AUM: Unleashing the Efficiency Potential of Shared Processors with Accelerator Units for LLM Serving

Cronus [1-7] is an object oriented distributed system which operates in a heterogeneous computer environment of interconnected local area networks. As a distributed system architecture. Cronus faces a number of resource management issues not present in non-distributed architectures. Strategies for effectively controlling the redundancy and reconfigurability inherent in Cronus are needed to take advantage of the distributed system environment. These strategies for resource management are often conveniently separated into policies and mechanisms. A policy is a goal or guideline set by a system administrator or component designer constraining the decisions made by a resource allocator. A mechanism is an internal system structure designed to implement a class of policies.In the Cronus distributed system model, there are currently two general aspects of resource allocation mechanisms which are particular to the network environment and must be effectively managed. One of these is the binding of a client request to a particular resource manager for those resources which are available redundantly. Redundancy comes in two forms: replicated objects, (e.g., a multi-copy file) and replicated managers (any manager for a type can create a new instance of that object type). For both forms the selection of an object manager to provide the given service is an important resource management policy decision. The other important aspect of resource management mechanism is the ability to reconfigure parts of the system by dynamically migrating objects. Object migration is a powerful tool for matching system resources to tasks in a manner that attempts to maximize some measure of system performance, reliability, or survivability. Both static reconfiguration (e.g., choosing or amending the placement of object managers and their associated objects), and dynamic reconfiguration (e.g., moving an individual object in direct response to demand for its use possible in the Cronus architecture and design.The general approach to resource management in Cronus is to individually control the management of the classes of objects which make up the system. This approach extends Cronus resource management concepts beyond system resources to the abstract resources developed by applications. Resource management for an individual abstract resource (type) and for groups of related resource types (services) in Cronus is based on combining a number of carefully planned mechanisms which are part of the system architecture. In addition to resource management by resource type, a client specific policy which considers collections of object types used in a specific context can be constructed.In Cronus we achieve system wide and easily controllable resource management by requiring the object managers to cooperate in enforcing a resource management policy for their resource type. An object manager can redirect operations to a peer manager on another processor on the basis of current resource status. In the case for creation of a file to an alternate file manager which may have more storage available. Part of the basis for decisions to redirect requests are parameters, settable dynamically by system administrators through monitoring and control functions, which control the resource management strategy. The creation of objects and resource management in general thus becomes a responsibility that is decentralized among object managers on each processor based on a global allocation policy, both in terms of sharing current status and possible redirection of operations between managers.The Cronus resource management model is based on the integration of the following set of primitive mechanisms: the kernel routes requests for a given type to any available manager of that type, using the Locate mechanismCronus managers redirect requests to a more appropriate peer to accomplish resource management objectivesmanagers periodically collect current status of their peers, via a standard mechanism (ReportStatus), to be used as a basis for selecting a site for redirecting an operationusers and applications optionally indicate specific location preferences with requestsan operator can monitor and regulate the functioning of the resource management policyapplications can use the basic system mechanisms (e.g. broadcast or multicast, ReportStatus operations) to build special-purpose resource management strategies tailored to their needThere is a hierarchy of desirable locations for implementing resource management policies: object managers, shared libraries, and finally client programs or users themselves. Managers are the most desirable because they are readily identifiable with a limited set of object types which are likely to have similar policy goals. Also, managers are addressable as a group for administrative control to regulate the policy parameters, and for peer manager cooperation to implement the policy. Many decisions are negotiated between managers based upon information periodically obtained via the ReportStatus operation. The Cronus Monitoring and Control System uses the same ReportStatus operation to present the operator with a global view of how well the resource management policy is proceeding.In this paper we elaborate on these approaches to distributed resource management in Cronus, including appropriate background, detailed system design, and experiences with incorporating these ideas into three different resource managers.

Recently, there has been an increased reliance on computing systems supported by a multi-tier architecture. In multi-tier computing systems, it is important to appropriately manage resource allocation to ensure fairness of a QoS (quality of service) level avoiding overload conditions in tiers. This paper proposes an adaptive resource management algorithm for multi-tier computing systems in order that all clients have the same QoS level. We introduce a computing architecture which consists of multiple tiers, a group of resource managers, and an arbiter. Each tier is specialized to execute each subtask of clients and hosts virtual machines on its server pool. Each resource manager handles resource allocation of each client and updates the resources by locally exchanging a QoS level of its client with some other resource managers. Then, the resource managers request the resources to the arbiter. The arbiter compensates the requested resources to avoid overload conditions in tiers. Based on the compensation by the arbiter, each resource manager reallocates the resources to the subtasks of its client. We show sufficient conditions for the proposed resource management algorithm to achieve a fair QoS level avoiding overload conditions in all tiers at each time.

Managers of natural resources are faced with increasingly complex issues involving resource management concerns and the emerging importance of the socio-cultural context of resource use. The resource manager has been forced to respond and react to new social, cultural, ecological, and economic realities. Educational institutions have often been slow to respond to the changing work environment, even though a growing number of practitioners and educators believe that traditional approaches to training resource managers are inadequate and increasingly, ineffectual. This paper outlines the development of a new curriculum attempting to respond to the new challenges faced by contemporary resource managers. The concepts of interdisciplinary and cross-disciplinary study are described, and the need for a new educational approach is argued. Finally, one institution's response to the changing needs and skills of resource managers is reviewed.

Chapter 8 - Two-Phase Commit

The work described in this paper began with a desire to include some linguistic concept of a resource manager within a dataflow language we have been designing [AGP76]. In doing so, we discovered that dataflow monitors (resource managers) provide a natural way of thinking about resources and especially their scheduling. Dataflow semantics are based upon a program composed of asynchronous operators interconnected by lines along which data tokens (messages) flow, such that when all of the input tokens for a given operator have arrived then that operator may fire (execute) by absorbing the input tokens, computing, and producing an output token as its result. These operations closely match one's intuitive model of resource managers (operators) passing signals (tokens) to one another for the purpose of synchronizing and scheduling resource usage. Previously though, dataflow languages [D73, K73, W75] have dealt only with the expression of highly asynchronous yet determinate computations; however, resource management characteristically involves indeterminate computation. The introduction here of dataflow monitors and an explicit nondeterministic merge operator for dataflow streams makes dataflow very well suited for expressing interprocess communication and operations on resources.

GenAI's advanced natural language processing capabilities will revolutionize numerous areas, ranging from a paradigm shift in education to the economy. Along with the positive aspects of GenAI, ethical and social risks are also one of the negative aspects that attract attention in the literature. The purpose of this study is to test the role of mindset and GenAI literacy in university students' awareness of ethical and social risk with structural equation modeling. According to the research results, the developed awareness of ethical and social risk of generative artificial intelligence (GenAI) and the adapted GenAI literacy scale are valid and reliable. GenAI literacy variable does not have a significant effect on ethical and social risk awareness towards GenAI. Mindset should be taken into account in activities for both variables.

In the era of digital transformation, the integration of artificial intelligence within enterprise resource planning (ERP) systems is redefining financial management. This research explores the unprecedented application of generative AI modelssuch as transformer-based architectures-for real-time anomaly detection within SAP FICO modules. Unlike traditional rulebased or statistical approaches, generative AI can learn complex, dynamic financial patterns, enabling the proactive identification of irregularities in general ledger entries, accounts receivable, and accounts payable. This study proposes a novel framework that embeds generative AI directly into SAP FICO workflows, facilitating continuous monitoring, adaptive learning, and automated escalation of financial anomalies. The research also addresses data privacy, explainability, and integration challenges, offering a roadmap for organizations seeking to enhance their financial governance and compliance posture. Through simulation and case-based analysis, the paper demonstrates how this approach can reduce financial fraud, improve audit readiness, and drive operational efficiency-heralding a new era of intelligent financial oversight in SAP environments.

Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Generative AI in network intrusion detection, with the goal of developing a proactive and adaptable cyber protection mechanism. In this work the use of Generative AI use for identifying network intrusions, using the NSL-KDD dataset. The work begins with meticulous data preprocessing using handling missing values, encoding, feature scaling, and feature extraction to enhance the dataset and then focuses on using Generative AI for intrusion detection. In this work many models have been used like SVM, AE, DAE, and GANs, and in this GANs has the best accuracy with 91.12% compared with other models.

Multi-tenancy in modern datacenters is currently limited to a single latency-critical, interactive service, running alongside one or more low-priority, best-effort jobs. This limits the efficiency gains from multi-tenancy, especially as an increasing number of cloud applications are shifting from batch jobs to services with strict latency requirements. We present PARTIES, a QoS-aware resource manager that enables an arbitrary number of interactive, latency-critical services to share a physical node without QoS violations. PARTIES leverages a set of hardware and software resource partitioning mechanisms to adjust allocations dynamically at runtime, in a way that meets the QoS requirements of each co-scheduled workload, and maximizes throughput for the machine. We evaluate PARTIES on state-of-the-art server platforms across a set of diverse interactive services. Our results show that PARTIES improves throughput under QoS by 61% on average, compared to existing resource managers, and that the rate of improvement increases with the number of co-scheduled applications per physical host.

We show how to reason about distributed database management systems, in which a commit protocol is used to coordinate activity of several resource managers. This is an interesting case study of an open system, in which each component is developed independently to operate with many possible environments. We give specifications for each resource manager, and a specification for the commit protocol, and show that the whole system is correct as long as each component has the properties required of it. We then show how to prove that specific examples have these properties. >

Final Report for project DE-EE0006787: Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations. This project developed and tested an optical monitoring system prototype that will be suitable for marine and hydrokinetic (MHK) full project lifecycle observation (baseline, commissioning, and decommissioning), with automated real-time classification of marine animals. This system can be deployed to collect pre-installation baseline species observations at a proposed deployment site with minimal post-processing overhead. To satisfy deployed MHK project species of concern (e.g. Endangered Species Act-listed) monitoring requirements, the system provides automated tracking and notification of the presence of managed animals within established perimeters of MHK equipment and provides high resolution imagery of their behavior through a wide range of conditions. During a project’s decommissioning stage, the system can remain installed to provide resource managers with post-installation data. Our technology, known as an Unobtrusive Multi-static Serial LiDAR Imager (UMSLI), is a technology transfer of underwater distributed LiDAR imaging technology that preserves the advantages of traditional optical and acoustic solutions while overcoming associated disadvantages for MHK environmental monitoring applications. This new approach is a purposefully-designed, reconfigurable adaptation of an existing technology that can be easily mounted on or around different classes of MHK equipment. The system uses low average power red (638nm) laser illumination to be invisible and eye-safe to marine animals and is compact and cost effective. The equipment is designed for long term, maintenance-free operations, to inherently generate a sparse primary dataset that only includes detected anomalies (animal presence information), and to allow robust real-time automated animal classification/identification with a low data bandwidth requirement. Advantages of the technology over others currently being used or being considered for MHK monitoring include: Unlike a conventional camera, the depth of field is near-infinite and limited by attenuation (approximately 5-8 m) rather than focal properties of a lens; Operation in an adaptive mode which can project a sparse grid of pulses with higher peak power for longer range detection (>10 meters) and track animals within a zone of interest with high resolution imagery for identification of marine life at closer range (<5m); System detection limit and Signal-to-Noise-Ratio is superior to a camera, due to rejection of both backscattering component and ambient solar background; Multiple wide-angle pulsed laser illuminators and bucket detectors can be flexibly configured to cover a 4pi steradian (i.e. omnidirectional) scene volume, while also retrieving 3D features of animal targets from timing information; Process and classification framework centered around a novel active learning and incremental classification classifier that enables accurate identification of a variety of marine animals automatically; A two-tiered monitoring architecture and invisible watermarking-based data archiving and retrieving approach ensures significant data reduction while preserving high fidelity monitoring. A methodology to train and optimize the classifier for target species of concern to optimize site monitoring effectiveness. This technological innovation addresses a high priority regulatory requirement to observe marine life interaction near MHK projects. Our solution improves resource manager confidence that any interactions between marine animals and equipment are observed in a cost-effective and automated manner. Without EERE funding, this novel application of multi-static LiDAR would not have been available to the MHK community for environmental monitoring.

Background: Parkinson's disease was described for the first time by James Parkinson in 1817 in the trial "Shaking Palsy," and thus there is also evidence of this disease in the Indian medical system 4500 years ago, for the diagnosis and its management with Mucuna pruriens.Years later, it was subsequently determined to contain levodopa. Two types of manifestations of Parkinson's disease are currently known, such as motor and non-motor, the first being the one that usually leads to diagnosis. Success in this will depend on the skill of the primary care physician, the ability to recognize the first symptoms by the patient, and the health systems in the management of care for the timely referral. This work shows the comprehensive management of a patient who arrives at Primary Care services, presenting vague and specific symptoms. These symptoms were treated with medications or remedies in order to calm the condition temporarily. It is convenient to call the specific symptoms such as headache, nausea, pain, dizziness, tiredness and weakness, poor motivation, sadness, easy crying, and sleep disturbances; A large percentage go to their health centers for presenting motor alterations, many times identified by their relatives or by themselves and despite being recognized as something abnormal, many of them come when the tremor intervenes with the activities of daily life. At this point, the management of the disease would begin, making clear the importance of education for the population to attend abnormal situations on time and not in late stages, significantly improving the quality of life or the prognosis of the disease Parkinson's, as is the case that occurred in our institution; A 64-year-old female patient who presents with tremor in the fifth finger of the right hand, she mentions that it is more intense when there are apparently stress situations, hyposmia and sleep disturbances, going to different health centers where they were controlled the discomfort with essential medications, many of these not requiring a prescription, temporarily decreasing in intensity; This being the beginning of multi-causality for proper management from considering the first contact doctor as a resource manager with the use of diagnostic skills to recognize characteristic signs in early stages of the disease and the ability to maintain continuity with the patients and their families as a model of family system. Studies were determined that non-motor manifestations could appear months or years before they manifest as motor symptoms to give comprehensive management to patients from their first contact with the health centers closest to them. The diagnostic presumption and its referral to the neurology and timely treatment service, until evaluating the functionality and efficacy of health policies, avoiding the delay in pharmacological treatment and access to specific neuroimaging studies at any stage of the disease. Objective: The objective of this report is to present a clinical case of a patient diagnosed with Parkinson's disease, from its management in the first contact medical services to have comprehensive management by a neurologist, thus in this way the period of latency to start treatment. Methods: We present a clinical case of a 64-year-old patient who came to consultation due to a tremor in the fifth finger of the right hand.A review of her medical history is performed, and management by different specialties is identified, for mood disorders, insomnia, essential tremor in addition to allergic rhinitis, already with drug treatment with slight or no improvement. The patient underwent a neurological examination and imaging and laboratory studies. She was referred to the neurology service as soon as possible. Result: After the interrogation and physical examination, the patient was referred to the Neurology service to confirm the presumption of diagnosis as a movement disorder under study. This process is often called the "latency period" of the diagnosis. The present work is shown as a decisive factor in giving a verdict on this problem. It is known that this level of resolution takes into account the pillars of first contact medicine as health resource managers. In this way, pharmacological treatment was started with a decrease in tremor as a motor symptom and an increase in quality and amount of sleep, increased mood, and affect as non-motor symptoms. Conclusion: The patient is under established medical and pharmacological control, and the long diagnostic latency period could be evidenced, as it usually happens in many movement disorders or their early stages, in this case, Parkinson's disease. Likewise, the family doctor is an instrument that allows the resolution of more than 90% of health problems in general, and the proper management of the remaining percentage is multifactorial, as well as medical skill and experience, the capacity of the patient or the family members. in recognizing early-stage motor disorders and health systems that often make a referral to other medical specialties difficult.

Distribution network design is one of the most essential strategic decisions in logistics systems and supply chains, as it structures the network by determining the number of echelons and specifying the types, quantities, and locations of various facilities. This chapter presents decision-support models for distribution and logistics network design, covering both continuous location models (gravity location models) and discrete network design models (capacitated/uncapacitated and their relevant variants). Comprehensive numerical experiments based on the same case study offer a direct comparison of these different network design approaches. Furthermore, as one of the most disruptive technologies in Industry 5.0, the impact of generative AI (GenAI) and large language models (LLMs) on the research and education of distribution network design is thoroughly analyzed. Our experiments demonstrate that GenAI and LLMs are powerful tools in providing structured introductions to existing problems, guiding model selection and explanation, assisting with coding and code clarification, facilitating model comparison, and generating initial ideas for new research questions in distribution and logistics network design. However, they also face reliability and accuracy challenges, exhibit limited critical thinking capabilities, and struggle to address emerging research questions. Our findings offer valuable guidance for incorporating GenAI tools and LLMs into research and educational processes in distribution and logistics network design.

In this special issue, we explore the opportunities and challenges of using Generative AI (GenAI), in particular, text generators in higher education learning and teaching. As GenAI has become increasingly popular with many staff and students, this special issue provides an overview of the current state of the field and offers insights into future research. This introduction paper consists of four parts. It begins by providing an overview of AI and Generative AI, identifying the gap and framing the special issue relating to the gaps. The second part explores the opportunities and challenges of GenAI in higher education, as identified in the literature. The third part provides an overview of the papers included in the special issue. The final part is the self-reflection of the lead author. The special issue aims to serve as a valuable resource for higher education stakeholders, such as students, practitioners, researchers and managers. We hope this collection will help advance knowledge and future research, encourage innovation and inform evidence-based policy and practices in the field of Generative AI in higher education.

Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.