Continuous memory allocation model for cloud services

Alyatama, Anwar; Alsumait, Asmaa; Alotaibi, Maryam M.

doi:10.1007/s11227-018-2455-x

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…For NF applications, TLB misses would result in nondeterministic performance characteristics in terms of overall packet throughput and latencies. To avoid TLB misses, large contiguous memory regions can be allocated to NF applications by allocating a large page (≫ 4 Kbyte) from the OS [101]. With a large page, the page index mapping is available with high probability as cached entity in the TLB.…”

Section: B Memory Management 1) Memory Allocation A: Contiguous Memor...mentioning

confidence: 99%

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

et al. 2022

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Scalable and flexible communication networks increasingly conduct the packet processing for Network Functions (NFs) in General Purpose Computing (GPC) platforms. The input/output (I/O)-intensive and latency-sensitive packet processing is challenging for the operating systems and hypervisors running on GPC platforms. This article surveys the existing enabling technologies and research studies on operating system and hypervisor aspects that directly influence the packet processing for NFs on GPC platforms. We organize this survey according to the main categories abstraction approach, memory access, and I/O strategy. We further categorize abstraction approach technologies and research studies into the categories operation systems, hypervisors, and containers. We partition the memory access category into the two subcategories of memory allocation and memory access, while we partition the I/O strategy category into the sub-categories I/O device virtualization and I/O device access. Our survey gives a comprehensive summary of the capabilities and limitations of the existing enabling technologies and researched approaches for abstraction, memory access, and I/O for NF packet processing. We outline critical future research directions for advancing NF packet processing on GPC platforms.

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Section: B Memory Management 1) Memory Allocation A: Contiguous Memor...mentioning

confidence: 99%

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

et al. 2022

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“…An algorithm presented in [82] divides the memory into multiple zones, shown in Figure 18(c), where a subgroup of relative request sizes compete in reverse order. In addition to this algorithm, [83] proposes a cloud computing service that provides a continuous memory size. A mathematical and an approximation model for different request size distributions, traffic intensities, memory sizes, and granularity values allow calculating optimal quantised sizes.…”

Section: Memory Allocationmentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

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The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail. INDEX TERMS Clouds, high performance computing, memory management, resource management. ANNA PUPYKINA received the higher education degree in data processing and control systems engineering from Samara State Aerospace University, Russia, in 2009. She is currently pursuing the Ph.D. degree with the Politecnico di Milano, under the supervision of Prof. G. Agosta. She was with Russian State Humanitarian University (RSUH), Togliatti, and also with the Department of Applied Mathematics and Informatics, Togliatti State University (TSU). Her main research interests include runtime management algorithms and programming model extensions for deeply heterogeneous systems. GIOVANNI AGOSTA received the Laurea degree in computer engineering and the Ph.D. degree in information technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. He is currently a tenured Researcher. He has authored more than 80 articles in international journals, conferences, and workshops. His main research interest includes compiler technology. He was a recipient of four HiPEAC Awards and three Best Paper Awards. He has been involved in eight European funded projects, since 2010, holding workpackage and task leader responsibilities. He is a Full Member of the HiPEAC Network and the Organizer of two workshops colocated with the HiPEAC Conference.

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