Riverbed Technology introduced new capabilities to its Cascade application-aware network performance management (NPM) solution, which delivers the needed visibility into virtualized data centers and data centers that use load balancing application delivery controllers (ADCs).
This new release, Cascade 9.5, extends end-to-end service performance monitoring and troubleshooting – from the remote LAN, across the wide area network (WAN), and now deeper into the data center. With this new release, the Cascade product line provides capabilities for complex, global networks that include load-balanced and virtualized data centers.
Enterprise customers deploy ADCs to optimize application delivery, flexibility and resiliency. The challenges presented by load balancing ADCs create significant visibility problems for network operations—by masking the necessary metrics and application dependencies — limiting their ability to monitor for end-to-end performance or troubleshoot degradations. Cascade 9.5 automates the discovery and monitoring of load balanced applications. By integrating with ADCs—F5 Local Traffic Manager, Riverbed Stingray Traffic Manager, and others—Cascade bridges the visibility gap between the client-side and server-side connections of load balancers, providing IT operations with a view of application performance.
In addition to deploying ADCs to optimize application delivery, enterprise customers are also virtualizing their data centers and consolidating IT resources to achieve flexibility and cost savings. However, as with ADCs, virtualization creates visibility challenges for IT operations teams because once an application enters a virtualized environment, they lose visibility into application performance. The new Virtual Cascade Shark software provides the real-time visibility needed to manage and troubleshoot application performance as it traverses the virtual switch in VMware ESX environments.
Virtual Cascade Shark monitors all inter-virtual machine traffic, exporting application-enhanced traffic metrics to the Cascade Profiler performance management dashboard, for centralized service-level discovery and monitoring and accelerated troubleshooting. It also continuously captures packets and stores them on the local server or in a storage area network (SAN) for back-in-time analysis with Cascade Pilot network analysis software.
This release of Cascade also includes a number of new features, including:
* Multi-segment analysis – Cascade Pilot simplifies the task of correlating and analyzing related traffic streams captured from multiple locations or sources to quickly identify where on the network performance issues are occurring – whether it is on the remote LAN, the WAN or within the data center.
* Precision time stamping support – Cascade Shark can now adopt nanosecond time stamps from SPAN port aggregators for greater precision and accuracy. Adopting time stamps from existing network equipment is more cost-efficient and enables coordinated time stamping across the network for performance troubleshooting. It can also be used with the new multi-segment analysis capabilities within Cascade Pilot for monitoring low-latency trading environments and other time-sensitive applications for business critical operations. Supported network taps include Gigamon GigaVUE, cPacket Networks CVU and cTap, and VSS Monitoring Distributed Traffic Capture.
* VoIP quality reporting – Cascade 9.5 tightens the integration between Cascade Shark and Cascade Profiler by bringing support for voice over IP (VoIP) protocols and quality metrics into Cascade Profiler. This information enables IT managers to determine how VoIP services are performing in conjunction with data resources in order to make effective capacity planning and optimization decisions, and improve end user experience. Supported VoIP quality metrics include mean opinion score (MOS), R-Factor, packet loss and jitter. In addition, Cascade supports the Cisco Skinny Client Control Protocol (SCCP).
Availability: The enhanced Cascade solution is expected to be generally available in Q1 2012.
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