<i>StorNIR</i>
            , a multi-objective replica placement strategy for cloud federations

Chikhaoui, Amina; Lemarchand, Laurent; Boukhalfa, Kamel; Boukhobza, Jalil

doi:10.1145/3412841.3441886

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Table I shows the penalty function for each priority level, high P 0 and low P 1 [14]. γ 1 was set to 0.2.…”

Section: Evaluation a Methodologymentioning

confidence: 99%

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Investigating Multi-Tier and QoS-Aware Caching Based on ARC

Ait-Oucheggou,

Rubini,

Battou

et al. 2023

2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

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Memory caching is a common practice to reduce application latencies by buffering relevant data in high speed memory. When the volume of data to cache is too large or a DRAM-based solution too expensive, several technologies such as NVM or high speed SSDs could complement DRAM to form a multi-tier cache. Additionally, most existing policies focus on categorizing the data based on factors like recency and frequency, setting aside the fact that applications/customers have varying Quality-of-Service requirements. This concept is well established in Cloud environment with Service Level Agreement (SLA). In this paper, by extending the Adaptive Replacement Cache (ARC), that uses recency and frequency lists, we propose a QoSaware Multi-tier Adaptive Replacement Cache (QM-ARC) policy with the ability to take into account data applications/customers priorities through the concept of penalty borrowed from the Cloud. QM-ARC is generic, as it can be applied whatever the number of tiers and can accommodate different penalty functions. Using synthetic and real traces, our solution improved QoS as compared to state-of-the-art work.

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“…Table I shows the penalty function for each priority level, high P 0 and low P 1 [14]. γ 1 was set to 0.2.…”

Section: Evaluation a Methodologymentioning

confidence: 99%

“…Higher-priority data have higher penalties than low priority data when they undergo the same latency. We used the penalty concept from the Cloud and calculate it as in [14], [15] as follows (note that other functions could be used):…”

Section: B Considering Qosmentioning

confidence: 99%

Investigating Multi-Tier and QoS-Aware Caching Based on ARC

Ait-Oucheggou,

Rubini,

Battou

et al. 2023

2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

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“…Moreover, the use of EMO algorithms is becoming an emerging trend and has been widely adopted for resource management in decentralized architectures such as cloud, edge–fog computing, VFC, etc. EMO provides intelligent heuristics to solve a wide range of optimization problems such as VM allocation [ 20 ], data replica placement [ 35 ], federated clouds [ 36 ], among others. Studies that address the facility location in fog environments can be broadly categorized from numerous perspectives.…”

Section: Related Workmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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