - Maximum amount per day Technical Parameters Technical control parameters: Maximum runtime of application Y s transaction X: ZZZ millisec. (here, runtime refers to the time from queuing until processing is completed) Maximum average runtime of application Y s transaction X Cloud-Provider Management Managing settings/offers of each provider, such as data center (DC), cloud provider: Cost per resource acc. to granularity: - Core number - System memory - Disk memory - Miscellaneous Allocation runtime (minutes until resource is available) Budget settings generate audit entries. Key data of each available resource provider (DC, cloud, etc.) can be viewed and configured.
7 Reporting Clearly-structured dashboards indicate how the utilization of the various providers cloud resources is distruibuted over time. The dashboards also show the scope of use and the resulting price for each cloud or resource. Figure 5: Reporting
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