Our Approach
Xangati is the recognized leader in performance management solutions for cloud/virtualization and automatically tracks down intermittent contention storms that adversely impact the performance of the applications running in this dynamic, shared infrastructure. Customers who deploy Xangati dramatically improve the performance assurance of their cloud environment through a software suite that continuously tracks cross-silo performance health to-the second.
“Legacy management vendors are unlikely to be able to acquire themselves into this market because their core platforms and business models do not work with the customers who are running virtualized environments and buying management solutions.”
- Bernd Harzog, The Virtualization Practice
Overview
Xangati solutions help drive cloud computing and virtualization initiatives, give confidence to migrate line of business (LOB) applications and extend virtual desktop infrastructure (VDI) initiatives. With the performance and capacity management clarity provided through our Xangati Management Dashboard (XMD) suite of software, administrators can take the next step in data center virtualization and into the cloud. . They can do so confidently, knowing that our state-of-the-art performance management and troubleshooting capabilities allow them to quickly identify intermittent contention storms adversely impacting the performance of their environment.
Successful cloud and virtualization initiatives mean that businesses can fully capitalize on their technology investment – which often leads to competitive advantage. The key to successful evolution of a data center strategy is actionable performance management insights. However, not all performance management solutions are equally effective.
Cloud and virtualization actually means complex and intertwined infrastructure. To implement it in the right way, you must first recognize the new performance management requirements that come with cloud and virtualization which are as follows:
- cross-silo
- live and continuous
- scalable
- focused on interactions
It is critical that the solution with the right approach – designed specifically to meet these requirements – be used. Xangati has that solution, and I’d like to take you under the hood of the Xangati solution to explain why, by providing a keener understanding of The Xangati Difference.
Cross-Silo
With little or no separation between silos within a virtualized infrastructure, the days of distinct, individually managed silos are becoming a relic of the past. In a virtualized environment, the center of gravity is not found in just one specific silo. Rather, there is continuous and dynamic interplay occurring within and across multiple silos. To adequately address this dynamism across silos, performance management solutions must be comprehensively cross-silo.
Xangati’s performance management solution is carefully designed for cloud-scale and was built as a truly extensible cross-silo solution. With Xangati, you can see why storage backups are taking longer than expected and impacting VDI end user performance; and you can see why memory is highly utilized and impacting your recently migrated LOB applications – by easily peering across silos. By viewing and correlating this cross-silo information, you have concrete and actionable context for problem solving.
Xangati achieves this through its support of disparate feeds from all silos – network, storage, servers, VMs, applications, etc. Some feeds such as NetFlow may be voluminous and continuously pushed while other feeds such as desktop stats may only be amenable to being pulled periodically. Whether they are pushed continuously or pulled discretely, Xangati can collect and collate disparate feeds from:
- the network – in the form of netflow, SNMP, DNS, AAA/Radius, LDAP;
- the hypervisor – in the form of virtual host, VM, network performance data;
- storage – in the form of storage target performance data; and
- applications – such as performance data from VDI desktops and clients, as well as their managers.
The ability to do this is significant – and essential in order to be cross-silo and extensible.
Live and Continuous
In a virtual environment, understanding what is happening at any precise moment – live and continuous – is critical if we are to uncover and fully understand the causality of the behavior and interactions between objects. Reports that are averaged over minutes or hours or a day do not provide the second-by-second insight that is vital. The importance is in the second-by-second transients. You need to see dynamic shifts as they occur and how they change over time to solve many problems.
The challenge is that the amount of data in a virtualized infrastructure is voluminous, and the amount of time you have to process that information is miniscule – no more than a few microseconds for each interaction. If we need to scale to a million objects in a virtualized environment – and we need to render that information on a per-second basis, we must go to a single microsecond time span to crunch the data.
Xangati’s engine is based in memory – allowing you to view and know about the behavior and interaction of any multitude of objects – and what they’re doing – down to the second. A database approach simply cannot handle this kind of time scale. The ability to process all of this data in memory is truly unique to Xangati and it’s the only way to crunch data to scale that is live.
The Xangati Difference means you have unparalleled second-by-second insights coupled with automated alerts – giving a high-definition video of everything that’s occurring versus a Polaroid snapshot.
Scalable
Cloud and virtual environments grow like there’s no tomorrow, with objects and interactions scaling to 10s and 100s of thousands! The requirement to be live and continuous is closely tied to the requirement to be highly scalable. You not only need to know what’s happening second-by-second but you also need to be able to do so for a very large number of objects.
Xangati’s in-memory architecture allows administrators to see dynamic interactions scaling to 100s of thousands. Also contributing to this scale is Xangati’s agent free model. While other performance management tools frequently rely on a multitude of agents to get the data they want, it is not an approach that is scalable. Additionally, agents are a source of resistance within IT organizations due to their deployment and management complexity. Without agents, Xangati can more easily cover all of the dynamic interactions across all silos that can impact VI or VDI performance.
With The Xangati Difference – in just one second – you have insight into more than a million metrics and interactions! Even at intervals of five to 15 minutes, other solutions don’t scale close to those numbers.
Focused on Interactions
Effective performance management in a virtualized world requires an understanding of how the actions of one object affect another. If there’s a problem with one object, you need to be able to see which other object interacts with it specifically that could be causing the problem. To accomplish this requires analytics that can detect and show, for example, that a CPU spike relates to a certain interaction between two VMs. The combination of the interactions is what really helps to understand the particular nature of the issue.
Xangati uses dynamic and interactional profiling and analytics to troubleshoot when there are performance issues. This ability to automatically unearth performance problems as – or even before – their adverse effects are felt is really important. It allows administrators to solve and/or contain these problems before their effects ripple through the infrastructure and become harder and harder to deal with and contain – both technically and otherwise.
Xangati delivers this troubleshooting capability through the use of interactional metrics. Using these interactional metrics, Xangati can identify the few adverse interactions from the many normal ones because we know how each and every object in the infrastructure behaves and interacts with other objects. Xangati is the only commercial profiling engine that incorporates interactional metrics at the same level as more traditional consumptive metrics (such as resource usage metrics).
In an increasingly complex and diverse virtualized environment, you need to know about each and every object in the infrastructure, how they behave and interact with each other in a live and continuous fashion to a scale of hundreds of thousands of objects. Whether these elements are virtual, physical, on the ground or in the cloud, The Xangati Difference means that you have the built-in awareness to visualize and understand second-by-second behavior – resulting in a management solution that is proactive in helping you ensure virtual infrastructure performance.
Xangati Performance Management Engine
The cornerstone of Xangati’s Management Dashboard (XMD) suite is its patented in-memory engine that analyzes the health of each object in your cloud infrastructure across dozens of metrics in four microseconds.
This engine architected from the ground up for cloud scale can process over a million high-resolution performance metrics in a second whereas other solutions would take minutes to tens of minutes to handle the same amount of data.
This in-memory engine architecture is a foundational distinction between Xangati and any other performance management solution designed for the cloud. All other solutions have database-driven models that process much lower resolution performance metrics minutes after the fact. The result is that these other solutions essentially miss intermittent contention storms impinging on the performance of your complete environment. And even when other solutions do eventually achieve an alert state, it is minutes after the problem has dramatically impacted the entire user community. Xangati’s architectural distinction allows our users to leverage our automated insights to triage issues. This is enabled by the alert DVR recordings capability of the system.
The DVR recordings are effectively a visual representation of the second by second processing that the performance management engine does for every object. These recordings will precisely show the key metric that is being impacted e.g. storage latency but moreover the contention points that were driving the shift. And in many customer environments with these intermittent contention storms, the problems are multi-dimensional in nature i.e. at one point the issue is CPU contention, later it is storage and even later it might be network.
In addition to framing the problem within the context of these DVR recordings they can also be shared with different team members with different expertise. For instance, if the contention has been isolated to storage capacity then the recording can be passed over to the storage administrator who can more closely study the issue and make the appropriate adjustments to his part of the infrastructure.
