Techniques for improving cloud infrastructure backup in a shared storage environment

A technique for cloud infrastructure backup in a virtualized environment utilizing shared storage includes obtaining a workload input/output (I/O) profile to the shared storage over a time period. An attempt to locate one or more time windows in the workload I/O profile for which a cloud infrastructure backup can be staged is initiated. In response to determining the cloud infrastructure backup can be staged during at least one of the time windows, staging of the cloud infrastructure backup is scheduled during a selected one of the time windows. In response to determining the cloud infrastructure backup cannot be staged during at least one of the time windows, an interference tolerance approach is employed for accessing the shared storage for active workloads and the cloud infrastructure backup during the staging of the cloud infrastructure backup.

BACKGROUND

The present invention generally relates to techniques for improving cloud infrastructure backup and, more specifically, to techniques for improving cloud infrastructure backup in a shared storage environment.

In general, cloud computing refers to Internet-based computing where shared resources, software, and information are provided to users of computer systems and other electronic devices (e.g., mobile phones) on demand, similar to the electricity grid. Adoption of cloud computing has been aided by the widespread utilization of virtualization, which is the creation of a virtual (rather than actual) version of something, e.g., an operating system, a server, a storage device, network resources, etc. A virtual machine (VM) is a software implementation of a physical machine (PM), e.g., a computer system, that executes instructions like a PM. VMs are usually categorized as system VMs or process VMs. A system VM provides a complete system platform that supports the execution of a complete operating system (OS). In contrast, a process VM is usually designed to run a single program and support a single process. A VM characteristic is that application software running on the VM is limited to the resources and abstractions provided by the VM. System VMs (also referred to as hardware VMs) allow the sharing of the underlying PM resources between different VMs, each of which executes its own OS. The software that provides the virtualization and controls the VMs is typically referred to as a VM monitor (VMM) or hypervisor. A hypervisor may run on bare hardware (Type 1 or native VMM) or on top of an operating system (Type 2 or hosted VMM).

Cloud computing provides a consumption and delivery model for information technology (IT) services based on the Internet and involves over-the-Internet provisioning of dynamically scalable and usually virtualized resources. Cloud computing is facilitated by ease-of-access to remote computing websites (e.g., via the Internet or a private corporate network) and frequently takes the form of web-based tools or applications that a cloud consumer can access and use through a web browser, as if the tools or applications were a local program installed on a computer system of the cloud consumer. Commercial cloud implementations are generally expected to meet quality of service (QoS) requirements of consumers and typically include service level agreements (SLAs). Cloud consumers avoid capital expenditures by renting usage from a cloud vendor (i.e., a third-party provider). In a typical cloud implementation, cloud consumers consume resources as a service and pay only for resources used.

BRIEF SUMMARY

Disclosed are a method, a data processing system, and a computer program product (embodied in a computer-readable storage medium) for improving cloud infrastructure backup in a shared storage environment.

A technique for cloud infrastructure backup in a virtualized environment utilizing shared storage includes obtaining a workload input/output (I/O) profile to the shared storage over a time period. An attempt to locate one or more time windows in the workload I/O profile for which a cloud infrastructure backup can be staged is initiated. In response to determining the cloud infrastructure backup can be staged during at least one of the time windows, staging of the cloud infrastructure backup is scheduled during a selected one of the time windows. In response to determining the cloud infrastructure backup cannot be staged during at least one of the time windows, an interference tolerance approach is employed for accessing the shared storage for active workloads and the cloud infrastructure backup during staging of the cloud infrastructure backup.

DETAILED DESCRIPTION

The illustrative embodiments provide a method, a data processing system, and a computer program product (embodied in a computer-readable storage medium) for improving cloud infrastructure backup in a shared storage environment.

It is understood that the use of specific component, device and/or parameter names are for example only and not meant to imply any limitations on the invention. The invention may thus be implemented with different nomenclature/terminology utilized to describe the components/devices/parameters herein, without limitation. Each term utilized herein is to be given its broadest interpretation given the context in which that term is utilized. As may be utilized herein, the term ‘coupled’ encompasses a direct electrical connection between components or devices and an indirect electrical connection between components or devices achieved using one or more intervening components or devices.

It should be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. A cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

Cloud characteristics may include: on-demand self-service; broad network access; resource pooling; rapid elasticity; and measured service. Cloud service models may include: software as a service (SaaS); platform as a service (PaaS); and infrastructure as a service (IaaS). Cloud deployment models may include: private cloud; community cloud; public cloud; and hybrid cloud.

On-demand self-service means a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with a service provider. Broad network access means capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and personal digital assistants (PDAs)). Resource pooling means computing resources of a provider are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. In resource pooling there is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

Rapid elasticity means capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale-out and be rapidly released to quickly scale-in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. Measured service means cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction that is appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

In an SaaS model the capability provided to the consumer is to use applications of a provider that are running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). In the SaaS model, the consumer does not manage or control the underlying cloud infrastructure (including networks, servers, operating systems, storage, or even individual application capabilities), with the possible exception of limited user-specific application configuration settings.

In a PaaS model a cloud consumer can deploy consumer-created or acquired applications (created using programming languages and tools supported by the provider) onto the cloud infrastructure. In the PaaS model, the consumer does not manage or control the underlying cloud infrastructure (including networks, servers, operating systems, or storage), but has control over deployed applications and possibly application hosting environment configurations.

In an IaaS service model a cloud consumer can provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software (which can include operating systems and applications). In the IaaS model, the consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

In a private cloud deployment model the cloud infrastructure is operated solely for an organization. The cloud infrastructure may be managed by the organization or a third party and may exist on-premises or off-premises. In a community cloud deployment model the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). The cloud infrastructure may be managed by the organizations or a third party and may exist on-premises or off-premises. In a public cloud deployment model the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

In a hybrid cloud deployment model the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). In general, a cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

As shown inFIG. 1, computer system/server12(in cloud computing node10) is illustrated in the form of a general-purpose computing device. The components of computer system/server12may include, but are not limited to, one or more processors or processing units (including one or more processor cores)16, a system memory28, and a bus18that couples various system components (including system memory28) to processors16. Bus18represents one or more of any of several types of bus structures, including a memory bus or memory controller bus, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include the industry standard architecture (ISA) bus, the micro channel architecture (MCA) bus, the enhanced ISA (EISA) bus, the video electronics standards association (VESA) local bus, and the peripheral components interconnect (PCI) bus.

Computer system/server12typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server12, and includes both volatile and non-volatile media, removable and non-removable media. System memory28can include computer system readable media in the form of volatile memory, such as random access memory (RAM)30and/or cache memory32.

As will be further depicted and described herein, memory28may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various disclosed embodiments. Program/utility40, having a set (at least one) of program modules42, may be stored in memory28by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules42generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server12may also communicate with one or more external devices14such as a keyboard, a pointing device, a display24, one or more other devices that enable a user to interact with computer system/server12, and/or any devices (e.g., network card, modem, etc.) that enable computer system/server12to communicate with one or more other computing devices. Such communication can occur via input/output (I/O) interfaces22. Still yet, computer system/server12can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter20communicates with the other components of computer system/server12via bus18. It should be understood that although not shown, other hardware and/or software components can be used in conjunction with computer system/server12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant array of inexpensive disk (RAID) systems, tape drives, and data archival storage systems, etc.

With reference toFIG. 3, a set of functional abstraction layers provided by cloud computing environment50(FIG. 2) is shown. It should be understood that the components, layers, and functions shown inFIG. 3are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted inFIG. 3, cloud computing environment50includes a hardware and software layer60, a virtualization layer62, a management layer64, and a workloads layer66.

Hardware and software layer60includes various hardware and software components. As one example, the hardware components may include mainframes (e.g., IBM® zSeries® systems), reduced instruction set computer (RISC) architecture based servers (e.g., IBM® pSeries® systems), IBM® xSeries® systems, IBM® BladeCenter® systems, storage devices, networks and networking components. As another example, the software components may include network application server software (e.g., IBM® WebSphere® application server software) and database software (e.g., IBM® DB2® database software). IBM, zSeries, pSeries, xSeries, BladeCenter, WebSphere, and DB2 are trademarks of International Business Machines Corporation registered in many jurisdictions worldwide.

Virtualization layer62provides an abstraction layer in which virtual entities (e.g., virtual servers, virtual storage, virtual networks (including virtual private networks), virtual applications and operating systems, and virtual clients are included. As previously discussed, these virtual entities may be accessed by clients of cloud computing environment50on-demand. The virtual entities are controlled by one or more virtual machine monitors (VMMs) that may, for example, be implemented in hardware and software layer60, virtualization layer62, or management layer64.

Management layer64provides various functions (e.g., resource provisioning, metering and pricing, security, user portal, service level management, and SLA planning and fulfillment). The resource provisioning function provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. For example, the resource provisioning function may be performed for virtual machines (VMs) by one or more VMMs. The metering and pricing function provides cost tracking (as resources are utilized within the cloud computing environment) and billing or invoicing for consumption of the utilized resources. As one example, the utilized resources may include application software licenses.

The security function provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. The user portal function provides access to the cloud computing environment for consumers and system administrators. The service level management function provides cloud computing resource allocation and management such that required service levels are met. For example, the security function or service level management function may be configured to limit deployment/migration of a virtual machine (VM) image to geographical location indicated to be acceptable to a cloud consumer. The service level agreement (SLA) planning and fulfillment function provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

The term ‘cloud infrastructure backup’ refers to storing ‘cloud management data’ to a secure location. The term ‘cloud infrastructure backup’ does not encompass backing up cloud user/customer local files into a remote cloud. Cloud infrastructure backup is performed by cloud administrators for cloud lifecycle management. As one example, assume a system administrator of Company A, which offers cloud services, performs regular backups of different cloud management components to a backup server that is isolated from a given cloud. Also assume a cloud architecture of Company A includes: shared storage, e.g., because all physical machines (PM) do not have a local disk; and many internal cloud components are unreachable from external Internet services due to enterprise security issues. In this case, a cloud infrastructure backup requires temporary ‘staging’ of system data (or cloud management data) to a management node that can connect to a backup host via the Internet. During a ‘staging’ operation, all management tasks have conventionally been halted as all system administrative tasks have been required to quiesce until ‘staging’ is complete. In general, halting management tasks has conventionally been desirable to prevent data inconsistency during backup.

However, as Company A employs a shared storage model, many workloads on the same cloud may use the same storage resources as the backup process. In general, a staging delay during cloud infrastructure backup may increase significantly when customer input/output (I/O) intensive workloads are executing in parallel with the cloud infrastructure backup process. The resource contention between the cloud infrastructure backup process (initiated by a cloud provider/administrator) and executing workloads (initiated by cloud customers) may negatively affect the performance of the cloud infrastructure backup process by increasing the staging delay and stopping all regular administrative activities and monitoring services and may also negatively affect the performance of executing workloads and thus lead to QoS compliance issues.

According to one aspect of the present disclosure, interference avoidance may be employed to prevent interference between a cloud infrastructure backup process and executing workloads, e.g., VM workloads. For example, instead of employing a conventional fixed-window based cloud infrastructure backup process, a dynamic-window based cloud infrastructure backup process may be implemented that predicts I/O workload peaks, according to an embodiment of the present disclosure, to determine an optimal schedule for executing a cloud infrastructure backup process.

According to another aspect of the present disclosure, interference tolerance may be employed to reduce interference between a cloud infrastructure backup process and executing workloads when interference avoidance is not possible. For example, a storage medium differentiation approach based on I/O latency (a solid-state device (SSD) versus a hard-disk drive (HDD), an HDD with higher revolutions per minute (RPM) versus an HDD with a lower RPM, etc.) may be employed to reduce interference between a cloud infrastructure backup process and executing workloads. According to one embodiment, an interference policy that utilizes cost as a constraint may be employed. For example, an interference policy based approach may utilize a cache in a storage controller to temporarily hold data to reduce cost associated with employing multiple storage resources. In general, the disclosed techniques can speed-up system backup (i.e., cloud infrastructure backup) for many cloud products, e.g., IBM® PureApplication Systems. The disclosed techniques may also increase the performance of high-priority workloads that run concurrently with a cloud infrastructure backup on to shared storage.

FIG. 4depicts an exemplary cloud environment400where cloud management nodes402are directly connected to a backup server460, but internal components404,406, and408(e.g., databases executing on management nodes402or applications executing on management nodes402) are not directly connected to backup server460. InFIG. 4, it should be appreciated that workload virtual machines (VMs)410(executing on physical machines (PMs)420,430, and440) and management nodes402utilize shared storage450, which may, for example, correspond to a storage area network (SAN). As backup is an inherently input/output (I/O) intensive process, backup can result in a significant increase in I/O operations (both in terms of I/O operations per second (IOPS) and megabytes per second (MB/s) of data transferred) on shared storage450. In general, a backup process must scan all volumes in a backup set to determine what files have changed to create a delta disk since a last backup point. Data may also be compressed and/or encrypted before transfer to an off-cloud backup storage repository which may further increase I/O requirements.

According to one or more embodiments of the present disclosure, to mitigate I/O interference between cloud infrastructure backup and executing workloads, a two-step approach may be employed. According to at least one embodiment, for interference avoidance an attempt is made to perform system backup during a time window when no I/O intensive workloads are executing and for interference tolerance (if no such time window is available) a policy based approach is utilized that efficiently executes both the system backup and workloads concurrently. In general, the interference avoidance approach may employ dynamic backup schedule generation by cloud control software (e.g., executing on management node402). For example, a cloud administrator may request that a system backup occur on or around a specific time of day. In this case, cloud control software determines the specific time that is optimal for the system to reduce I/O interference between management and user functions.

With reference toFIG. 5, a relevant portion of shared storage450is illustrated with ‘M’ types of data (with ‘K’ priority levels) being directed towards ‘N’ types of storage resources504,506,508, and510(one or more SSDs, one or more HDDs, HDDs with different RPM, RAM disk, etc.) by storage controller502. In this example, the types of data include workload (WL) data for workloads 1, 2 . . . , M and backup data (BD) of a cloud infrastructure backup process. InFIG. 5, a policy based approach is employed to determine how to route data operations to a class of storage medium. For example, storage controller502may use a policy to direct higher priority data towards a faster storage resource. As one example, in the case where only one type of workload data is executed concurrently with backing up system data and there is one SSD node and one HDD node in an associated storage pool, the backup data is assigned a higher priority than the workload data. In this case, storage controller502sends the backup data to the SSD (e.g., type 1 storage504) and the workload data to the HDD (e.g., type N storage510), as the SSD is faster than the HDD and the backup data has higher priority than the workload data. In one embodiment, if the workloads are already deployed and residing on the SSD, the workloads are live-migrated to the HDD. In the above example, it is assumed that the default SSD capacity on the cloud is sufficient to hold the system (infrastructure/management) data. In this case, when workloads share the SSD (where the backup data is supposed to be written) and backup has priority over workload, workloads are evicted via live migrations.

When cost is a constraint, maintaining ‘N’ different types of storage resources may not be feasible. In this case, low priority data may be temporarily held in a cache within storage controller502. With reference toFIG. 6, storage controller502is further illustrated as including cache602, which is utilized to temporarily store workload (WL) data prior to sending the cached workload data to storage resources according to an embodiment of the present disclosure. As is illustrated inFIG. 6, while storage controller502caches workload data (WL), backup data (BD) is routed directed to a storage resource. In the example ofFIG. 6, ‘M’ and ‘K’ are equal to two and ‘N’ is equal to one.

According to another embodiment, additional tagging (e.g., by cloud control software) can be used to denote when to cache workload data (WL) and when to flush workload data. As is illustrated, when tag ‘C’ is associated with workload data (WL) storage controller502caches the first two blocks of workload data received, and when tag ‘F’ is associated with workload data (WL) storage controller502flushes the three blocks of workload data in cache602to a storage resource. It should be appreciated that when backup data is large in size, cache602of storage controller502may become full with workload data. In this case, storage controller502flushes the workload data from cache602and holds the backup data (in a portion of cache602or another memory) while flushing the workload data from cache602. It should be appreciated that the flush duration is generally small compared to the overall backup process and, in this case, backup performance is minimally impacted.

With reference toFIG. 7a process700for cloud infrastructure backup in a virtual environment utilizing shared storage, according to one aspect of the present disclosure, is illustrated. When cloud infrastructure backup and workloads must execute at the same time (i.e., interference avoidance is not possible), process700employs an interference tolerance approach for staging. According to one aspect, the interference tolerance approach utilizes a storage medium performance differentiation mechanism by leveraging different types of storage resources (e.g., RAM disk, SSD, or HDDs with different RPMs) in a common pool. As mentioned above, there can be ‘N’ different types of storage resources, and cloud control software may tag data and set a priority level for each type of data (e.g., there may be ‘K’ levels of priority). Process700may be implemented, for example, through the execution of one or more program modules42(seeFIG. 1) of cloud control software residing in management layer64(seeFIG. 3) by processor16(of computer system12of management node402).

Process700may, for example, be initiated in block702in response to a user or cloud control software requesting a cloud infrastructure backup in a shared storage environment. Next, in block704, processor16analyzes an I/O profile for storage controller502that was obtained over a given time period (e.g., days, weeks, or months). Then, in block706, processor16identifies one or more time windows for which I/O workloads are relatively low. Next, in decision block708, processor16determines whether a cloud infrastructure backup can be performed in any of the time windows identified (i.e., whether backup staging fits in one of the identified windows).

In response to backup staging fitting in one or more of the identified windows, control transfers from block708to block712. In block712, processor16schedules the cloud infrastructure backup to execute during a selected one of the time windows. For example, if multiple time windows are available that meet time requirements for staging the cloud infrastructure backup, the time window with the largest time may be selected. As another example, if multiple time windows are available that meet time requirements for staging the cloud infrastructure backup, the time window with the smallest time may be selected. Following scheduling staging of the cloud infrastructure backup in a time window, control transfers from block712to block714, where process700terminates until a next cloud infrastructure backup is indicated.

In response to backup staging not fitting in one or more of the time windows in block708, control transfers to block710. In block710, processor16employs an interference tolerance approach to concurrently schedule access to shared storage450for cloud infrastructure backup data with workload data. It should be appreciated that the cloud infrastructure backup data is staged for later transfer (by management node402) to backup server460. For example, processor16may employ a storage medium differentiation approach based on I/O latency (an SSD versus an HDD, an HDD with a higher RPM versus an HDD with a lower RPM, random access memory (RAM) disk, tape, etc.) to reduce interference between staging a cloud infrastructure backup and executing workloads that utilize shared storage450. As one example, an interference policy that utilizes cost as a constraint may be employed. Further, an interference policy based approach may utilize cache602in storage controller502(seeFIG. 6) to temporarily hold workload data for a single storage resource. Following block710control transfers to block714, where process700terminates until staging of a next cloud infrastructure backup is indicated.

Accordingly, techniques have been disclosed herein that may advantageously increase the performance of workloads that run concurrently with staging a cloud infrastructure backup in a shared storage environment.