A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems

A cyber-physical system (CPS) employs tight integration of, and coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS

Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic Thermal and Power Management: From Computers to Buildings

Thermal and power management have become increasingly important for both computing and physical systems. Computing systems from real-time embedded systems to data centers require effective thermal and power management to prevent overheating and save energy. In the mean time, as a major consumer of energy buildings face challenges to reduce the energy consumption for air conditioning while maintaining comfort of occupants. In this dissertation we investigate dynamic thermal and power management for computer systems and buildings. (1) We present thermal control under utilization bound (TCUB), a novel control-theoretic thermal management algorithm designed for single core real-time embedded systems. A salient feature of TCUB is to maintain both desired processor temperature and real-time performance. (2) To address unique challenges posed by multicore processors, we develop the real-time multicore thermal control (RT-MTC) algorithm. RT-MTC employs a feedback control loop to enforce the desired temperature and CPU utilization of the multicore platform via dynamic frequency and voltage scaling. (3) We research dynamic thermal management for real-time services running on server clusters. We develop the control-theoretic thermal balancing (CTB) to dynamically balance temperature of servers via distributing clients\u27 service requests to servers. Next, (4) we propose CloudPowerCap, a power cap management system for virtualized cloud computing infrastructure. The novelty of CloudPowerCap lies in an integrated approach to coordinate power budget management and resource management in a cloud computing environment. Finally we expand our research to physical environment by exploring several fundamental problems of thermal and power management on buildings. We analyze spatial and temporal data acquired from an real-world auditorium instrumented by a multi-modal sensor network. We propose a data mining technique to determine the appropriate number and location of temperature sensors for estimating the spatiotemporal temperature distribution of the auditorium. Furthermore, we explore the potential energy savings that can be achieved through occupancy-based HVAC scheduling based on real occupancy data of the auditorium

Network and Server Resource Management Strategies for Data Centre Infrastructures: A Survey

The advent of virtualisation and the increasing demand for outsourced, elastic compute charged on a pay-as-you-use basis has stimulated the development of large-scale Cloud Data Centres (DCs) housing tens of thousands of computer clusters. Of the signi�cant capital outlay required for building and operating such infrastructures, server and network equipment account for 45% and 15% of the total cost, respectively, making resource utilisation e�ciency paramount in order to increase the operators' Return-on-Investment (RoI). In this paper, we present an extensive survey on the management of server and network resources over virtualised Cloud DC infrastructures, highlighting key concepts and results, and critically discussing their limitations and implications for future research opportunities. We highlight the need for and bene �ts of adaptive resource provisioning that alleviates reliance on static utilisation prediction models and exploits direct measurement of resource utilisation on servers and network nodes. Coupling such distributed measurement with logically-centralised Software De�ned Networking (SDN) principles, we subsequently discuss the challenges and opportunities for converged resource management over converged ICT environments, through unifying control loops to globally orchestrate adaptive and load-sensitive resource provisioning

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected