354 research outputs found

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

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    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

    A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

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    This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable Computin

    Machine Learning Centered Energy Optimization In Cloud Computing: A Review

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    The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing

    Hybrid Computing for Interactive Datacenter Applications

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    Field-Programmable Gate Arrays (FPGAs) are more energy efficient and cost effective than CPUs for a wide variety of datacenter applications. Yet, for latency-sensitive and bursty workloads, this advantage can be difficult to harness due to high FPGA spin-up costs. We propose that a hybrid FPGA and CPU computing framework can harness the energy efficiency benefits of FPGAs for such workloads at reasonable cost. Our key insight is to use FPGAs for stable-state workload and CPUs for short-term workload bursts. Using this insight, we design Spork, a lightweight hybrid scheduler that can realize these energy efficiency and cost benefits in practice. Depending on the desired objective, Spork can trade off energy efficiency for cost reduction and vice versa. It is parameterized with key differences between FPGAs and CPUs in terms of power draw, performance, cost, and spin-up latency. We vary this parameter space and analyze various application and worker configurations on production and synthetic traces. Our evaluation of cloud workloads shows that energy-optimized Spork is not only more energy efficient but it is also cheaper than homogeneous platforms--for short application requests with tight deadlines, it is 1.53x more energy efficient and 2.14x cheaper than using only FPGAs. Relative to an idealized version of an existing cost-optimized hybrid scheduler, energy-optimized Spork provides 1.2-2.4x higher energy efficiency at comparable cost, while cost-optimized Spork provides 1.1-2x higher energy efficiency at 1.06-1.2x lower cost.Comment: 13 page

    Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

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    Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black-boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters. We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.Comment: Accepted by 2023 IEEE International Parallel & Distributed Processing Symposium (IPDPS

    Task scheduling mechanisms for fog computing: A systematic survey

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    In the Internet of Things (IoT) ecosystem, some processing is done near data production sites at higher speeds without the need for high bandwidth by combining Fog Computing (FC) and cloud computing. Fog computing offers advantages for real-time systems that require high speed internet connectivity. Due to the limited resources of fog nodes, one of the most important challenges of FC is to meet dynamic needs in real-time. Therefore, one of the issues in the fog environment is the optimal assignment of tasks to fog nodes. An efficient scheduling algorithm should reduce various qualitative parameters such as cost and energy consumption, taking into account the heterogeneity of fog nodes and the commitment to perform tasks within their deadlines. This study provides a detailed taxonomy to gain a better understanding of the research issues and distinguishes important challenges in existing work. Therefore, a systematic overview of existing task scheduling techniques for cloud-fog environment, as well as their benefits and drawbacks, is presented in this article. Four main categories are introduced to study these techniques, including machine learning-based, heuristic-based, metaheuristic-based, and deterministic mechanisms. A number of papers are studied in each category. This survey also compares different task scheduling techniques in terms of execution time, resource utilization, delay, network bandwidth, energy consumption, execution deadline, response time, cost, uncertainty, and complexity. The outcomes revealed that 38% of the scheduling algorithms use metaheuristic-based mechanisms, 30% use heuristic-based, 23% use machine learning algorithms, and the other 9% use deterministic methods. The energy consumption is the most significant parameter addressed in most articles with a share of 19%. Finally, a number of important areas for improving the task scheduling methods in the FC in the future are presented

    A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

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    Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033

    IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment

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    peer reviewedGiven the increase diversity of smart devices and objectives of the application management such as energy consumption, makespan users expect their requests to be responded to in an appropriate computation environment as properly as possible. In this paper, a method of workflow scheduling based on the fog-cloud architecture has been designed given the high processing capability of the cloud and the close communication between the user and the fog computing node, which reduces delay in response. We also seek to minimize consumption and reduce energy use and monetary cost in order to maximize customer satisfaction with proper scheduling. Given the large number of variables that are used in workflow scheduling and the optimization of contradictory objectives, the problem is NP-hard, and the multi-objective metaheuristic krill herd algorithm is used to solve it. The initial population is generated in a smart fashion to allow fast convergence of the algorithm. For allocation of tasks to the available fog-cloud resources, the EFT (earliest finish time) technique is used, and resource voltage and frequency are assumed to be dynamic to reduce energy use. A comprehensive simulation has been made for assessment of the proposed method in different scenarios with various values of CCR. The simulation results indicate that makespan exhibits improvements by 9.9, 8.7% and 6.7% on average compared with respect to the methods of IHEFT, HEFT and IWO-CA, respectively. Moreover, the monetary cost of the method and energy use have simultaneously decreased in the fog-cloud environment

    Bandwidth Allocation Algorithm for Makespan Optimization in a Fog-Cloud Environment: Monitoring Application

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    Fog computing technology has emerged to handle a large amount of data generated by the Internet of Things (IoT) terminals and cope with latency-sensitive application requests by allocating computation and storage resources at the edge of the Internet. In many IoT applications, the data acquisition procedures must apply the Directed Acyclic Graph (DAG) to get real-time results. The principal goal of DAG scheduling is to reduce total completion time without breaking priority constraints by properly allocating tasks to processors and arranging task execution sequencing. In this paper, we propose a bandwidth-aware workflow allocation (BW-AWA) that schedules tasks by priority to the resource and optimizes the total execution time (Makespan) in the entire computing system. The task allocation process needs to consider the dependency between tasks. The proposed approach is tested with a monitoring application case study, and the results are compared to well-known approaches to demonstrate its effectiveness in optimizing the Makespan
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