NAVIGATING THE HYBRID INFRASTRUCTURE MANAGEMENT PERFORMANCE CHALLENGE.

Transcription

1 NAVIGATING THE HYBRID INFRASTRUCTURE MANAGEMENT PERFORMANCE CHALLENGE. Written by Chris Mellor. This whitepaper is sponsored by:

2 CONTENTS 1 2 // INTRODUCTION // A CHANGING WORLD 3 5 // CHANGING APPLICATIONS // DATA, VISUALISATION AND AI 6 // VIRTUAL INSTRUMENTS 11 // CONCLUSION

3 1 // INTRODUCTION INTRODUCTION Business is changing and so, too, is the technology driving it. Disruption and digitalisation are seeing organisations deliver more services as software and that s shone the spotlight on the infrastructure supporting that software. As part of a company s digital transformation initiatives, today s infrastructures are increasingly cloud, hybrid and softwaredefined. They are complex, heterogeneous, interconnected, distributed, and operate at tremendous scale. Applications - and thus the business - are expected to operate efficiently and optimally, delivering the service levels required with a minimum of wasted resources. Infrastructure Performance Management (IPM), and the visibility it provides, is the answer to the question of how to achieve this. Successful IPM monitors application response times, detects the state of the IT infrastructure and the inter-related events that take place within it. It understands how these events influence application performance, detects performance anomalies, diagnoses the underlying causes, helps solve the problems and so brings application performance to its desired state. It works in a challenging and ever-changing environment, the like of which earlier generations of infrastructure management software systems have never seen.

4 2 // A CHANGING WORLD Today s infrastructure is like nothing in history. Yes, data centres exist, but they are either on the customer s own premises, in an outsourced hosting provider, or that of a public cloud provider such as Amazon or Microsoft. Often, they are combined to produce a hybrid infrastructure. Yes, within a data centre we find a highly complex set of system components such as servers, networks, storage, data protection and secondary data management. However, today s servers can be a mix of industry standard X86 or proprietary systems, run multiple cores and threads, a mix of hypervisors and operating systems, run VMware vsphere, KVM, or Microsoft Hyper-V, and containerisation software like Docker. Yes, applications can be end-user facing, system level or behind online transactions but today they could be running on a single server, one or more VMs in one or more servers, distributed across a cluster, set of microservices or containerised. Networks are highly organised and hierarchical with, also, a hierarchy of switches and routers and telecommunications systems from a variety of suppliers. Application data lives in external shared arrays and processed in block (SAN), file (NAS) or object form stored using NAND, SSD or hard disk drives and accessed by SATA, SAS or NVMe protocols. The external storage may be accessed by Ethernet (iscsi), Fibre Channel, InfiniBand, or the faster and developing NVMe-over-Fabrics method. Adding further complication storage may be local or implemented as a virtual SAN by combining storage in a group of servers into a single resource pool, or a virtual or VSAN, as is the case with hyper-converged infrastructure. On top of this, these elements each have their own proprietary management and diagnostic systems, tools and interfaces. And none of these silo-specific tools talk to or understand each other. What does this mean? An IT infrastructure that can have thousands, even tens of thousands, of hardware and software components, each of which needs to interoperate in a dynamic, organised and integrated way with the other components. It is one of the most complex operational environments to manage ever invented, possibly the most complex of all.

5 3 // CHANGING APPLICATIONS The applications running on top of this infrastructure are becoming more dispersed and built from more components. As enterprises become digitally-focused, application performance has become critical making application-aware infrastructure performance monitoring more important than ever. As is common, applications are grouped into the following tiers, with those further down the list receiving lower resource prioritisation: 01 Tier 1 BUSINESS-CRITICAL APPS 02 Tier 2 BUSINESS-ACTIVITY SUPPORT APPS 03 Tier 3 BACKGROUND-ACTIVITY APPS But with thousands of application-level software services, tens of thousands of underlying infrastructure hardware and software components and billions of separate events taking place hourly - trillions if we include packets flowing across network components - performance can easily become an issue.

6 4 // CHANGING APPLICATIONS Performance management segments Most IPM, NPM and APM vendors have extended their feature sets to include AIOps capabilities. Virtual Instruments is a leader in both IPM and AIOps. Graphic by Virtual Instruments When problems happen, it becomes crucial to quickly find and understand the root cause of performance issues and how they may impact the various applications. Root cause determination in this massive environment is truly finding the needle in the proverbial haystack. Microservices are exacerbating this issue. Monolithic applications are being broken down into hundreds, if not thousands, of smaller and interconnected services. This, therefore, means a vastly increased amount of potential performance issues - and accompanying data to observe. That s in addition to the standard infrastructure component data sources that include wire-level, machine and software instrumentation from hardware and software systems, devices and so on. Among these are log files - recorded and typically time-stamped events. These are useful because they provide the basis of an audit trail to help track activity and diagnose problems and for mapping applications and their services to the underlying and shared Infrastructure. Application transactions and I/O requests can be tracked and timed as they flow across the infrastructure. Adding to the challenge is the fact that systems should not only be able to observe, monitor and help manage the performance of applications but also be aware of the relative importance of the different applications to the business. They should also be able to react appropriately if low-value applications over use infrastructure components to the detriment of higher-value applications.

7 5 // DATA, VISUALISATION AND AI The resulting performance-problem universe is vast. Without infrastructure monitoring, and the necessary integration with IPM, ordinary application performance management (APM) tools have a blind spot when it comes to looking deeper into your infrastructure. This deeper look is important when it comes to identifying component and resource contention issues that lie behind application-level problems. Such tools function best when looking across the application layer, not down into the layers. Analytics, and associated dashboards, provide a window into this universe of thousands of metrics and billions of events. Data can be fed into machinelearning based analytics tools that provide context and details that let you understand what s happening and why. To be of worth, though, this data must be served in real time and be presented in an organised hierarchically with progressively more detailed drill-down views. It must provide an application-centric viewpoint, showing top-level applications and their components and dependencies. It needs to be able to reveal an application s use of infrastructure elements and identify infrastructure components that participate in an application s services. The dashboard should help set parameters, such as network queue levels, by analysing historical data for host IO, modelling a range of queue depth settings versus performance, and recommending optimal settings to maximise performance. Data visualisation, however, is not our only guide through this expanded universe. Allied with machine learning, data can deliver a form of automated real-time monitoring and management that works on a scale suited to today s most demanding and complex environments. Machine learning works by feeding data into machine models for pattern analysis - a process that allows the model to learn to anticipate and identify new, future events. For our purposes, that data could be a diet of infrastructure events such as end-us er transaction times, web site dwell times, event logs and network packet flow data.

8 6 // VIRTUAL INSTRUMENTS Into this space comes Virtual Instruments, an infrastructure performance monitoring and management supplier that started at infrastructure ground level - including from wire-level with cable taps, and end-to-end storage I/O access performance. It has extended its capabilities up the stack to go deeper into servers - physical, virtual and containerised - system software, networks, and applications, both on-premises and in hybrid deployments with public and private clouds. This has been achieved through integrations with third-party suppliers in the applications and infrastructure device areas. Its offerings include VirtualWisdom and WorkloadWisdom. VirtualWisdom is an application-centric IPM platform with four main capabilities / use cases: Application Service Assurance Predictive Capacity Management Workload Infrastructure Balancing Problem Resolution and Avoidance WorkloadWisdom is a storage performance validation and testing platform. VirtualWisdom application-level integrations Understanding where applications live on the infrastructure, their relative business value, and how they are stressing the infrastructure is essential to performance assurance. VirtualWisdom gains insight into a customer s application estate and services through third-party integrations, such as:

9 7 // VIRTUAL INSTRUMENTS ServiceNow CMDB and service mapping integration with VirtualWisdom, including ITSM ticketing integration NetFlow analysis providing bandwidth utilisation and all client to server communications Mapping of routers and vsphere distributed switches (vds) by VirtualWisdom ProbeNetflow captures flow records from NetFlow, sflow, Jflow, and IPFIX and sends them to the VirtualWisdom appliance Host integration via SSH or WMI Application discovery and mapping and context with AppDynamics integration Application discovery and mapping with Dynatrace integration These capabilities provide automated application and application service discovery, and they drive application topology mapping and application service-infrastructure element linking by VirtualWisdom. Integration with AppDynamics is being extended with support for AppD Alerts enabling VirtualWisdom to correlate end-user or business user transaction response times with potential performance issues in the infrastructure. This will be a powerful capability. VirtualWisdom s workload and performance analytics enable infrastructure component use to be balanced, avoiding resource contention, between Tier 1, 2 and 3 applications within a data centre and, where appropriate, public clouds. This can be particularly helpful deep in the infrastructure at the networked storage layer where VIrtualWisdom can identify which applications from which tier are accessing storage resources and pinpoint contentions affecting Tier 1 application performance. VirtualWisdom server integration Host operating system monitoring capabilities in VirtualWisdom enable the monitoring of Linux or Windows operating systems running in the cloud (AWS/Azure), virtual servers, hyper-converged infrastructure (HCI) and physical servers. VirtualWisdom supports VMware ESX, Microsoft Hyper-V and IBM PowerVM hypervisors. It supports monitoring of Linux/Windows running on KVM

10 8 // VIRTUAL INSTRUMENTS with VirtualWisdom 6.0 and will have native KVM hypervisor integration in early VirtualWisdom storage and networking device integrations VirtualWisdom has direct instrumentation integrations with a set of networking and storage devices at wire, machine and software levels: NetApp and NTAP Storage Probes - ProbeNTAP is an API-based software integration which collects and correlates health and utilisation data from ONTAP 8 and 9 clustered systems Dell EMC VMAX/PowerMax and IBM SVC storage probes are API-based software integrations that monitor the health, performance, and capacity of the arrays ProbeSDS for Dell EMC s ScaleIO and for VMware s VSAN hyper-converged and converged environments Virtual Server Probe for VMware ESXi, HyperV and IBM PowerVM ProbeNetFlow for Ethernet switches ProbeSW for FC switches SAN Performance Probe family for wire data level real-time analysis of 8 & 16Gbps Fibre Channel and 10Gb iscsi environments NAS Performance Probe family for wire data level real-time analysis of 10Gbps networked storage devices supporting NFSv3 and SMB v2/v3 Cisco MDS Virtual Instruments captures information from Cisco networking devices using Cisco s NetFlow network protocol and its SAN Telemetry Streaming with no TAPs or hardware probes required These probes are passive and agentless so as not to use the resources they are monitoring. VirtualWisdom supports all Fibre Channel, iscsi and NFSv3/SMB arrays of all generations, past and present, that adhere to the ANSI specifications. It supports all host Fibre Channel HBAs, independent of vendor. NVMe over Fabrics is on the roadmap for later in Virtual Instruments VirtualWisdom will provide support for containers natively. The initial focus is Docker and VMware Integrated containers, which will be delivered in 2019, building on the Docker monitoring capabilities acquired as part of Xangati in late 2016.

11 9 // VIRTUAL INSTRUMENTS VirtualWisdom analytics VirtualWisdom s machine learning-based analytics routines analyse wire, machine and software-level data across silos and inside application contexts to help resource use optimisation, problem detection and resolution. There are three sets of analytics routine: Problem resolution and avoidance Event Advisor identifies deviations from typical workload behaviour, with a prioritised list of potentially interesting events that require attention - ranked by magnitude and duration Trend Matcher looks for causality between trends, accepting a base trend for a target metric, comparing it with every device in the database over a specified timeframe then providing a list of matching devices sorted by correlation Queue Solver looks at the effect of having different I/O queue depths for a host s HBA card(s) and recommends a depth throttle setting to optimise the read or write response time of that host Workload infrastructure balancing Balance Finder for verifying workload balancing across hosts by verifying correct multi-pathing software operation across host ports VM Coordinator aligns virtual workloads to physical servers to reduce contention for CPU or memory resources VM Deployment Advisor identifies the optimal cluster and host on which to deploy a VM, based on available capacity and expected VM workload across CPU, memory, I/O, and network Storage Port Balancer identifies overloaded storage ports and makes HBA/ Host move suggestions that would rebalance the front-end ports of a Fibre Channel storage array

12 10 // VIRTUAL INSTRUMENTS Predictive capacity management Workload Analysis collects workload profile data to build an optimised storage infrastructure, enabling a user to visualise and understand workload behaviour in terms of IOPS, latency and throughput, across time and locality Capacity Forecasting monitors for capacity utilization and trends, enabling capacity forecasting for compute, network and storage and offering time to zero capacity alerts Application service assurance Seasonal Trend, learning from seasonal business activity patterns, provides resource needs prediction by hour, day, month, quarter or year, running against any and all metrics in the VirtualWisdom platform, such as I/O/sec, MB/sec, CPU utilisation for ESX and so on VirtualWisdom also offers deep machine-learning capabilities, heuristics and other algorithmic IT capabilities. This is an area of intense focus and development

13 11 // CONCLUSION CONCLUSION Infrastructure is evolving like never before: it s heterogeneous, interconnected and distributed. It s hybrid and software defined and it runs on-prem and across clouds. The growing importance of software running across this massive infrastructure mandates the need for applications and operations to be monitored and managed like never before to achieve efficiency. Such a process cannot rely on silo-centric infrastructure monitoring alone. While application-level monitoring (APM tools) are capable of revealing shortfalls in performance and the potential costs this entails, it cannot relate these to underlying root causes in the infrastructure. Nor can it optimise the infrastructure, across multiple hybrid cloud environments, or ensure its general health. Integration between application (APM) and infrastructure-level (IPM) management and monitoring is essential, and that is what Virtual Instruments delivers. It is our belief that, overall, and in this area, no other supplier provides facilities that are as broad and deep as those from Virtual Instruments.

14 12 This whitepaper is sponsored by: