scholarly journals A Survey of Resource Management for Processing-In-Memory and Near-Memory Processing Architectures

2020 ◽  
Vol 10 (4) ◽  
pp. 30
Author(s):  
Kamil Khan ◽  
Sudeep Pasricha ◽  
Ryan Gary Kim
Keyword(s):  
Resource Management ◽  
Random Access ◽  
Computation Offloading ◽  
New Paradigm ◽  
Data Movement ◽  
Memory Processing ◽  
Big Data Applications ◽  
Challenges And Opportunities ◽  
Management Techniques ◽  
Access Patterns

Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management.

scholarly journals Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments

2019 ◽  
pp. 1-23 ◽  
Author(s):  
Muhammed Tawfiqul Islam ◽  
Rajkumar Buyya
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Data Processing ◽  
Job Scheduling ◽  
Big Data Processing ◽  
Big Data Applications ◽  
Art Practices ◽  
Core Components ◽  
Management Techniques ◽  
Computing Environments

This chapter presents software architectures of the big data processing platforms. It also provides in-depth knowledge on resource management techniques involved while deploying big data processing systems in the cloud environment. It starts from the very basics and gradually introduce the core components of resource management which are divided into multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.

scholarly journals Implications of NVM Based Storage on Memory Subsystem Management

2020 ◽  
Vol 10 (3) ◽  
pp. 999
Author(s):  
Hyokyung Bahn ◽  
Kyungwoon Cho
Keyword(s):  
Random Access ◽  
Disk Drive ◽  
Main Memory ◽  
Memory Storage ◽  
Storage Device ◽  
Storage Devices ◽  
Large Memory ◽  
Memory Subsystems ◽  
Non Volatile Memory ◽  
Management Techniques

Recently, non-volatile memory (NVM) has advanced as a fast storage medium, and legacy memory subsystems optimized for DRAM (dynamic random access memory) and HDD (hard disk drive) hierarchies need to be revisited. In this article, we explore the memory subsystems that use NVM as an underlying storage device and discuss the challenges and implications of such systems. As storage performance becomes close to DRAM performance, existing memory configurations and I/O (input/output) mechanisms should be reassessed. This article explores the performance of systems with NVM based storage emulated by the RAMDisk under various configurations. Through our measurement study, we make the following findings. (1) We can decrease the main memory size without performance penalties when NVM storage is adopted instead of HDD. (2) For buffer caching to be effective, judicious management techniques like admission control are necessary. (3) Prefetching is not effective in NVM storage. (4) The effect of synchronous I/O and direct I/O in NVM storage is less significant than that in HDD storage. (5) Performance degradation due to the contention of multi-threads is less severe in NVM based storage than in HDD. Based on these observations, we discuss a new PC configuration consisting of small memory and fast storage in comparison with a traditional PC consisting of large memory and slow storage. We show that this new memory-storage configuration can be an alternative solution for ever-growing memory demands and the limited density of DRAM memory. We anticipate that our results will provide directions in system software development in the presence of ever-faster storage devices.

Secrecy-Driven Resource Management for Vehicular Computation Offloading Networks

IEEE Network ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 84-91 ◽  
Author(s):  
Yuan Wu ◽  
Li Ping Qian ◽  
Haowei Mao ◽  
Xiaowei Yang ◽  
Haibo Zhou ◽  
...  
Keyword(s):  
Resource Management ◽  
Computation Offloading

Mobile Cloud Resource Management

2016 ◽  
pp. 1747-1773
Author(s):  
Konstantinos Katzis
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Mobile Cloud Computing ◽  
Finite Energy ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Seamless Integration ◽  
Cloud Resource Management ◽  
Cloud Resource ◽  
Management Techniques

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.

Global Information Systems and Human Resource Management

2011 ◽  
pp. 30-43
Author(s):  
Fred Niederman
Keyword(s):  
Information Technology ◽  
Information Systems ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Information Technologies ◽  
Global Organizations ◽  
Human Resource Strategy ◽  
Information Systems Professionals ◽  
Management Techniques

A socio-technical approach to information systems requires recognition of the inextricable link between information technologies and humans as designers and users. This essay explores five areas in which information technology and human computer designers/users interact within the context of global organizations. These five areas are: using information technology to support the human resource strategy of global organizations, using information technology to support the generation and distribution of organizational learning, using human resource management techniques and programs to support the work of information systems professionals, using human resource management techniques and programs to support the work of global “end-users” or knowledge workers, and, finally, national and regional policies to support technical and human resource infrastructures.

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