As companies strive to keep pace with and get ahead of the competition during a time of rapid, continuous digital transformation, cloud adoption is no longer a nice to have, it’s a must-have. But it’s not a straightforward process.

When choosing the right public cloud provider, you should also consider the importance of right-sizing resources against service demand to avoid service slowdowns and disruptions. Another critical factor is determining your resource requirements. You want to balance provisioning versus cost because overprovisioning can unnecessarily drive up costs and underprovisioning can risk service quality.

Without accurate insights, or an understanding of how cloud services will be used, a migration of on-premises workloads to the cloud, or a migration between cloud providers, could set you up for service failures, budget overruns, and overspending.

With the above complexities in mind where do you start?

BMC can help, with the free BMC Helix Cloud Migration Simulator. With this tool, you can reduce the time, complexity, and costs of migrating servers and workloads to the cloud and improve the accuracy of your planning process by evaluating and right-sizing your workload migration before you select a service provider. This gives you the insights you need to understand your resource requirements against the cost of maintaining your service levels—and stay within your budget.

The BMC Helix Cloud Migration Simulator allows you to compare the costs and resource requirements of leading public cloud providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Cloud, absolutely free.

• Increased insight: Keep budget owners, project owners, and stakeholders informed and in control of their cloud spend and budgets with self-service views.
• Actionable recommendations: Optimize cloud resources and eliminate wasted spend with artificial intelligence and machine learning (AI/ML)-driven recommendations and actions.
• Increased visibility and control: Get thresholds and alerts on spend, keep budget owners informed and in control of their cloud spend, and prevent budget overruns.
• Views of resource utilization: Create views customized for the cloud resources, budget, and spend that are important to budget owners, business owners, and stakeholders.
• Automated resource optimization: Receive automated recommendations and actions for right-sizing resources and terminating unused resources, such as idle or unused virtual machines, databases, and elastic block storage (EBS) volumes.
• Management of reserved instances: Analyze usage patterns of both reserved instances and on-demand resources to ensure efficient usage and get automated recommendations for converting on-demand instances to reserved instances to increase cost savings.

And so much more!

No Reservations is the late Anthony Bourdain’s Emmy Award-winning series chronicling his travels around the globe to explore the cities, villages, and countries that offer life’s truest surprises, without requiring reservations. However, in IT service and operations management, not having reservations could lead to negative implications that can impact service, reliability, costs, and customer satisfaction.

What are reservations and why are they important?

The ability to accurately estimate future resource requirements and align with ever changing business demand is critical for service assurance without overprovisioning, which can increase costs, or under provisioning, which can put service quality at risk. Reservations help ensure that the resources for new applications and business services are available when they’re needed, without incurring costs, until deployment.

So, what is a reservation? A reservation is a method of allocating resources at the time of the project, instead of pre-allocating resources and associated costs before they are needed. Examples of events that would require reservations are Black Friday, health insurance open enrollment periods, marketing campaigns, and product launches. Planning for these events requires input from multiple sources: marketing, sales, customer sentiment—and not just historical data.

Artificial intelligence (AI)- and machine learning (ML)-driven predictive analytics aligned with business key performance indicators (KPIs) helps enterprises identify the required resources they need to support increases in business demand due to special events or business growth. However, additional insight is required to determine how and when to allocate a given resource or set of resources for use by an event, specific workload, instance, project, user, department, or tenant for a specific period in time.

How and why reservations work

Reservations enable IT to deploy applications and services on time and understand which resources are available at the time of release. Viewing current resources—both used and available—along with future onboarded and offboarded resources, enables IT to accurately reserve resources for future needs without incurring costs ahead of time.

The more accurately and efficiently that IT departments can right-size resources with demand, the less they need to overprovision and waste resources. That also reduces the risk that services, applications, end-users, and customers will experience performance issues related to cost concerns and under provisioning. Actively aligning resources with demand also improves service while reducing both cost and risk, rather than pitting one goal against the other. This can result in significant savings without sacrificing service, workload, and application performance.

The benefit of reservation-aware resource management is that it provides detailed, projected saturation information to manage capacity based on future resource commitments. IT departments can manage resource reservations and increase future capacity by onboarding. This enables them to automatically evaluate and predict the spare capacity to optimize resources and costs while also reducing the trap of overprovisioning and underprovisioning.

To learn more about reservations and efficient resource, capacity, and cost management in today’s complex IT environments, download the e-book, “Service Assurance and Optimization with AIOps”

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One of the prevalent issues service owners, site reliability engineers (SREs), capacity planners, and cloud administrators face is how to accurately determine the cost effectiveness of migrating servers and business services that are hosted on-premises to the public cloud.

When evaluating cloud migrations, there are several factors to take into consideration to avoid negative implications, including:

• Cost of workload migration: Estimating the projected costs of moving servers and services to the public cloud versus the cost of keeping them on-premises
• Balancing cost versus risk: Determining the appropriate balance of cloud resources to stay within budgets while ensuring service assurance
• Single server versus multiple server migration: Determining the impact and cost of migrating a single server or all servers that a business service requires to the public cloud
• Forecasting resource requirements in the cloud: Managing multiple diverse, disconnected tools to analyze and forecast the capacity requirements in the cloud
• Optimizing servers to maintain service quality: Aligning cloud server resources with business services to maintain service quality and service level agreements (SLAs)
• Planning for future resource requirements: Identifying resources required to support future business demand and key performance indicators (KPIs)

If you don’t consider these potential hurdles, your cloud migrations could experience budget overruns, poor service quality, service interruptions, and slower onboarding of new services—all of which can affect profitability, customer loyalty and satisfaction, and competitive positioning in the market.

The good news is you can use “what if” simulations to estimate the costs of migrating workloads quickly and accurately to the cloud and aligning resources with business demand. “What if” migration simulations evaluate the opportunity and cost effectiveness of migrating on-premises infrastructure to the cloud and deliver right-sized recommendations to help reduce cloud cost and budget overruns and ensure service assurance. The “what if” simulation results enable you to:

• Quickly determine the projected cost of using the public cloud versus the cost of your on-premises infrastructure
• Have the flexibility to simulate the migration of selected servers or all servers that a business service uses
• View optimized recommendations for instance sizes and other associated characteristics on the public cloud
• Modify characteristics and evaluate the impact of the change on the overall migration cost
• Accurately compare costs of migrations between cloud service providers including Amazon Web Services, Microsoft Azure, Google Cloud, and IBM Cloud

To Learn More

Check out the following resources to understand effective strategies for accurately sizing and migrating on-premises resources to cloud.

• Service Assurance and Optimization with AIOps (e-book)
• IDG: Meeting the Challenges of Optimizing IT Cost and Capacity Management (whitepaper)
• BMC Helix Continuous Optimization (datasheet)
• It’s Time to Shift from Capacity Optimization to Resource and Cost Optimization (blog)

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Today’s enterprises are adopting new technology resources such as Kubernetes, containers, and Pods at a rapid pace to deliver modern applications and service assurance; onboard new workloads; and keep pace with business demand. However, Kubernetes administrators, service owners, and site reliability engineers (SREs) face challenges with right-sizing deployments to meet service level agreements (SLAs) and control costs against increasingly tight budgets. How do you analyze existing capacity, identify potential risks and bottlenecks, and accurately predict the resources required to support future changes in business demand? Where do you start?

Visualize resources you have to meet business and customer expectations

Do you have the right resources to satisfy business and customer expectations? Without insights into risk, efficiency, and cost, delivering quality services that meet your SLAs could be a challenge.

BMC Helix Continuous Optimization provides Kubernetes administrators, service owners, and SREs with the insights to understand the overall health and status of business services and gain visibility across all current enterprise resources to help maximize their performance, availability, and service. It also helps quickly identify overallocated Pods and containers, while automating optimizations to reduce service slowdowns and deliver service assurance.

Optimize existing infrastructure resources to meet business key performance indicators (KPIs) and SLAs

One of the primary challenges is determining how to make the most of the investments that run your business today.

BMC Helix Continuous Optimization provides artificial intelligence (AI), predictive analytics, machine learning (ML), and automation to help you dynamically optimize modern application resources based on Kubernetes, microservices, containers, and Pods. Predictive insight and recommendations for prescriptive actions can help you increase efficiency and lower costs.

AI- and ML-automated recommendations are based on historical resource use data, providing insight into unused or underutilized resources that can be terminated or reclaimed. Predictive saturation alerts and notifications help you adjust and optimize resource configurations to increase performance and efficiency while resolving resource constraints to prevent business service disruption.

Accurately plan to support changes in business demand

increasingly complex IT service and operations management (ITSM/ITOM) environments make it difficult to accurately predict and align resources to support business demand and innovation.

BMC’s Helix Continuous Optimization makes it easy to run “what if” simulations and reports to accurately predict resource requirements with before and after usage models. “What if” modeling provides insights into the best resource configurations, location, and cost so you can make informed decisions and prevent application performance slowdowns or failures.

To learn more about aligning Kubernetes resources to support business demand and innovation download the e-book, Service Assurance and Optimization with AIOps

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Could predictive insight and prescriptive action through artificial intelligence (AI) and machine learning (ML) drive positive business outcomes versus historically used capacity and cost optimization methods? Probably!

Here’s why.

The emergence of new technologies such as multi-cloud and hybrid cloud, along with the increased deployment of applications based on Kubernetes, micro-services, containers, and Pods, have dramatically increased IT complexity. The result is a myriad of capacity management and cost tools that are not meant to work together. That makes it difficult to get a clear picture of how current resources are performing to support applications and business services, and how to align them with business initiatives.

A recent IDG survey¹ of IT decision makers documents how IT professionals must juggle multiple tools to manage the capacity and cost of their hybrid cloud environments, inhibiting end-to-end visibility, slowing remediation response time, and making it harder to accurately anticipate demand. In fact, 76 percent of those surveyed admitted they were using a combination of cloud-based and on-premises capacity management solutions, with 63 percent using both on-premises and cloud-based IT cost-management solutions.

Organizational disruption is also increasing IT complexity. According to BMC Software product manager Dennis Newberry, “Over the last several years, there is more development being done within the lines of business and not within centralized IT. Where your long-term planning functions and infrastructure optimization occurred in IT, now it is happening outside of that core function. That amplifies the challenge of obtaining comprehensive visibility into those environments…because you have to work across a larger organization.”

What’s the answer?

To get there, you need a comprehensive, integrated, solution that increases visibility and facilitates effective planning for changing business demands. The ideal solution leverages input from across the enterprise, integrated with best-in-class optimization tools driven by AI, machine learning (ML), predictive analytics, and business key performance indicators (KPIs) to help IT and lines of business align IT resources with increases in business demand.

“What if” modeling can help. For example, simulations for cloud workload migrations and Kubernetes workload placement provide prescriptive insights into resource configurations, locations, and cost to inform decision-making and prevent application performance slowdowns or failures.

The benefits of predictive insight and prescriptive action using AI, ML, and advanced analytics include:

• Accurate and efficient rightsizing of resources aligned with demand and budget
• Reduced capital and operating expenditures (Capex/OpEx)
• Increased service performance, service level agreements (SLAs), innovation, and agility

To learn more, download the e-book, Continuous Optimization: The Next Evolution in Capacity, Resource and Cost Optimization.

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1 IDG Whitepaper: Meeting the Challenges of Optimizing IT Cost and Capacity Management ↩

In today’s complex business environment, organizations are striving to accelerate innovation with increased business agility, improved customer service, reduced capital and operating expenditures (CapEx/OpEx), and faster return on investment (ROI), all while contributing to growth and profitability. Capacity optimization isn’t getting it done anymore. It’s time to shift our thinking to resource and cost optimization.

Until recently, enterprises relied on capacity planning and optimization to align IT resources with changing business demand and ensure service level agreements (SLAs) for critical business services. However, as organizations adopt new technologies to deliver scalable services faster and more reliably, simply focusing on capacity optimization alone will not deliver the positive business outcomes IT decision makers seek. Why?

The emergence of new technologies such as multi-cloud, hybrid cloud, and legacy on-premises infrastructure, along with the increased deployment of applications based on Kubernetes, micro-services, containers, and Pods, have dramatically increased IT complexity. The result? IT is attempting to manage complex environments with multiple, disconnected tools that make it difficult to:

• View and understand the resources, usage, and costs of supporting applications and business services
• Identify dependencies between resources and the impact of changes to the infrastructure
• Accurately predict capacity and scale IT services to meet business demand
• Manage growing infrastructure complexity—the combination of on-premises and cloud-based capacity and cost management solutions and the transition to modern technologies (Kubernetes, containers, Pods, etc.) from legacy technologies

Having a toolset that’s loaded with disconnected tools is clearly not the answer, and it can also increase costs and degrade business service performance. A better alternative is to shift to comprehensive solutions that deliver deeper, artificial intelligence and machine learning (AI/ML)-enabled insights to help balance risk, efficiency, and cost. Organizations need a way to see resource usage and demand across the entire infrastructure to continuously optimize resources to deliver service and operational excellence that exceeds customer expectations, minimizes risk, and reduces capital and operational spend.

By doing so, they can:

• Achieve greater productivity and efficiency while reducing risk around migrations and strategic company initiatives
• Improve clarity into capital and operational costs and vendor billing
• Make the business more agile
• Plan and position for the future with more accurate IT forecasts

To learn more about the shift from capacity optimization to resource and cost optimization and how it could benefit your organization, download the e-book, Service Assurance and Optimization with AIOps.

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How are IT decision makers balancing risk versus cost using capacity optimization? Honestly, it’s a highwire act that jeopardizes service quality and budgets if not handled properly.

Today’s increasingly complex IT environments are making it more difficult for leaders to continuously optimize IT resources and costs to ensure optimal business service assurance, get a clear view across the entire IT infrastructure, and manage the transition of legacy technologies to modern technologies.

With these challenges, IT is forced to choose between overprovisioning by purchasing infrastructure such as servers and storage to ensure service quality or underprovisioning to save money. This is something BMC product manager Dennis Newberry addressed in a recent IDG white paper, Meeting the Challenges of Optimizing IT Cost and Capacity Management.

“How do you manage that balance? I can increase my costs and lower my risk by overprovisioning my environment, so I’ll never have a performance-related outage,” he said. “Conversely, if I want to reduce costs, I could run the potential of having a performance-related issue. So ultimately you’re trying to find the right balance and then maintain that balance.”

How do you achieve and maintain the correct balance between overprovisioning and underprovisioning? You need a way to accurately see the resource usage and demand across your entire infrastructure to ensure you can support your applications, users, and business strategy with high service quality and responsiveness.

Enterprises using artificial intelligence (AI) and machine learning (ML) tools to increase visibility and support business demand will be able to continuously optimize their resources by rightsizing, maximizing allocation and efficiency, predicting saturation, and matching business initiatives with “what if” simulations and service level agreements based on intelligent analysis of service performance—all without manual intervention.

Increased visibility also helps IT teams:

• Improve IT and end-user productivity and efficiency
• Reduce risk around migrations and strategic company initiatives
• Achieve better clarity into capital and operational costs and vendor billing
• Make the business more agile
• Plan and position for the future with more accurate forecasting

To learn more about effectively balancing cost versus risk with capacity optimization solutions, download the e-book, Service Assurance and Optimization with AIOps.

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IT decision makers (ITDMs) face several challenges managing IT capacity and cost in today’s increasingly complex IT service and operations management (ITSM/ITOM) environments. In fact, 86 percent readily admit in a recent IDG survey that they find it “very challenging” or “extremely challenging” to optimize their IT resources to adequately meet changing business demands. Top challenges include:

• An inability to scale IT services as needed
• Difficulty predicting capacity requirements
• Lack of visibility into resource usage and cost
• Managing the growing infrastructure complexity of on-premises and cloud-based capacity and cost management solutions
• Organizational disruption due to new ways of doing business, i.e., the surge of remote workers due to the pandemic
• Managing the transition to modern technologies—Kubernetes, containers, Pods and so on—from legacy technologies

These difficulties become even more concerning when one considers the continued emphasis on “doing more with less” due to shrinking ITSM/ITOM budgets. Additional complications include managing the exponentially growing volume of data and number of applications, device types, and users—along with user expectations.

So, how are leading IT decision makers finding a balance and meeting those emerging requirements while maximizing the efficiency of expensive resources?

The short answer is they are using software solutions that increase the visibility of resources across the enterprise—physical, virtual, container, Pods, Kubernetes, and cloud infrastructure—to help identify the resources they have and the dependencies and relationships between them. Greater visibility also provides comprehensive views into how changing business requirements impact the IT resources needed to maintain service levels, capacity requirements, budgets, and costs.

With today’s discovery and optimization tools, IT decision makers can gain deeper insight into resource demand and usage. Modern discovery and optimization tools also help them better predict, plan, deploy, and right-size IT resources leveraging artificial intelligence (AI) and machine learning (ML) recommendations to accurately align IT resources with emerging business requirements.

According to the IDG survey, ITDMs can yield a number of benefits, including:

• A reduction in budget overruns by leveraging AI- and ML-driven analytics for resource and cost optimization
• Reduced slowdowns, service outages, and risk, with a deeper understanding of the overall health of business services, through comprehensive views and predictive saturation forecasting
• Increased alignment of resources with business requirements using advanced analytics and forecasts for actionable insights that help ensure service quality
• Improved accuracy when determining the impact of legacy workload migrations to the cloud, and placement of containerized workloads using “what if” simulations

To learn more about efficient resource, capacity, and cost management in today’s complex IT environments, download the e-book, “Service Assurance and Optimization with AIOps.”

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The future of service and operations management is here now and is crucial to control the chaos of hybrid IT. Innovations in service and operations management are emerging as an imperative from the service desk to the boardroom for enterprises to achieve faster time-to-market; increase productivity, efficiency, and quality of service. In a landscape that seems to evolve on a daily basis, successful digital leaders know that aligning IT service and operations management is the key to growing at the scale required to compete and thrive. Industry leading enterprises are embracing this convergence to drive a service experience that delivers service delivery excellence, empowers line-of-business users, and increases business agility.

Instead of treating each as silos, there are immediate benefits realized with a converged end-to-end service and operations management system:

• Faster time-to-market for delivery of quality, differentiating, services
• Faster resolution of service issues and operational events to increase service quality
• Increased IT staff efficiency enabling focus on strategic initiatives
• Automated resolution of routine issues freeing up service desks to focus on issues that impact productivity & efficiency
• Reduced risk of security vulnerabilities
• Increased business agility
• Reduced CapEx

Not only does integration break down silos, it helps you connect the dots across your ecosystem turning your unknowns into knowns. Let’s explore how you can take advantage of these benefits by first understanding best practices and critical capabilities required as you evaluate adopting a converged ITSM / ITOM environment.

Industry leading service and operations management tools should:

• Integrate and automate all key processes including incident, problem, change, release and SLA management to uncover events, alerts and anomalies in real time.
• Apply AI and machine learning across operations management (AIOps), service management and user environments to accelerate resolution of requests and trouble tickets, as well as automated event remediation.
• Provide a flexible and streamlined user experience for self-service and request fulfillment.
• Support non-IT business use cases such as finance and CRM.
• Understand infrastructure trends and adjust automation to match.
• Enable scalability beyond the data center.
• Deliver seamless operational support of enterprise services across multi-cloud environments.
• Increase visibility and control with end-to-end integration of data, management tools and platforms including service and operational dependencies, performance and utilization.
• Reduce risk and cost through intelligent control of services and infrastructure.
• Provide a unified view of cost and performance metrics for all service delivery and IT operations to ensure cost-effective and efficient use of resources.

Learn more about how your enterprise can leverage convergence of service and operations management to increase service efficiency, quality, speed and business agility while reducing costs. The Future of Service and Operations Management: A Buyer’s Guide to learn:

• For IT operations managers, it’s meeting strategic goals for service delivery excellence through automated tools and information that reduce manual workload and use human intervention where it adds the most value rather than responding to alerts and requests.
• For line-of-business users, it’s access to the services and information they need to help themselves, quickly and simply. The right action at the right time, with the least friction, through powerful user portals, intelligent automation and easy-to-use service catalogs.
• For the enterprise as a whole, it’s enhancing the ability to empower users across the organization through digital transformation that’s powerful, efficient and effective.

IT process automation (ITPA) can make it much faster and more efficient to manage infrastructure and operations. In the process, ITPA can deliver a number of strategic advantages, including accelerating an organization’s digital transformation. In spite of all the potential of ITPA however, determining the best path forward isn’t necessarily very clear cut for decision makers. Part of the challenge is that the ITPA market includes quite a few tools and solutions, which can vary substantially in their approaches, strengths, and weaknesses. How do you determine which solution and approach is best for your organization? Read on to learn more.

In the following sections, I take a look at some of the different approaches available currently, and I detail how these differences can determine whether or not your organization achieves its business and operational objectives.

Task Automation is not ITPA—and Why the Distinction Matters

Task-level automation refers to the process of automating specific, discrete efforts. However, task-level automation is limited in its potential. Simply automating a specific task can reduce some manual efforts, but it can’t deliver top-level business objectives like enhancing compliance or reducing incident escalations.

On the other hand, ITPA automates and orchestrates entire IT processes across multiple functions and domains. ITPA often requires the combination of disparate management tools, as well as predefined workflows, advanced capabilities like machine learning, and more. Compared to task automation, ITPA is far more powerful because it enables your teams to better align IT services with the needs of the business. ITPA can help your teams improve the speed, accuracy, and effectiveness of IT service delivery. With ITPA, your organization can realize the following benefits:

• Reduced costs. Automating entire workflows and complex, interconnected processes reduces the need for human intervention and the need to escalate issues to more expensive, high-level staff members.
• Enhanced business agility. Through ITPA, IT teams can move from being a cost center to being a generator of business value. ITPA can help you manage change more effectively, deliver more innovative services, and speed issue resolution.
• Improved user experiences and service levels. User requests can be handled faster through automated self-service, without needing skilled IT staff to be involved. By reducing the need for manual intervention in IT processes, ITPA increases accuracy while improving responsiveness.
• Decreased risk. Through ITPA, your teams can establish the standardized processes that promote more consistent and efficient compliance with regulatory mandates and security policies. ITPA can help your organization reduce the potential for errors, streamline approvals, and enhance monitoring. With these capabilities, you can establish more granular governance, more frequent and accurate infrastructure audits, and better security.

What capabilities should you look for when evaluating ITPA offerings?

As decision makers evaluate ITPA alternatives, it is important to focus on the following critical capabilities:

Simplicity

An ITPA solution should be straightforward to set up. It should also deliver the rich, pre-packaged capabilities that speed time to value. The better a solution’s capabilities in this regard, the better your organization’s chances of realizing your cost reduction, productivity, and compliance goals.

It is essential that solutions simplify the process of setting up automation. Look for solutions that offer predefined workflow templates, actions, and connectors. By establishing automated coordination of workflows, teams can realize these objectives:

• Ensure events are detected and remediated sooner, which enhances quality of service.
• Institute more infrastructure checks and updates, which helps boost compliance.
• Simplify change management, which improves productivity and user satisfaction.

Orchestration focus

It is vital to find an integrated platform that enables an automation-first strategy. Your teams need a solution that supports the creation of workflows that span applications, platforms, and tools. With a strong ITPA solution, your team can orchestrate critical activities in order to maximize compliance, security, and efficiency.

By enabling orchestration across domains and functions, ITPA solutions enable your teams to address business challenges that extend beyond isolated efficiency issues. For example, solutions can employ advanced machine-learning and artificial-intelligence technologies in order to make orchestrated processes “smart”—leveraging historical and real-time data in order to address potential issues before they occur.

Broad integration

An advanced ITPA and orchestration solution must offer the flexibility needed to bridge the gaps between systems, platforms, and domains. To close the automation loop, a solution must integrate seamlessly with a range of technologies, including tools for event management, configuration management, virtualization, service desks, security, and cloud management. By offering prebuilt connections for relevant management and automation systems, ITPA solutions can simplify deployment and help your organization fully leverage existing resources and investments.

Want to learn more about key differences between ITPA offerings and what you should consider in your evaluation? Please be sure to access a copy of our buyer’s guide.