Relevant Literature
A growing body of academic and industry research validates the core benefits delivered by Critical Path Software’s (CPS) optimization tools. Papers from Google, AAAI, and top universities highlight how reducing CPU and I/O usage in data centers directly lowers energy consumption, carbon emissions, and operational costs—goals CPS achieves through non-invasive mainframe tuning. Studies on carbon-aware computing, workload scheduling, and power-performance tradeoffs all support CPS’s approach: optimize from within, reduce resource waste, and align IT operations with global sustainability targets.
How Critical Path Software
Supports a More Sustainable Planet
This AAAI 2024 paper, Carbon Footprint Reduction for Sustainable Data Centers in Real-Time [https://arxiv.org/pdf/2403.14092], provides strong support for Critical Path Software’s (CPS) optimization offerings by reinforcing the principle that efficient workload management and CPU reduction are critical to reducing data center carbon footprints. Here’s a breakdown of how it aligns directly with CPS’s tools like TurboTune® and TurboTuneSQL®:
1. CPU and Load Optimization = Lower Carbon Footprint
“Achieving significant carbon footprint savings requires reducing energy consumption and replacing carbon-intensive energy sources…” (p. 1)
Relevance to CPS:
TurboTune® and TTSQL reduce CPU and I/O activity, which leads to immediate energy savings—thereby reducing Scope 2 emissions. This aligns with the paper’s core objective: optimizing IT loads for sustainability.
2. Workload Shifting as a Carbon-Aware Strategy
“Carbon-Aware Workload Scheduling (CAS)… decreases carbon emissions by rescheduling workloads to times of lower CI [Carbon Intensity]” (p. 5)
Relevance to CPS:
While CPS doesn’t shift workloads based on renewable energy availability, it does optimize how those workloads run (e.g., improving SQL efficiency and VSAM tuning). This enables faster job completion and fewer CPU cycles, helping indirectly with load timing and energy window alignment.
3. Subsystem-Level Efficiency Matters
“Energy savings of 10-15% were achieved by optimizing cooling and IT loads using MARL agents” (Tables 2–4)
Relevance to CPS:
CPS optimizes at the subsystem level (e.g., VSAM, DB2), the exact layer where inefficiencies drive up total data center power demand. These targeted reductions offer a practical, low-effort path to achieving similar savings.
4. Low-Cost, Non-Invasive Optimization Approach
“Existing isolated methods… fail due to lack of real-time coordination and complex dependencies” (p. 2)
Relevance to CPS:
CPS provides real-world solutions that don’t require full-system reinforcement learning or AI/ML agents. Their software works with existing production environments without requiring massive re-architecture—offering fast, measurable reductions, especially in legacy mainframe environments.
5. Proof that Optimization is Measurable
“DC-CFR demonstrated an average carbon footprint reduction of 14.46%, energy usage of 14.35%, and energy cost of 13.69%” (p. 6)
Relevance to CPS:
CPS has documented customer outcomes showing 5%–30% CPU reduction, which translates into even higher energy and cost savings in some environments. This aligns well with the paper’s claim that workload and energy-aware strategies yield measurable environmental benefits.
6. Multi-Factor Environmental Benefits
“Optimization reduces not only operational emissions but also delays infrastructure upgrades and improves thermal efficiency” (p. 7)
Relevance to CPS:
CPS’s software also extends hardware lifespan and defers capital expenditures by improving performance with existing resources—reducing Scope 3 emissions tied to hardware refresh and manufacturing.
Summary of Alignment
| Optimization Target | DC-CFR (Paper) | CPS Offerings (TurboTune, TTSQL) |
|---|---|---|
| CPU Efficiency | Multi-agent RL tuning | VSAM & SQL tuning via static code/data analysis |
| Real-Time Energy Savings | Yes | Near-real-time via batch job tuning & SQL tuning |
| Scope 2 Emissions | Reduced via workload & HVAC tuning | Reduced via lower power draw from CPU reductions |
| Scope 3 Emissions | Extended hardware life, fewer upgrades | Same—by reducing stress on systems and storage |
| Cost Savings | 13–14% energy cost reduction | MLC and operational savings in the same range |
| ESG/Sustainability Goals | Fully supported | CPS tools contribute directly to ESG KPIs |
The paper Carbon-Aware Computing for Datacenters (Radovanović et al., 2021) [https://arxiv.org/pdf/2106.11750] provides significant conceptual support for Critical Path Software’s (CPS) optimization solutions by highlighting the growing importance of carbon-aware workload management in data centers. Although Google’s approach is focused on scheduling flexibility in cloud-native environments, many of the paper’s core principles reinforce CPS’s value in mainframe optimization for sustainability.
1. Workload Scheduling and Carbon Intensity
“We developed a system that shifts the timing of compute tasks to when low-carbon power sources are most plentiful.” (p. 1)
CPS Relevance:
CPS solutions (e.g., TurboTune® and TTSQL) don’t shift workload timing, but they reduce the intensity of the workload—minimizing the CPU cycles and I/O needed to complete jobs. This aligns with the same goal: lower energy consumption when executing IT tasks.
2. Carbon-Aware Decisions Start with Energy Optimization
“The carbon cost of electricity varies significantly over time and by location, and computing workloads should adapt accordingly.” (p. 2)
CPS Relevance:
CPS focuses on making workloads more efficient regardless of timing or location. By reducing CPU and I/O demand, even jobs run during high-carbon-intensity windows consume less total electricity, thus lowering their environmental impact.
3. Impact of CPU Optimization on Emissions
“By shifting compute tasks in time and location, we observed up to a 30% reduction in gross carbon intensity.” (p. 6)
CPS Relevance:
CPS customers have reported 5%–30% CPU reduction, which, if run during the same energy window, directly lowers emissions. Like Google’s results, CPS outcomes translate into carbon savings—but achieved by optimizing how workloads run instead of when or where.
4. Legacy Workloads Can Be Carbon-Intensive
“While many workloads are already optimized for cost and performance, they are not optimized for carbon impact.” (p. 4)
CPS Relevance:
Legacy mainframe systems are often untouched by modern sustainability efforts. CPS’s tools specifically target this gap—by tuning VSAM and DB2 workloads that contribute heavily to compute demand but lack optimization for energy and emissions.
5. The Need for System-Wide Optimization
“Sustainable computing requires a system-level view, considering both infrastructure and workloads.” (p. 2)
CPS Relevance:
CPS embodies this system-wide philosophy. It looks at subsystem-level inefficiencies in DB2 and VSAM that aggregate to meaningful system-wide energy savings—without requiring application rewrites or cloud migration.
6. Environmental Metrics Are Now Essential
“Carbon-aware computing introduces a new metric: carbon efficiency per job.” (p. 3)
CPS Relevance:
CPS enables organizations to track and document CPU reductions, which correlate to power and carbon savings. These metrics can support ESG reporting, Scope 2/3 emissions tracking, and regulatory compliance—especially for federal and public-sector clients.
7. Alignment with Google’s Vision of Sustainable IT
“We believe that every datacenter should become carbon-aware.” (p. 1)
CPS Relevance:
This statement underscores the global push toward carbon intelligence in IT operations. CPS offerings are a tangible way to bring carbon-aware principles into mainframe environments, which are often overlooked in modern sustainability planning.
Conclusion
This paper supports CPS’s mission by validating the carbon-saving impact of compute efficiency, reinforcing the importance of workload-level optimization, and offering a framework that helps translate CPS’s technical benefits into carbon-aware, sustainability-aligned outcomes.
Summary Table
| Principle from Paper | CPS Alignment |
|---|---|
| Shift workloads to lower CI times | Reduce workload intensity overall |
| Track carbon efficiency per job | Track CPU savings per job (for ESG/sustainability) |
| Focus on system-wide energy strategy | Tune subsystems (VSAM, DB2) for max impact |
| Optimize legacy workloads | Specializes in mainframe workload efficiency |
| Measurable carbon savings | 5%-30% CPU reduction = energy + carbon reduction |
| Require no cloud migration | Compatible with legacy Z/OS systems |
The paper “GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction” [https://www.mdpi.com/2071-1050/15/21/15249]offers strong validation and conceptual support for the value Critical Path Software (CPS) brings to sustainable IT—especially in legacy mainframe environments. While GEECO focuses on real-time multi-agent reinforcement learning (MARL) to manage cooling, battery use, and flexible workload scheduling in cloud-scale data centers, its key findings align directly with the benefits that CPS delivers through its subsystem-level CPU and I/O optimization.
1. Carbon-Aware Workload Optimization is a Priority
“Sustainability and carbon footprint reduction have emerged as critical factors driving the need for innovative optimization techniques in data center operations.” (p. 1)
CPS Alignment:
CPS tools (TurboTune®, TTSQL) are purpose-built to optimize resource-heavy mainframe workloads, reducing CPU usage and carbon emissions. These improvements support client ESG strategies in the same spirit as GEECO’s carbon-aware framework.
2. Energy Optimization = Carbon Footprint Reduction
“Achieving significant carbon footprint savings requires reducing energy consumption and replacing carbon-intensive energy sources…” (p. 1)
CPS Alignment:
CPS reduces energy demand by minimizing CPU cycles and I/O operations, particularly for VSAM and DB2 workloads. This aligns perfectly with the paper’s emphasis on energy reduction as the first step toward sustainability.
3. Subsystem-Level Coordination Yields Greater Gains
“Our approach can significantly decrease carbon emissions… Over a span of one year, [it] demonstrated an average carbon footprint reduction of 14.46%, energy usage by 14.35%, and energy cost by 13.69%.” (p. 6)
CPS Alignment:
These metrics mirror real-world results seen with CPS deployments, where customers have reported 5%–30% CPU reductions. While GEECO uses RL agents across HVAC, load shifting, and battery control, CPS achieves comparable energy results by optimizing CPU resource usage—without requiring AI/ML integration.
4. Legacy Systems Need Practical Optimization Paths
“Existing isolated methods… fail due to lack of real-time coordination and complex dependencies.” (p. 2)
CPS Alignment:
CPS solutions work immediately in existing enterprise systems (like Z/OS), offering non-invasive, low-lift paths to reduce CPU without the need to implement complex AI agents, simulations, or architectural overhauls.
5. Flexible Workload Tuning Enhances Sustainability
“ALS shifts flexible IT load to low grid CI hours.” (p. 10)
CPS Alignment:
Although CPS doesn’t shift job timing, it makes all workloads lighter, allowing them to complete faster and with less energy. This still contributes meaningfully to energy savings and carbon reduction.
6. Quantifiable, Location-Based Energy and Cost Reductions
“DC-CFR outperforms ASHRAE in carbon footprint, energy usage, and cost across New York, Washington, and Arizona.” (Tables 2–4)
CPS Alignment:
CPS optimization similarly supports quantifiable savings—especially in regulated or energy-cost-sensitive regions. These measured outcomes help organizations comply with Scope 2/3 tracking and support ESG reporting.
Summary Table: GEECO vs. CPS
| Optimization Element | GEECO (MARL Framework) | CPS (TurboTune & TTSQL) |
|---|---|---|
| Core Strategy | Real-time multi-agent coordination | Static subsystem-level optimization |
| Workload Management | Time-based flexible workload shifting | Code-based CPU/I/O reduction |
| Infrastructure Dependency | Requires HVAC, battery, energy models | No new hardware or energy models required |
| Emissions Impact (CO₂ reduction) | ~14.5% avg reduction (GEECO, p. 6) | 05%–30% CPU reduction (customer case studies) |
| Real-Time Operation | Yes (RL agents) | Achieved through batch/job tuning |
| Scope 2 & 3 Alignment | Direct (energy, hardware usage, cooling) | Direct (lower energy use, deferred hardware upgrades) |
| Complexity of Implementation | High (requires simulators, data models, RL tuning) | Low (quick to implement, low-code rollout) |
The paper “Power-Performance Tradeoffs in Data Center Servers: DVFS, CPU Pinning, Horizontal, and Vertical Scaling” [https://arxiv.org/pdf/1903.05488]provides multiple findings that strongly support the value proposition of Critical Path Software (CPS)—especially in the context of energy efficiency, CPU resource management, and sustainable IT infrastructure.
Here’s how the paper directly supports CPS offerings:
Key Alignments Between CPS and the Paper
1. CPU Resource Optimization is Central to Power Savings
“The power consumption of data centers accounts for 1.4% of total world consumption… 56% is used by servers.” (p. 2)
CPS Support:
TurboTune® and TTSQL reduce CPU usage by optimizing dataset and SQL efficiency. This directly targets the most energy-intensive component of data centers—the servers—where CPS tools operate, aligning with the paper’s core focus.
2. CPU Throttling and Scaling Have Tradeoffs
“Server throttling… reduces power but at the cost of performance degradation.” (p. 3)
“Horizontal and vertical scaling improve performance, but not proportionally to resource use.” (p. 4, 20)
CPS Support:
Rather than trading off performance for energy savings, CPS tools improve both. CPS achieves measurable CPU reductions without degrading throughput or user experience, avoiding the performance compromise seen in traditional throttling or scaling.
3. Intelligent CPU Workload Distribution Matters
“Pinning processes to cores makes a server dynamically energy proportional… especially at mid-utilization.” (p. 15)
CPS Support:
TurboTune makes similar gains by improving how workloads (especially VSAM and DB2 jobs) interact with system resources. Optimizing CI sizes, buffering, and access paths achieves greater efficiency without requiring hardware-level pinning or architectural changes.
4. Power-Performance Tuning Requires Deep System Understanding
“Reducing CPU frequency doesn’t always reduce energy use—under some loads, it increases power draw.” (p. 9)
“Performance degradation due to scaling and pinning can be mitigated with precise configuration.” (p. 15)
CPS Support:
This emphasizes the need for targeted, expert tuning—exactly what CPS provides through automated analysis and low-touch implementation. Unlike manual DVFS or VM pinning, CPS ensures reductions are beneficial and safe for workloads.
5. Workload Consolidation Has Limits
“Consolidation saves energy but often causes latency spikes, VM migration overhead, and cooling inefficiencies.” (p. 3, 20)
CPS Support:
CPS avoids this by optimizing within the workload itself rather than relying on risky consolidation techniques. That means no VM migrations, no rearchitecting, and no compromise to stability—all while reducing resource use and carbon footprint.
Quantitative Support Parallels CPS Results
CPU pinning reduced power by up to 7% under load—CPS has documented 5%–30% CPU savings, depending on environment complexity.
Horizontal/vertical scaling provided only partial gains—CPS achieves direct workload efficiency, not dependent on virtualization strategies.
The paper recommends combining techniques like pinning and scaling—CPS provides a unified, automation-driven approach without server reconfiguration.
Summary
| Paper Finding | CPS Alignment |
|---|---|
| CPU usage is the main driver of server energy use | CPS reduces CPU load via subsystem tuning |
| DVFS and CPU pinning have limited, conditional benefits | CPS offers consistent, measurable CPU and energy savings |
| Complex optimization strategies are needed | CPS automates those strategies at the workload level |
| Energy savings should not degrade performance | CPS improves performance and efficiency simultaneously |
| Server-level tactics (DVFS/pinning) need architectural awareness | CPS works without architectural disruption |
