Effects of storage heterogeneity in distributed cache systems

Reddy, Kota Srinivas; Moharir, Sharayu; Karamchandani, Nikhil

doi:10.23919/wiopt.2018.8362849

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Therefore, if the requirements from the system are read-intensive, then the use of distributed cache must be considered. On the other hand, write-intensive or read/write intensive applications do not require a distributed cache [31]. NoSQL databases [13] are known to mitigate the challenges associated with traditional databases.…”

Section: Nosql -A Solution For Big Data Storage Issuesmentioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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Section: Nosql -A Solution For Big Data Storage Issuesmentioning

confidence: 99%

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Khan¹,

Liu²,

Ali³

et al. 2023

Advances in Computers

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“…The deployment of an optimal caching system within a network infrastructure, up to the edge, is a very complex problem and remains an open issue [7]. Unlike the vast majority of existing work, we are not only interested in the spatial placement of caches in an infrastructure, but also in determining the optimal amount of cache to be placed at the different locations of the network, which makes the placement problem even harder.…”

Section: Introductionmentioning

confidence: 99%

A GRASP-Based Approach for Dynamic Cache Resources Placement in Future Networks

Ben-Ammar

Hadjadj‐Aoul

2020

J Netw Syst Manage

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Dealing with the ever-increasing video traffic is certainly one of the major challenges facing Internet Service Providers (ISPs). In this context, the strategic placement of caches is seen as one of the most important remedies, especially with recent advances in the field of virtualization. Unlike the existing works, which only focus on the placement issue, we also consider the problem of determining the optimal amount of cache to place at each possible location. We formalize, in this paper, the problem of caches placement as a multi-objective optimization problem, in which we minimize both the average distance from which contents are retrieved and the peering links utilization. As the proposed problem is NP-hard, we propose to solve it using the Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic. Simulations results reveal the quality of the obtained solutions compared to an exhaustive search method. At the same time, they reveal that the solution is not to put all resources at the edge or at the core, as some studies claim, but to partition them judiciously, which mainly depends on the objectives of the ISPs.

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“…Setting up an optimal caching system within a network infrastructure, up to the edge of the latter at the Multi-access Edge Computing (MEC) level, is very complex and remains an open issue in the literature [3]. In fact, if we want to optimize the use of caches, we will tend to put everything in the same place at the exit of the network (at Point of Presence (PoP) level), where users requests will be aggregated.…”

Section: Introductionmentioning

confidence: 99%

Efficiently allocating distributed caching resources in future smart networks

Ben-Ammar

Hadjadj‐Aoul

Ait-Chellouche

2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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During the last decade, Internet Service Providers (ISPs) infrastructure has undergone a major metamorphosis driven by new networking paradigms, namely: Software Defined Networks (SDN) and Network Function Virtualization (NFV). The upcoming advent of 5G will certainly represent an important achievement of this evolution. In this context, static (planning) or dynamic (on-demand) caching resources placement remains an open issue. In this paper, we propose a new technique to achieve the best trade-off between the centralization of resources and their distribution, through an efficient placement of caching resources. To do so, we model the cache resources allocation problem as a multi-objective optimization problem, which is solved using Greedy Randomized Adaptive Search Procedures (GRASP). The obtained results confirm the quality of the outcomes compared to an exhaustive search method and show how a cache allocation solution depends on the network's parameters and on the performance metrics that we want to optimize.

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Effects of storage heterogeneity in distributed cache systems

Cited by 7 publications

References 15 publications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

A GRASP-Based Approach for Dynamic Cache Resources Placement in Future Networks

Efficiently allocating distributed caching resources in future smart networks

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