Essays about: "Distributed scheduling"

Showing result 1 - 5 of 48 essays containing the words Distributed scheduling.

  1. 1. Optimization of the Cloud-Native Infrastructure using Artificial Intelligence

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Animesh Singh; [2023]
    Keywords : Artificial Intelligence; Test Channel Build; Test Channel Scheduling; Artificiell intelligens; Byggning av testkanal; Schemaläggning av testkanal;

    Abstract : To test Cloud RAN applications, such as the virtual distributed unit (vDU) and centralized virtual unit (vCU), a test environment is required, commonly known as a “test bed” or “test channel”. This test bed comprises various cloudnative infrastructures, including different hardware and software components. READ MORE

  3. 2. Optimizing the instruction scheduler of high-level synthesis tool

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Zihao Xu; [2023]
    Keywords : Instruction scheduling; Scheduling algorithm; CGRA; High-level Sythnesis; SiLago; Algorithm-level Synthesis; Constraint programming; Instruktion schemaläggning; schemaläggning algoritm; CGRA; High-level Sythnesis; SiLago; Algoritm-nivå Synthesis; Constraint programmering;

    Abstract : With the increasing complexity of the chip architecture design for meeting different application requirements, the corresponding instruction scheduler of high-level synthesis tool needs to solve complex scheduling problems. Dynamically Reconfigurable Resource Array (DRRA) is a novel architecture based on Coarse-Grained Reconfigurable Architecture (CGRA) on SiLago platform, the instruction scheduler of Vesyla-II, the dedicated High-Level Synthesis (HLS) tool targets for DRRA needs to schedule the specific instruction sets designed for Distributed Two-level Control System (D2LC). READ MORE

  4. 3. The relationship between schedule influence, schedule satisfaction and work-related outcomes within Swedish elder care

    University essay from Malmö universitet/Institutionen för Urbana Studier (US)

    Author : Josefin Björk; [2023]
    Keywords : elder care; schedule; scheduling; work-life;

    Abstract : The thesis aimed to investigate the relationship between influence over scheduling and schedule satisfaction with three central factors within the psychosocial work environment within the elder care sector: quality of work, work-life conflict and intention to quit. The JD-R model was used as theoretical approach as it provides a framework of the relationship between different work factors, characteristics, health and wellbeing, as well as a context to the different factors in the scope of the thesis. READ MORE

  5. 4. Computation Offloading for Real-Time Applications : Server Time Reservation for Periodic Tasks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Lizzy Tengana Hurtado; [2023]
    Keywords : Computation Offloading; Real-Time Applications; Resource Reservation; Beräkningsavlastning; realtidsapplikationer; resursreservation;

    Abstract : Edge computing is a distributed computing paradigm where computing resources are located physically closer to the data source compared to the traditional cloud computing paradigm. Edge computing enables computation offloading from resource-constrained devices to more powerful servers in the edge and cloud. READ MORE

  6. 5. Decentralized Learning over Wireless Networks with Imperfect and Constrained Communication : To broadcast, or not to broadcast, that is the question!

    University essay from Linköpings universitet/Kommunikationssystem

    Author : Martin Dahl; [2023]
    Keywords : Decentralized Stochastic Gradient Descent; Decentralized Learning; Medium Access Control; Wireless Communications; Machine Learning; Imperfect Communication; Resource-Constrained; Resource Allocation; Scheduling;

    Abstract : The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. READ MORE