1. Be Clear and Specific

• Objective: Clearly describe the main focus of the research (e.g., Kubernetes scheduling, heterogeneous clusters, ML workloads).
• Avoid Ambiguity: Use precise terms to indicate the system, problem, or technology (e.g., "Kubernetes scheduler" instead of "orchestration system").
• Highlight the Contribution: Include the novel aspect (e.g., "extension," "optimization") to differentiate from existing work.

2. Incorporate Key Keywords

• Purpose: Include domain-specific terms to ensure the title is discoverable in academic databases (e.g., IEEE Xplore, ACM Digital Library) and aligns with Computer Systems terminology.
• Examples: Use terms like "scheduling," "heterogeneous computing," "machine learning workloads," "Kubernetes," "resource optimization."
• Balance: Avoid overloading with jargon; aim for terms that are broadly understood in systems research.

3. Emphasize Novelty and Impact

• Highlight the Gap: Suggest how the research addresses an unsolved problem (e.g., "optimized for heterogeneous clusters").
• Focus on Outcome: Include the intended benefit, such as "improved performance," "enhanced scalability," or "efficient resource utilization."
• Systems Focus: Emphasize systems-level contributions (e.g., scheduler design, system integration) typical in OSDI, SOSP, or EuroSys papers.

4. Keep It Concise

• Length: Aim for 8-15 words to balance detail and brevity, as longer titles may lose impact.
• Avoid Redundancy: Eliminate unnecessary words (e.g., "A Study on" or "Towards") unless required by journal/conference guidelines.
• Clarity Over Creativity: Prioritize clarity over catchy phrases, as is standard in systems research.

5. Align with the Problem Statement

• Consistency: Ensure the title reflects the problem statement (e.g., addressing suboptimal scheduling in Kubernetes for ML workloads on heterogeneous clusters).
• Scope: Match the scope of the aim and objectives (e.g., focus on scheduler extension, not general orchestration).

6. Follow Conference/Journal Conventions

• Check Guidelines: Review target venues (e.g., NSDI, ASPLOS, EuroSys) for title formatting preferences (e.g., capitalization, colons).
• Style: Use a descriptive or declarative style common in systems papers (e.g., "Optimizing X for Y" or "A System for Z").
• Examples from Field: Model after titles in top systems conferences, such as "Tiresias: A GPU Cluster Manager for Distributed Deep Learning" [NSDI 2020].

7. Test for Readability and Appeal

• Audience: Ensure the title is understandable to systems researchers and practitioners familiar with Kubernetes and ML.
• Feedback: Share drafts with peers or mentors to confirm clarity and relevance.
• Searchability: Test if the title includes keywords likely to be searched (e.g., "Kubernetes," "heterogeneous," "machine learning").

8. Avoid Overpromising

• Be Realistic: Don't claim overly broad impacts (e.g., "Revolutionizing Cloud Computing") unless justified.
• Focus on Contribution: Emphasize the specific system or technique (e.g., scheduler extension) rather than vague goals.

Discussion of the Example Title

• Clarity and Specificity: The title clearly specifies the system (Kubernetes), the component (scheduling), the target application (machine learning workloads), and the context (heterogeneous CPU/GPU/TPU clusters).
• Keywords: Includes key terms like "Kubernetes," "scheduling," "machine learning," "heterogeneous," and "CPU/GPU/TPU" for discoverability in systems research.
• Novelty and Impact: "Optimizing" suggests a performance-focused contribution, addressing the gap in inefficient pod placement for ML workloads.
• Conciseness: At 10 words, it's brief yet descriptive, fitting within typical systems paper title lengths.
• Alignment with Problem Statement: Reflects the problem statement's focus on suboptimal scheduling in Kubernetes for heterogeneous ML clusters, emphasizing performance and resource utilization.
• Systems Convention: Follows a descriptive style common in OSDI or NSDI papers (e.g., "Optimizing X for Y") and avoids vague or overly broad terms.

Alternative Title Examples

To illustrate flexibility, here are variations depending on emphasis:

• More Technical: "A Kubernetes Scheduler Extension for Heterogeneous ML Workload Optimization"
• Broader Scope: "Efficient Scheduling for ML Workloads in Heterogeneous Kubernetes Clusters"
• Specific Metric: "Low-Latency Kubernetes Scheduling for Heterogeneous ML Clusters"

Additional Tips for Computer Systems Context

• Reflect Systems Priorities: Use terms like "optimizing," "efficient," or "scalable" to align with systems research goals (e.g., performance, scalability, resource efficiency).
• Reference the Platform: Explicitly mentioning "Kubernetes" ensures relevance to cloud orchestration research.
• Highlight Application: Including "machine learning workloads" ties to the growing importance of ML in systems research.
• Check Venue Fit: For example, NSDI prefers titles emphasizing systems and networking, while ASPLOS may favor hardware-software integration (e.g., mentioning CPU/GPU/TPU).

Part 6 will focus on the Evaluation.