Chapter 16: Define And Describe Total Cost Of Ownership ✓ Solved
Chapter 16: Define and describe total cost of ownership. Lis
Chapter 16: Define and describe total cost of ownership. List at least 10 items to consider when determining a data center’s total cost of ownership. Define and describe a capital expense. How are capital expenses different from operational expenses? Define and describe economies of scale and provide a cloud-based example.
Define and describe “right-sizing” as it pertains to cloud computing. Define Moore’s law and discuss how it might influence cloud migration. Given company revenues of $2.5 million and expenses of $2.1 million, calculate the company’s profit and profit margin.
Chapter 17: Compare and contrast functional and nonfunctional requirements and provide an example of each. Discuss why a designer should avoid selecting an implementation platform for as long as possible during the design process. Discuss various trade-offs a designer may need to make with respect to nonfunctional requirements. Discuss why the system maintenance phase is often the most expensive phase of the software development life cycle.
Chapter 19: Define scalability. List five to ten potential relationships that align with the Pareto principle, such as how 80 percent of sales come from 20 percent of customers. Compare and contrast vertical and horizontal scaling. Explain the importance of the database read/write ratio. Assume a site guarantees 99.99 percent uptime. How many minutes per year can the site be down?
Chapter 20: List and describe five ways you think the cloud will change the future of TV. List and describe five potential uses for intelligent fabric. List and describe five ways the cloud will influence the mobile application market, or vice versa. Discuss the importance of HTML 5. Discuss how the cloud will impact future operating systems. List and describe three potential location-aware applications. List and describe five ways intelligent devices may work together.
Your responses should adhere to APA format and style and include proper citations to any sources you used in your answers.
Paper For Above Instructions
Chapter 16: Total cost of ownership, capital expenditures, economies of scale, and right-sizing
Total cost of ownership (TCO) is a holistic measure that captures all direct and indirect costs associated with acquiring, deploying, and operating an information technology or data center asset over its useful life. TCO goes beyond the purchase price to include ongoing costs such as energy, cooling, maintenance, staff, and downtime (Armbrust et al., 2010; Mell & Grance, 2011). In practice, TCO helps organizations compare capital-intensive investments against ongoing operating expenses and to understand long-term financial commitments (Weinman, 2012).
Items commonly considered in calculating data center TCO (at least 10 items):
- Capital equipment depreciation and lifecycle replacement
- Energy consumption (electricity for IT equipment)
- Cooling and HVAC infrastructure
- Power distribution and uninterruptible power supply (UPS) systems
- Physical security and facilities costs (floor space, real estate)
- Software licenses and maintenance contracts
- Network connectivity and bandwidth
- Staffing and operational labor costs
- Facility maintenance and facility management staff
- Downtime costs and business continuity planning
- Expansion capacity and scalability costs
- Taxes, insurance, and compliance-related expenses
Capital expenses (CapEx) are upfront investments in physical assets or long-lived infrastructure, such as servers, storage, and data center facilities. Operational expenses (OpEx) are ongoing costs of running the environment, including energy, software subscriptions, maintenance, and personnel. CapEx typically yields asset ownership and depreciation benefits over time, while OpEx affects monthly or annual operating budgets and may offer more flexibility but lower asset ownership (Mell & Grance, 2011; Erl, Mahmood, & Puttini, 2013).
Economies of scale occur when average costs per unit decrease as production or capacity increases. In cloud computing, providers achieve economies of scale by aggregating demand across thousands of customers, enabling highly efficient data centers, bulk power contracts, and optimized cooling. A cloud-based example is how a hyperscale provider can amortize hardware, network, and facility costs across many tenants, resulting in lower per-unit costs compared with running a private data center (Armbrust et al., 2010; Weinman, 2012).
Moore’s law, right-sizing, and cloud economics
Right-sizing refers to provisioning resources that closely match actual workload needs, avoiding significant over- or under-provisioning. In cloud computing, right-sizing helps organizations minimize waste and align cloud spend with demand, often through iterative monitoring and automated scaling policies (Kavis, 2014).
Moore’s law describes the historic trend of increasing transistor density and performance at roughly the same cost over time. Although physical limits loom, cloud providers continuously exploit advances in semiconductor technology to deliver more compute power at similar or lower price points, accelerating migration to cloud-based architectures as workloads scale and cost-per-performance declines (Moore, 1965; Armbrust et al., 2010).
Profit calculation example: With revenues of $2.5 million and expenses of $2.1 million, profit equals $0.4 million (profit = revenue − expenses). Profit margin equals profit divided by revenue, i.e., $0.4 million / $2.5 million = 0.16 or 16%. This straightforward calculation illustrates the importance of pricing, efficiency, and scale when evaluating cloud transitions against in-house infrastructures (Weinman, 2012; Buyya et al., 2009).
Chapter 17: Functional vs nonfunctional requirements, platform decisions, and maintenance costs
Functional requirements define what the system must do (e.g., authentication, data input, report generation). Nonfunctional requirements describe how the system performs (e.g., performance, reliability, security, maintainability) and often drive architectural decisions (Tanenbaum & Bos, 2014; Zhang, Cheng, & Boutaba, 2010).
Example: A banking application must support secure login (functional) and maintain transaction latency below a defined threshold (nonfunctional). A designer should delay committing to a single implementation platform to permit exploration of alternative architectures early, since platform choice constrains later design options, increases risk, and can hinder optimizations later in the process (Kavis, 2014; Erl et al., 2013).
Nonfunctional trade-offs include performance versus cost, security versus usability, scalability versus simplicity, and maintainability versus rapid delivery. These trade-offs influence architectural patterns, data storage choices, and testing strategies (Mell & Grance, 2011; Rimal et al., 2011).
The maintenance phase is often the most expensive in the software development life cycle due to ongoing bug fixes, updates, compatibility with evolving environments, and the need for continuous support resources (Tanembaum & Bos, 2014). This reality reinforces the value of solid design, proper documentation, and modular architectures that facilitate maintainability (Kavis, 2014).
Chapter 19: Scalability, Pareto relationships, scaling approaches, and uptime
Scalability is the ability of a system to handle increased load by adding resources or by optimizing usage of existing resources. The Pareto principle often surfaces in IT where roughly 80 percent of the benefit comes from 20 percent of the causes or users, guiding prioritization of optimization efforts (Weinman, 2012).
Vertical scaling increases the capacity of a single node (e.g., upgrading CPU/RAM), while horizontal scaling adds more nodes to distribute load. Horizontal scaling is generally more resilient and aligns with cloud-native architectures; vertical scaling can be limited by hardware constraints (Armbrust et al., 2010; Erl et al., 2013).
The database read/write ratio is a critical metric because read-heavy workloads behave differently from write-heavy workloads, affecting caching strategies, replication, and sharding decisions. A balanced ratio that optimizes latency and throughput is essential for performance (Rimal et al., 2011).
Uptime of 99.99 percent implies a downtime budget of about 52.6 minutes per year. This is calculated as (1 − 0.9999) × 365 days × 24 hours × 60 minutes ≈ 52.6 minutes. Designing for such reliability requires robust redundancy, failover mechanisms, and proactive monitoring (Mell & Grance, 2011; Buyya et al., 2009).
Chapter 20: Cloud implications for TV, intelligent fabrics, mobile apps, HTML5, OS, location-aware apps, and device synergy
Five ways the cloud will shape the future of TV include: (1) scalable streaming delivery via content delivery networks and cloud transcoding, (2) personalized content recommendations driven by cloud-based analytics, (3) ad insertion and measurement platforms hosted in the cloud, (4) dynamic cloud-assisted game and interactive experiences, and (5) multi-device synchronization and cloud-backed storage for resumes and preferences (Weinman, 2012; Armbrust et al., 2010).
Five potential uses for intelligent fabric include: wearable health sensors integrated into clothing, responsive textiles that adapt to temperature or moisture, embedded actuators for haptics or lighting, energy-harvesting fabrics to power small devices, and smart fabrics enabling real-time environmental monitoring (Weinman, 2012; Erl et al., 2013).
Five ways the cloud will influence the mobile application market (or vice versa) include: mobile apps leveraging cloud backends for storage and processing, increased use of API-driven architectures, ongoing demand for offline-first synchronization strategies, cloud-based push and notification services, and scalability patterns that favor serverless or microservices deployments (Armbrust et al., 2010; Buyya et al., 2009).
The importance of HTML5 lies in its capability to deliver cross-platform, feature-rich web applications with offline support, multimedia handling, and device-agnostic access, reducing the need for native deployments and enabling richer mobile experiences via the browser (Tanenbaum & Bos, 2014).
The cloud will impact future operating systems by enabling lighter-weight, cloud-integrated OS designs, frequent security updates via cloud-delivered policies, and increased reliance on cloud services for storage, compute, and management tasks (Mell & Grance, 2011; Zhang, Cheng, & Boutaba, 2010).
Three potential location-aware applications include: (1) context-aware navigation and local recommendations, (2) geofenced content delivery and targeted advertising, and (3) asset tracking and fleet management with real-time visibility (Armbrust et al., 2010; Rimal et al., 2011).
Five ways intelligent devices may work together include: cross-device data synchronization for seamless user experiences, cooperative sensing across wearables and home devices, distributed control of smart environments, combined data streams for enhanced analytics, and coordinated privacy controls across devices (Weinman, 2012; Buyya et al., 2009).
APA-format considerations: The discussion above integrates foundational concepts from cloud economics, scalability, and software architecture as established in the literature. In practice, students should reference primary sources when presenting calculations or design rationales and maintain adherence to APA guidelines in their final submissions (Armbrust et al., 2010; Mell & Grance, 2011; Zhang, Cheng, & Boutaba, 2010).