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The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency…
The rapid expansion of cloud services and their unpredictable workload demands present significant challenges in resource management. Traditional resource management approaches, primarily based on static rules and thresholds, often fail to…
Both the training and use of Large Language Models (LLMs) require large amounts of energy. Their increasing popularity, therefore, raises critical concerns regarding the energy efficiency and sustainability of data centers that host them.…
Data centers have become a popular computing platform for various applications, and they account for nearly 2% of total US energy consumption. Therefore, it has become important to optimize data center power, and reduce their energy…
The rapid expansion of cloud computing and data center infrastructure has led to significant energy consumption, posing environmental challenges due to the growing carbon footprint. This research explores energy-aware management strategies…
This paper presents datacenter power profiles, a new NVIDIA software feature released with Blackwell B200, aimed at improving energy efficiency and/or performance. The initial feature provides coarse-grain user control for HPC and AI…
Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve the performance of data center computer systems, in which…
Dynamic nature of the cloud environment has made distributed resource management process a challenge for cloud service providers. The importance of maintaining the quality of service in accordance with customer expectations as well as the…
We use Energy Packet Network paradigms to investigate energy distribution problems in a computer system with energy harvesting and storages units. Our goal is to minimize both the overall average response time of jobs at workstations and…
Cloud platforms have been focusing on reducing their carbon emissions by shifting workloads across time and locations to when and where low-carbon energy is available. Despite the prominence of this idea, prior work has only quantified the…
Scientific workflow management systems support large-scale data analysis on cluster infrastructures. For this, they interact with resource managers which schedule workflow tasks onto cluster nodes. In addition to workflow task descriptions,…
Data centers are the building blocks of IT business organizations providing the capabilities of centralized repository for storage, management, networking and dissemination of data. With the rapid increase in the capacity and size of data…
Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but…
The soaring energy demands of large-scale software ecosystems and cloud data centers, accelerated by the intensive training and deployment of large language models, have driven energy consumption and carbon footprint to unprecedented…
Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing…
Task graphs provide a simple way to describe scientific workflows (sets of tasks with dependencies) that can be executed on both HPC clusters and in the cloud. An important aspect of executing such graphs is the used scheduling algorithm.…
Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments…
Cloud providers have introduced pricing models to incentivize long-term commitments of compute capacity. These long-term commitments allow the cloud providers to get guaranteed revenue for their investments in data centers and computing…
Developing CPU scheduling algorithms and understanding their impact in practice can be difficult and time consuming due to the need to modify and test operating system kernel code and measure the resulting performance on a consistent…
Cloud Computing is the latest blooming technology in the era of Computer Science and Information Technology domain. There is an enormous pool of data centres, which are termed as Clouds where the services and associated data are being…