OMBM: optimized memory bandwidth management for ensuring QoS and high server utilization

Sung, Hanul; Min, Jeesoo; Ha, Sujin; Eom, Hyeonsang

doi:10.1007/s10586-018-2828-1

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…Challenge 3-Reliability in critical applications: In order to support mission-critical applications in the IoV (e.g., first responder communication and transportation systems), services with low communication latency and high reliability are required [47][48][49]. These latency-sensitive applications have propagation lengths that range from short to medium.…”

Section: Challenges Of Iov-assisted Smart Gridmentioning

confidence: 99%

Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations

et al. 2021

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Blockchain technology is highly coupled with cryptocurrencies; however, it provides several other potential use cases, related to energy and sustainability, Internet of Things (IoT), smart cities, smart mobility and more. Blockchain can offer security for Electric Vehicle (EV) transactions in the Internet of Vehicles (IoV) concept, allowing electricity trading to be performed in a decentralized, transparent and secure way. Additionally, blockchain provides the necessary functionalities for IoV decentralized application development, such as data exchange, personal digital identity, sharing economy and optimized charging pattern. Moreover, blockchain technology has the potential to significantly increase energy efficiency, decrease management costs and guarantee the effective use of the energy recourses. Therefore, its application in the IoV concept provides secure, autonomous and automated energy trading between EVs. While several studies on blockchain technology in smart grids have been conducted, insufficient attention has been given to conducting a detailed review and state-of-the-art analysis of blockchain application in the IoV domain. To this end, this work provides a systematic literature review of blockchain-based applications in the IoV domain. The aim is to investigate the current challenges of IoV and to highlight how blockchain characteristics can contribute to this emerging paradigm. In addition, limitations and future research directions related to the integration of blockchain technology within the IoV are discussed. To this end, this study incorporates the theoretical foundations of several research articles published in scientific publications over the previous five years, as a method of simplifying our assessment and capturing the ever-expanding blockchain area. We present a comprehensive taxonomy of blockchain-enabled applications in the IoV domain, such as privacy and security, data protection and management, vehicle management, charging optimization and P2P energy trading, based on a structured, systematic review and content analysis of the discovered literature, and we identify key trends and emerging areas for research. The contribution of this article is two-fold: (a) we highlight the limitations presented in the relevant literature, particularly the barriers of blockchain technology and how they influence its integration into the IoV and (b) we present a number of research gaps and suggest future exploratory areas.

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Section: Challenges Of Iov-assisted Smart Gridmentioning

confidence: 99%

Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations

et al. 2021

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“…Contention for lower levels of cache and With compute-memory performance gap, allocating larger cache slices and higher memory bandwidth significantly enhances performance bound QoS metrics (Sung et al, 2017). Concurrent applications with diverse memory access patterns, compute-memory phases and intensities and cache utilization is a major reason for application slow down and QoS degradation (Tang et al, 2011;Subramanian et al, 2015).…”

Section: Memorymentioning

confidence: 99%

“…Cache partitioning to provide either larger or at least sufficient enough last level cache slices is a common approach to meet QoS requirements of latency critical applications (Kasture and Sanchez, 2014;Iyer et al, 2007). Identifying application/thread priority and scaling cache allocation accordingly, following utilitarian principles is another strategy to improve overall throughput metrics (Herdrich et al, 2016;Sharifi et al, 2011;Sung et al, 2017;Guo et al, 2007a). Optimizing for memory controller proximity reduces shared resource pressure further and provides predictable QoS guarantees (Beckmann et al, 2015;Subramanian et al, 2015).…”

Section: Memorymentioning

confidence: 99%

On-Chip Dynamic Resource Management

Miele

Kanduri²,

Moazzemi³

et al. 2019

FNT in Electronic Design Automation

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The need for dynamic resource management has shadowed the exponential growth of on-chip transistor capacity, and the challenge is accentuated by the heterogeneity of resources, and the bewildering variety of constraints and requirements of applications, platforms and users. The field has started with a few research papers in the early 1990s but has grown today to over hundred yearly publications, leading to an accumulated body of literature presumable far above 1000 papers. We focus on the dynamic (run-time) management of onchip resources and mostly ignore design-time techniques and off-chip resources of larger electronic systems. Moreover, we do not attempt a complete review of all published work on the topic. Rather, this survey provides a structured review and discussion of the state of the art and is divided along the primary objectives of resource management techniques: performance, power, reliability and quality of service, each

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“…All the provisioning techniques prioritize applications based on QoS requirements and dynamically adapt further by monitoring resource utilization upon provisioning. Memory and Storage: With the widening compute-memory performance gap, allocating larger cache slices and higher memory bandwidth significantly enhances performance bound QoS metrics [36] [37] [38]. Using cache partitioning to provide either larger/sufficient cache slices is a common approach to meet QoS requirements of latency critical applications [39] [40].…”

Section: Qosmentioning

confidence: 99%

“…Using cache partitioning to provide either larger/sufficient cache slices is a common approach to meet QoS requirements of latency critical applications [39] [40]. Identifying application/thread priority and scaling cache allocation accordingly, following utilitarian principles is another strategy to improve overall throughput metrics [41] [42] [36]. Optimizing for memory controller proximity [43] [38] and allocating higher bandwidth can enhance QoS of memory intensive applications [44] [45].…”

Section: Qosmentioning

confidence: 99%

Trends in On-chip Dynamic Resource Management

Moazzemi¹,

Kanduri²,

Juhász³

et al. 2018

2018 21st Euromicro Conference on Digital System Design (DSD)

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The Complexity of emerging multi/many-core architectures and diversity of modern workloads demands coordinated dynamic resource management methods. We introduce a classification for these methods capturing the utilized resources and metrics. In this work, we use this classification to survey the key efforts in dynamic resource management.We first cover heuristic and optimization methods used to manage resources such as power, energy, temperature, Qualityof-Service (QoS) and reliability of the system. We then identify some of the machine learning based methods used in tuning architectural parameters in computer systems. In many cases, resource managers need to enforce design constraints during runtime with a certain level of guarantee. Hence, we also study the trend in deploying formal control theoretic approaches in order to achieve efficient and robust dynamic resource management.

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OMBM: optimized memory bandwidth management for ensuring QoS and high server utilization

Cited by 4 publications

References 28 publications

Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations

Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations

On-Chip Dynamic Resource Management

Trends in On-chip Dynamic Resource Management

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