As part of our Cloud Insight Jam, we got in touch with several experts and asked for their predictions on cloud optimization and monitoring in 2020. These experts represent the top cloud vendors, cloud solutions providers, and IT software companies, and have decades of combined experience with optimizing and monitoring the cloud. We’ve compiled seven quotes from six experts on where they see cloud optimization and monitoring in 2020 and beyond.
Thanks to all of these experts for submitting their quotes and advice — and be sure to follow us on Twitter all day for insights, advice, and best practices on cloud computing during our #CloudInsightJam!
Nik Koutsoukos, CMO and Evangelist, Catchpoint
Digital Consumers and Digital Professionals Behave the Same
“In the past year, we have seen a real shift in how digital consumers and digital professionals use web-based technologies to the point where it is hard to tell the difference between the two groups. They both behave the same online and at work, creating a greater need for network insights and monitoring to understand the differences. In 2020, we will see a greater investment in monitoring internet-based services and network delivery to ensure user-based services like apps in the cloud and SaaS solutions are available and meet end-user expectations.”
Customer and Employee Experience Monitoring Matures
“Enterprises will continue investing in digital tracking of customers and employees to deliver high-class experiences. The challenge is data privacy, protection and regulation will complicate the effective delivery of digital services. This will cause vendors to jump through hoops to deal with an increasingly more complex set of privacy and protection rules. We expect to see monitoring evolve to meet the user experience needs while meeting regulatory and privacy needs.”
Tomer Shiran, Cofounder and CEO, Dremio
Enterprises Say Hello to Efficiency Benchmarks
“Escalating public cloud costs have forced enterprises to re-prioritize the evaluation criteria for their cloud services, with higher efficiency and lower costs now front and center. The highly elastic nature of the public cloud means that cloud services can (but don’t always) release resources when not in use. And services which deliver the same unit of work with higher performance are in effect more efficient and cost less. In the on-premises world of over-provisioned assets such gains are hard to reclaim. But in the public cloud time really is money. This has created a new battleground where cloud services are competing on the dimension of service efficiency to achieve the lowest cost per compute, and 2020 will see that battle heat up.”
Dimosthenis Kaponis, CTO, Netdata
Enterprises Need Better Solutions for Observability
“Monitoring is one of the pillars of Observability, alongside Logging and Tracing. And the rapid adoption of modern cloud-native architectures, where everything, from infrastructure to production loads to issues, is ephemeral, renders many of the existing, centralized monitoring solutions largely inadequate both from a performance standpoint, and a cost perspective. At the same time, the advent of 5G promises an explosion in the number of always-connected devices, and with them the system and application monitoring needs. Vendor-neutral, real-time and distributed Observability, coupled with open collaboration platforms are our best bet in addressing the challenges of performance, scale and economics in the industry.”
Frank Jablonski, VP of Global Marketing, SIOS Technology
Machine Learning and AI Will Deliver Cost Savings Through Greater Cloud Efficiencies
“Enterprises are looking for application and cloud service providers to help them operate more efficiently through the use of machine learning (ML) and artificial intelligence (AI) to deliver more effective resource management. Achieving this will require the environment or application to understand when it needs more resources and then automatically scaling up those resources to meet the increased demand. Conversely, the technology will need to understand when specific resources are no longer needed and safely turn them off to minimize costs. Today such dynamic resource allocation can be unreliable or must employ an inefficient manual process, forcing cloud customers to either spend more than necessary or fall short of meeting service levels during periods of peak demand.”
Angelique Medina, Product Marketing Director, ThousandEyes
Backbone Networks Increase Dramatically
“As the amount of Internet traffic grows by the minute with every TikTok video, business traffic is competing against cat videos on a network that it wasn’t designed for. Just as the ThousandEyes Cloud Performance Benchmark report found Google Cloud and Microsoft Azure preferring to use their own private backbone networks (with AWS and IBM also offering this option), we’ll see more SaaS companies and cloud-based service providers creating private backbone networks to optimize their own network traffic instead of relying on the unpredictable public Internet.”
Bob Farzami, VP of Cloud Product Management, Virtana
Dynamic Resource Allocation Will Gain Traction as an Optimization Method
Public cloud optimization has experienced a significant evolution over the last few years. The first generation of cloud optimization services focused on bill analysis, with the second generation focusing on cross-analyzing cost with capacity. The third and current generation encompasses the previous two generations, in addition to hybrid infrastructure monitoring and capacity planning. This generation yields recommendations on whether workloads would perform best on premise or in the cloud, depending on what’s more cost effective. In 2020 and beyond, we’ll see a fourth generation of public cloud optimization emerge: dynamic resource allocation, enabled by workload automation technology comprised of performance capacity and cost management capabilities. In order for Gen-4 to proliferate, security and governance issues will need to be addressed for third-party vendors to gain the access they need to analyze workloads and change control processes for mission critical applications.
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