https://ojs.wiserpub.com/index.php/CCDS <p>Cloud Computing and Data Science(CCDS) is an international, open-access, and peer-reviewed journal dedicated to advancing research in cloud computing and data science. The topics of strong interest to our readership span the exploration of established and rapidly emerging topics, which include but are not limited to: green cloud computing, edge computing, big data, and data mining, <a href="http://ojs.wiserpub.com/index.php/CCDS/about"><u>click here to see more...</u></a></p> <p> </p> Universal Wiser Publiser en-US Cloud Computing and Data Science 2737-4106 https://ojs.wiserpub.com/index.php/CCDS/article/view/9014 <p>In this paper, we demonstrate that a server running a single latency-sensitive application can be treated as a black box to reduce energy consumption while meeting a Service-Level Agreement (SLA) target. We find that it is possible to identify “sweet spot” settings for packet batching and processing rate control. These settings represent optimal trade-offs between the software stack and hardware. Specifically, they account for both the arrival rate and the composition of requests being served. By testing a few combinations of these settings on the live system, a proof-of concept controller can dynamically find settings that reduce energy consumption while meeting a desired tail latency for the request rate. Our work demonstrates three key findings. First, without software changes, energy savings of up to 60% are achievable across diverse hardware systems by controlling batching and processing rates. Second, specialized research Operating Systems (OSes) can leverage this to achieve a further 40% energy savings over general-purpose OSes. Finally, we show that a controller that is agnostic to the application, system, and hardware, can find energy efficient settings for different request rates while meeting performance objectives.</p> Han Dong Sanjay Arora Yara Awad Orran Krieger Jonathan Appavoo Copyright (c) 2026 Han Dong, Sanjay Arora, Yara Awad, Orran Krieger, Jonathan Appavoo https://creativecommons.org/licenses/by/4.0 2026-03-11 2026-03-11 191 214 10.37256/ccds.7220269014 https://ojs.wiserpub.com/index.php/CCDS/article/view/9092 <p>An increasing number of companies are migrating their Enterprise Resource Planning (ERP) systems to the cloud-an area that remains relatively underexplored, as traditional ERP systems were typically deployed on premises. While many organizations already operate other systems in the cloud, ERP systems are particularly critical because they integrate core business processes and manage daily operations, making cloud migration a high-risk transformation that must be carefully planned and executed to ensure business continuity. This study aims to identify the key challenges and implementation frameworks associated with Cloud ERP migration through a Systematic Literature Review (SLR). Studies published between 2015 and 2025 were retrieved from five academic databases, screened using predefined inclusion and quality criteria, and synthesised using Excel and Orange Data Mining software, resulting in a final sample of 58 studies. The results identify 26 distinct challenges-such as data migration, security and privacy concerns, vendor dependence, and resistance to change-and 12 classes of frameworks intended to mitigate these barriers across different organizational and contextual settings. In contrast to prior reviews that primarily catalogue adoption drivers or isolated challenges, this study contributes a structured synthesis that explicitly maps implementation challenge categories to classes of implementation frameworks across different organisational contexts. This integrative perspective reveals systematic coverage gaps in existing frameworks and provides decision-oriented guidance for selecting implementation approaches in practice.</p> Caroline Hornegger Michael Kohlegger Christian Ploder Copyright (c) 2026 Caroline Hornegger, Michael Kohlegger, Christian Ploder https://creativecommons.org/licenses/by/4.0 2026-03-19 2026-03-19 169 190 10.37256/ccds.7220269092