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By selectively scaling down the execution frequency of application tasks, significant energy savings can be achieved while preserving timing constraints. For applications composed of dependent tasks, energy-aware frequency scaling is predominantly addressed through the\n                    <jats:italic>Scaling Axiomatic Approach<\/jats:italic>\n                    (\n                    <jats:italic>SAA<\/jats:italic>\n                    ), which exploits task slack to enable safe frequency reduction but incurs a high computational cost due to repeated global timing recalculations. To mitigate this limitation, the\n                    <jats:italic>GinGa<\/jats:italic>\n                    approach was proposed to reduce the computational complexity, albeit with a degradation in energy optimization effectiveness. This paper introduces the\n                    <jats:italic>Scaling Axiomatic Approach Replacement<\/jats:italic>\n                    (\n                    <jats:italic>SaaR<\/jats:italic>\n                    ), a low-complexity, compile-time mechanism designed as a principled replacement for\n                    <jats:italic>SAA<\/jats:italic>\n                    . While preserving the axiomatic foundation of slack-based frequency scaling,\n                    <jats:italic>SaaR<\/jats:italic>\n                    restructures the computation through bounded and localized timing-update mechanisms and a dedicated time-updating criterion, thereby eliminating repeated global recomputation. As a result,\n                    <jats:italic>SaaR<\/jats:italic>\n                    achieves energy savings comparable to those of\n                    <jats:italic>SAA<\/jats:italic>\n                    while significantly reducing the computational complexity. Experimental results confirm that\n                    <jats:italic>SaaR<\/jats:italic>\n                    outperforms\n                    <jats:italic>GinGa<\/jats:italic>\n                    and provides an effective balance between energy optimization and execution efficiency on DVFS-enabled computing platforms.\n                  <\/jats:p>","DOI":"10.1007\/s11227-026-08406-8","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:28:56Z","timestamp":1774952936000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SaaR: a strategy for energy efficiency in DVFS computing platforms beyond the scaling axiomatic approach"],"prefix":"10.1007","volume":"82","author":[{"given":"Tarek","family":"Hagras","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gamal A.","family":"El-Sayed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"issue":"3","key":"8406_CR1","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1007\/s10586-022-03713-0","volume":"26","author":"A Katal","year":"2023","unstructured":"Katal A, Dahiya S, Choudhury T (2023) Energy efficiency in cloud computing data centers: a survey on software technologies. 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