89 Fifth Avenue, 7th Floor. New York, NY White Paper. HP 3PAR Priority Optimization: A Competitive Comparison

Transcription

1 89 Fifth Avenue, 7th Floor New York, NY White Paper HP 3PAR Priority Optimization: A Competitive Comparison

2 Printed in the United States of America Copyright 2014 Edison Group, Inc. New York. Edison Group offers no warranty either expressed or implied on the information contained herein and shall be held harmless for errors resulting from its use. All products are trademarks of their respective owners. First Publication: February, 2014 Produced by: Chris M. Evans, Senior Analyst; Manny Frishberg, Editor; Barry Cohen, Editor-in- Chief

3 Table of Contents Executive Summary... 1 Introduction... 2 Objective... 2 Audience... 2 Contents of this Report... 2 The Need for Quality of Service (QoS)... 3 Noisy Neighbor... 3 Best Effort... 4 Quality of Service Requirements... 4 HP 3PAR Priority Optimization: Deep Dive... 6 Background... 6 Priority Optimization Implementation... 6 Workload Thresholds... 6 Workload Prioritization... 7 Use Cases... 8 Competitive Analysis... 9 EMC Enterprise VMAX... 9 Symmetrix Priority Controls... 9 Dynamic Cache Partitioning... 9 EMC VNX Unisphere QoS Manager (UQM) NetApp FlexShare Storage QoS Conclusions and Recommendations... 13

4 Executive Summary Quality of Service (QoS) is an essential component in delivering modern, highly scalable, multi-tenant storage architectures. The use of QoS moves advanced storage systems away from the legacy approach of delivering I/O requests with best effort in mind. It tackles the problems of the noisy neighbor, delivering predictable tiered service levels and managing burst I/O, irrespective of other users of a shared system. Mature QoS solutions meet the requirements of controlling service metrics such as throughput, bandwidth, and latency without requiring the system administrator having to manually balance physical resources. These solutions, therefore, offer responsiveness, and flexibility, while being highly scalable and application focused. HP 3PAR StoreServ Priority Optimization, the Quality of Service feature of the HP 3PAR StoreServ family, delivers on all of the requirements described above, with a consistent approach and results, when deployed with hard disk drives, solid-state drives, or a combination of both media. For this study, the HP 3PAR StoreServ solution was compared to competing offerings from EMC and NetApp. EMC s enterprise VMAX implements QoS using two features; Symmetrix Priority Controls and Dynamic Cache Partitioning. Neither product delivered true QoS features, but rather focused on prioritizing workload without constraining it. EMC s midrange offering, Unisphere QoS Manager, was more closely aligned to the requirements of QoS, but failed to scale and had limit applicability. NetApp offers two solutions, depending on the mode used to install Data ONTAP. NetApp FlexShare is available for 7-mode users, and implements a simple prioritization algorithm. It fails to scale, requiring individual settings for each volume. NetApp Storage QoS, the offering for cluster-mode, provided service-based metrics for throughput and bandwidth management, but offered no features to manage busy systems, and had challenges with management overhead. In summary, HP 3PAR StoreServ Priority Optimization provided the most mature and flexible QoS management solution of the vendors in this comparison. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 1

5 Introduction Objective This report looks at the implementation of Quality of Service (QoS) on the HP 3PAR StoreServ storage array and compares the features and functionality offered to equivalent competitive products available in the marketplace today. As IT departments within organizations move toward delivering technology as a service, QoS features become a critical requirement for ensuring the end-user experience is delivered consistently and satisfactorily. Audience Decision makers in organizations, looking to deliver improved and measurable service level differentiation within deployments that use centralized storage, will find this report provides an understanding of the need for and benefits of Quality of Service features. Contents of this Report Executive Summary A summary of the background and conclusions derived from Edison s research and analysis. The Need for Quality of Service A primer on the evolution of shared storage and need to provide service-based I/O in a multi-tenant environment. HP Priority Optimization: Deep Dive An in-depth discussion on the features and functionality of the HP 3PAR StoreServ Quality of Service feature. Competitive Analysis An examination of the implementation of QoS in competitive storage platforms with comparison to HP 3PAR StoreServ. Conclusions and Recommendations A summary of the findings from the research. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 2

6 The Need for Quality of Service (QoS) Storage within the enterprise has gone through a number of transformations since the introduction of hard disk technology in the mid-1950s. As the mainframe declined in the early 1990s and Open Systems computing became more prevalent, storage was deployed in a distributed fashion, with each server assigned its own resources. Of course, this solution did not scale and the late 1990s saw the introduction of storage area networks (SANs) and consolidated storage. Today, the largest enterprise storage arrays can scale to multi-petabytes in size, supporting hundreds of hosts and a wide a range of workloads, including virtual server infrastructure, databases, and unstructured data. Throughout this time, storage arrays have continued to respond to I/O requests in the same way, processing requests in order and delivering the results of I/O operations as fast as possible on a best effort basis. The move to service-based delivery of technology, in the form of cloud computing, has seen a transformation in the way in which IT resources are provided to businesses in both private and public cloud implementations. Best effort is no longer good enough and computing resources need to be delivered to meet service-level objectives within service-level agreements. At scale, this new model of working becomes difficult for traditional storage to achieve without risking breaching service levels. This is the point at which QoS becomes a required feature. Noisy Neighbor Today s enterprise storage environments are, by their nature, multi-tenant, delivering storage I/O to multiple different and potentially disparate workloads. This is especially true in cloud deployments, where each tenant may be from a different line of business or customer to a service provider. The term noisy neighbor is used to refer to the situation where one host server creates a sufficient workload to impact the performance and response time of other servers sharing the same physical infrastructure. The rogue server delivers a high number of I/O requests, unfairly impacting those around it. Without adequate controls, noisy neighbors create unpredictable application performance, which can translate directly into lost business for a service provider. Resolving the noisy neighbor issue protects critical applications and at the same time provides consistent and predictable service levels for all applications. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 3

7 Best Effort Until recently storage arrays used hard disk drives as their permanent storage media. Compared to the rest of the components that make up an array, hard drives are orders of magnitude slower than processors, memory and internal pathways such as PCI Express. As a result, devices were designed to deliver I/O as fast as possible, with no delay in the servicing of an I/O request. Cache memory is typically used to confirm I/O completion in write operations before the change has been committed to disk, simply to mask the relatively slow performance of the hard drive. Delivering I/O as fast as possible has a number of consequences. First, response times are unpredictable, as they are based on a dependency of spinning media and the workload mix from many different types of I/O operation, including random and sequential requests or perhaps heavy read or write requests. This means that, as an array is loaded with active data, the response time profile will change, typically getting slower as utilization increases. This is not a problem for the newest data added to the array but delivers a markedly different experience for the first users of that device. Quality of Service Requirements The ideal solution for shared storage infrastructure is to deliver I/O requests based on service metrics. Implementing Quality of Service should be detached from the physical aspects of delivering I/O (for example assigning fixed amounts of cache to a volume) as the process of doing this is not measurable in terms of application performance. Implementing QoS means meeting the following requirements: Throughput This is a measure of performance usually expressed in I/O operations per second or IOPS and is an indication of the number of transactions being completed for a host. Higher values are better. Bandwidth This is a measure of the performance of an array in terms of the volume of data transferred per unit of time and is typically measured in MB/s (megabytes per second). Higher values are better. Latency Also known as response time, this is a measure of the time taken to complete an I/O request. This term is also sometimes referred to as the Service Time. Lower values are better with figures usually quoted in milliseconds. Responsiveness Storage systems delivering QoS should be responsive to change in the I/O profile and be able to adapt quickly to maintain QoS levels. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 4

8 Flexibility QoS settings should be dynamically flexible and have the ability to be changed to meet changing application requirements. Once changes are made, systems should be quick to re-establish equilibrium and meet the new service levels. Application Focused QoS settings need to be application focused and be capable of being applied at the application level, rather than on the architecture of the hardware, or in terms of purely LUNs or volumes. Scale Solutions should continue to provide Quality of Service as they scale in capacity (and therefore in complexity) and level of activity (when they become busy). Enable Overprovisioning Storage arrays by their nature are capable of delivering only a finite amount of performance. Systems should be capable of managing the overprovisioning of performance to ensure resources are not wasted, but also managing the proportional delivery of resources as the array becomes busier. All of the above requirements need to be delivered autonomically, that is, without the need for manual intervention. QoS deployments that depend on continual monitoring and amendment by an administrator are not scalable to deliver, as the overhead of management becomes both practically and financially excessive. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 5

9 HP 3PAR Priority Optimization: Deep Dive Background HP 3PAR StoreServ systems can be deployed with hard disk drives (both Fast and Nearline class) and solid state disks (SSDs). Each physical device is divided into 1GB chunklets, which are then recombined into RAID groups to create logical disks. Virtual Volumes (the entity exported to the host) are then created from logical disks, which have been grouped into CPGs or common provisioning groups. The 3PAR architecture means that all disks across all controllers are available for I/O and no physical segmentation of resources exists. This delivers high performance and forms the framework onto which Quality of Service policies can be applied. Priority Optimization Implementation HP 3PAR StoreServ implements Quality of Service on two levels, based on both absolute and relative workload performance. Absolute workload constraints are applied by specifying limits on throughput (IOPS), bandwidth (MB/s) or both values. This feature ensures that no single application can monopolize I/O resources and create the so-called noisy neighbor effect. Eliminating the noisy neighbor effect allows HP 3PAR StoreServ systems to deliver consistent service levels and provide guaranteed protection for critical applications. Relative workload constraints are implemented on HP 3PAR StoreServ systems by specifying priority levels and latency goals to each QoS policy. The prioritization of workload allows 3PAR StoreServ systems to throttle low priority workload as systems become busy, ensuring that latency goals are achieved for higher priority work. The ability to prioritize workload means 3PAR StoreServ systems can continue to deliver service to critical applications as systems become busier over time. Workload Thresholds HP 3PAR StoreServ implements Quality of Service by enforcing thresholds restrictions on throughput and bandwidth. Policies are applied at the VVset level, which groups virtual volumes together to form an application workload. As a VVset may contain a single volume, then the lowest level of granularity on QoS is the virtual volume. Policies can also be defined at the virtual domain level, allowing separate policies to be established for each tenant in a multi-tenant environment. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 6

10 A volume can be defined in up to eight VVsets, all of which may have QoS metrics applied to them. In this instance, the individual rules are merged together using OR logic and the resultant figures used to apply QoS settings for the volume. When an I/O is received by a 3PAR StoreServ array, it performs one of three actions: 1. If QoS thresholds have not been exceeded, the I/O is passed to the virtual volume and processed as normal. 2. If QoS thresholds have been exceeded, the I/O is delayed and added to the Priority Optimization policy queue. 3. If the queue is full, the host is issued the QFULL command to restrict further I/O. Queued I/O requests do not consume system resources such as cache. Priority Optimization allows workload restrictions to be nested. For example, a policy could be applied that restricts a virtual domain to 10,000 IOPS, while each individual volume in the domain has IOPS restrictions that exceed this figure in total. This allows a certain degree of over-provisioning and limits the wasting of resources. When Priority Optimization is enabled, by default all volumes are managed by the all others rule, when no QoS policy has been applied. HP recommends having this rule enabled, as it is disabled by default. Workload Prioritization HP 3PAR StoreServ Priority Optimization protects critical workloads as the overall I/O activity level on a system increases. Each policy can be assigned a priority level (high/normal/low) and latency goal, expressed in milliseconds, which specifies the expected response time to be achieved for the policy group. Each policy also uses a minimum goal, below which a workload will not be throttled. With latency goals in place, as systems become busier and latency targets are breached, the system busy level (a measure of system activity) increases. Using a scaling factor based on I/O block size, the HP 3PAR StoreServ system will throttle low priority I/O as the system busy level rises to 25%. At this point, throttling will start to include medium priority workloads. At 50% system busy, the system will start to include throttling of high priority workloads. To ensure that applications are not completely starved of I/O, all volumes are assigned a minimum threshold, below which their I/O will not be throttled. This ensures that in Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 7

11 periods of high activity even low priority workloads receive some service. The latency goal and minimum goal work in conjunction with each other to ensure the optimum application of resources to each workload. Use Cases The combination of Workload Thresholds and Workload Prioritization features enables HP 3PAR StoreServ systems to manage certain typical use cases found in enterprise environments. Protection of Mission Critical Applications storage arrays have only a finite amount of resources available to serve to hosts. In systems without Quality of Service functions that experience high load, applications will compete against each other, resulting in unpredictable and throughput and response times. HP 3PAR StoreServ systems can use latency goals and priority levels to protect critical applications. In systems with high load, I/O is preserved for those applications with high priority and latency goals applied. Predictable Service Levels consistency of throughput and response time is critical to delivering storage to multi-tenant environments. Multi-tenant configurations can be highly unpredictable in terms of I/O demands and so the ability to consistently deliver performance is a key feature for both public cloud service providers and IT departments. HP 3PAR StoreServ uses a combination of workload thresholds and prioritization to ensure customer requirements can be met at both the tenant and application level. Defined Service Level Agreements once consistent service can be achieved, the next enhancement is to offer multiple service levels to provide service differentiation. This may mean implementing service levels to match workload types, for example delivering lower priority service for test/development applications. It may also mean the ability to charge end users (either in private or public clouds) based on the service level they receive. HP 3PAR StoreServ enables the use of differentiated service levels through priority levels, while maintaining a minimum service level guarantee. Managing Burst I/O in some scenarios, applications require additional performance to manage a temporary peak in workload. Without the ability to manage I/O bursts, application performance can be degraded, or resources have to be allocated to cater for anticipated burst periods. HP 3PAR StoreServ systems allow burst I/O by implementing latency goals on burstable workloads and minimum goals on other applications. This allows burst I/O to occur, scaling back lower priority workload to achieve this, but with controls to ensure those throttled workloads still receive service. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 8

12 Competitive Analysis The implementation of 3PAR StoreServ Quality of Service features has been compared to equivalent competitive products. These include the EMC enterprise VMAX platform, EMC midrange VNX platform, and Data ONTAP platforms from NetApp (both 7-mode and cluster-mode). EMC Enterprise VMAX EMC offers a number of features to manage Quality of Service on their enterprise platforms. Dynamic Cache Partitioning allows cache resources to be divided up and dynamically assigned to volume pools; Symmetrix Priority Controls prioritizes workload for LUNs/volumes. Symmetrix Priority Controls The Symmetrix Priority Controls (SPC) feature provides prioritization of read I/O for logical volumes (LUNs) sharing the same physical disk. SPC enables up to 16 service levels to be created and associated with logical volumes. During busy periods, back-end read I/O is reordered, giving priority to high priority workload and reducing response time for those logical LUNs. SPC delivers best results when physical disks have a mixture of different application workload classes. However if a disk pool contains similar workloads, little benefit will be achieved. SPC is not effective with applications that have different I/O profiles, such as high read or write I/O. EMC does not recommend using SPC on flash drives and the feature is currently not supported with Virtual Provisioning, making it severely limited in application. Dynamic Cache Partitioning Dynamic Cache Partitioning (DCP) allows the creation of up to eight cache partitions, logically and evenly dividing up the physical cache resources of an array. Partitions may be defined as either static or flexible, which determines the way in which cache is assigned to each type. Static partitions have a fixed amount of cache assigned to them, which is specified as a percentage of the overall cache amount in the system. All of the cache resources are permanently dedicated to that partition once it is created. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 9

13 Flexible partitions have a minimum, maximum, and target percentage allocation, which establish lower, upper, and target thresholds on the cache size, respectively. The sum of the target values for each partition should total 100 percent, whether dynamic or static. Cache balancing is enabled by specifying a Donation Time (in seconds) for each flexible partition. Low Donation Times increase the chance of pages being moved to other partitions, while high Donation Times decrease the chance of pages being moved. Host I/O activity is throttled using the Write Pending Limit parameter. This specifies the maximum percentage of cache pages waiting to be written before destage operations are given higher priority and host I/O is throttled. A percentage value between 40 and 80 may be used. It is clear that EMC s implementation of Quality of Service within the Symmetrix/VMAX family is based on improving performance for the system rather than targeting the service level requirements of the application. Both SPC and DCP require the administrator to have knowledge of the workload profile on each volume or LUN, something that is not scalable in practice. Neither solution discusses QoS in terms of the metrics needed to deliver service-based performance, such as latency and throughput. EMC VNX Unisphere QoS Manager (UQM) EMC s midrange VNX platform uses the Unisphere QoS Manager (UQM) to monitor and control application performance on VNX systems. Management can be achieved through the Graphical User Interface or Unisphere Command Line Interface. UQM uses the concept of I/O classes to group similar application workloads, based on either related LUNs (which could compose an application), or non-lun specific groupings that look at I/O type (for instance read/write) and I/O block size. I/O classes are then combined into policies that can be used to monitor and/or enforce Quality of Service. Policy enforcement is achieved using Goals. A goal can use one of three methods to enforce QoS: Cruise Control, Limit, or Fixed Queue Depth. Cruise Control specifies a target bandwidth (MB/s) or throughput (IOPS) to be achieved for an I/O class. The VNX workload is dynamically adjusted to meet the Cruise Control goal. This can take up to several minutes to be achieved. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 10

14 Limit specifies a limit of throughput or bandwidth to be achieved for an I/O class; Fixed Queue Depth specifies a hard-coded limit on the I/O for a workload. It is not recommended for normal usage. UQM can take some time to establish an I/O workload goal, which can result in unexpected peaks in performance. In addition, Cruise Control is only recommended for implementation on a maximum of two I/O classes at any one time. I/O classes themselves must not be ambiguous and cannot have overlapping LUN definitions within the same policy. I/O classes are limited to contain only 64 LUNs. NetApp NetApp has two implementations of QoS features within their Data ONTAP storage platforms, depending on the mode of installation. For 7-mode deployments, QoS features are implemented through FlexShare. For clustered mode, NetApp implements a feature called Storage QoS. FlexShare FlexShare is NetApp s implementation of Quality of Service functionality for 7-mode systems. This is the older non-clustered version of Data ONTAP, which NetApp maintains in parallel to their newer cluster-mode software. FlexShare is not a true QoS implementation, but rather simply a prioritization mechanism that biases I/O with higher priority. FlexShare uses five static prioritization levels named VeryHigh, High, Medium, Low, VeryLow, and a prioritization numbering system from eight to 92, where eight corresponds to VeryLow and 92 equals VeryHigh. With FlexShare enabled, volumes are assigned a default level of Medium, unless the default setting has been amended (NetApp does not recommend setting the default to VeryHigh or VeryLow as this can have unintended consequences). FlexShare settings are made at the volume level and have to be set for each volume independently. FlexShare allows two other settings to be made for each volume. The management of cache can be influenced by specifying one of three values for the cache parameter; keep, reuse, and default. The keep option requests that cache pages are kept as long as possible for reuse, which is favorable for volumes with lots of read activity. The reuse value recommends releasing and reusing cache pages as soon as possible good for volumes with low read activity. The third option is default, which applies normal cache management behavior. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 11

15 The system parameter allows FlexShare to prioritize either system or user activity against a volume. This is expressed as a percentage of system workload. FlexShare is not autonomic and requires a large amount of manual intervention to implement and maintain. It is simply a prioritization method and does not prevent the noisy neighbor issue, as it does not throttle workload in any way. Storage QoS Storage QoS was introduced into Data ONTAP cluster-mode in release 8.2 (the feature is not available to 7-mode customers). It implements Quality of Service features by limiting I/O workload based on throughput (IOPS) or bandwidth (MBs). Settings are implemented through policies and can be applied to Storage Virtual Machines (SVMs), volumes, LUNs or individual files. However QoS settings cannot be nested; therefore if a policy has been assigned at the SVM level, it applies to all objects within that SVM. This means the administrator has to either set policies on volumes individually, or use SVMs to group volumes together. There are some limits applied to Storage QoS. It is restricted to clusters of up to eight nodes in size, and only 3,500 policy groups per cluster are permitted. Only 10,000 objects (SVMs, volumes, LUNs, files) can be assigned a policy. Note that a cluster can scale up to 49,000 LUNs or 12,000 NAS volumes, so some compromise in the use of QoS may be required. Storage QoS is not supported on certain NetApp volume types including infinite volumes, data protection mirrors, load sharing mirrors, node root volumes, and FlexCache volumes. Storage QoS is implemented at the protocol layer and does not impact system resources. It is possible for a group of resources to exceed their policy restrictions if the workload is particularly bursty. Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 12

16 Conclusions and Recommendations HP 3PAR StoreServ Priority Optimization implements Quality of Service features across the entire range of products with the same features and functionality. This includes systems using a mix of hard disk drives and solid state drives, as well as the all-flash 7450 system. In contrast, both EMC and NetApp have multiple implementations of QoS by platform, with no similarity in the features offered. HP 3PAR StoreServ Priority Optimization uses service-based metrics based on throughput, bandwidth, and latency to achieve balanced and efficient performance for all applications, while using all system resources. In contrast, the implementation of EMC s Symmetrix Priority Controls and NetApp s FlexShare are only focused on workload prioritization, and do not limit I/O. Neither solution resolves the noisy neighbor problem or provides for consistent I/O service levels. NetApp Storage QoS and EMC Unisphere QoS both use service-based metrics to contain workloads; however both are restricted in their deployment and scalability. In contrast, HP 3PAR Priority Optimization provides autonomic workload prioritization, restriction, and balancing and is the only solution to address the scenario of managing Quality of Service on highly utilized systems. Only HP 3PAR StoreServ systems address the requirement to cater for workloads needing burst I/O, which offers service providers the ability to deliver differentiated (and chargeable) services. In summary, HP 3PAR StoreServ Priority Optimization offers the most mature and complete implementation of Quality of Service based on the comparisons made in this report. The technology has been implemented to deliver service-based functionality and to implement QoS as an application-level rather than system-level feature. 4AA5-1671ENW Edison: HP 3PAR Priority Optimization: A Competitive Comparison Page 13