Optimizing Resource Allocation for Virtualized Network Functions in a Cloud Center Using Genetic Algorithms

Rankothge, Windhya; Le, Franck; Russo, Alessandra; Lobo, Jorge

doi:10.1109/tnsm.2017.2686979

Cited by 139 publications

(71 citation statements)

References 25 publications

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“…This research follows this latter approach applying it to both the switching ON/OFF of VMs in a single data center (that is the main scope of [53]) and to the distribution of workload over multiple data centers. The techniques used to approach the resource allocation problem propose a plethora of heuristics ranging from Ant Colony Optimization [6] to Genetic Algorithms [63] or rely on hierarchical approaches [43], [57], [64] for scalability reasons. A different approach is to focus more on a mathematical formulation of the problem [52], [53], [60], [65], [66], aiming to a closed-form solution or relying on external solvers.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Ardagna

Ciavotta

Lancellotti

et al. 2021

IEEE Trans. Cloud Comput.

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Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.259% versus up to 17.245% of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.

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Section: Related Workmentioning

confidence: 99%

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Ardagna

Ciavotta

Lancellotti

et al. 2021

IEEE Trans. Cloud Comput.

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show abstract

“…Since the defender's objective utility function expressed in (7) corresponds to a nonlinear constraint problem, the optimal solution is extremely difficult to find. As a classic algorithm for searching for an approximately optimal solution [39], the genetic algorithm (GA) provides an alternative approach. GA is a stochastic global search and optimization method that mimics natural biological evolution.…”

Section: B Quantal Responsementioning

confidence: 99%

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Liu

et al. 2019

Mathematical Problems in Engineering

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Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a tradeoff between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.

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“…Optimizing algorithm module is implemented using two approaches: GA and ILS. The GA and ILS approaches are well known methods in the planning and scheduling context [3,4,5]. In this section, the limited work on scheduling and planning in the hospital management context will be discussed.…”

Section: Related Workmentioning

confidence: 99%

Experimental Study on an Efficient Dengue Disease Management System

Jayasuriya¹,

G.K.K.T.Galappaththi²,

Sampath³

et al. 2018

ijacsa

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Dengue has become a serious health hazard in Sri Lanka with the increasing cases and loss of human lives. It is necessary to develop an efficient dengue disease management system which could predict the dengue outbreaks, plan the countermeasures accordingly and allocate resources for the countermeasures. We have proposed a platform for Dengue disease management with following modules: (1) a prediction module to predict the dengue outbreak and (2) an optimization algorithm module to optimize hospital staff according to the predictions made on future dengue patient counts. This paper focuses on the optimization algorithm module. It has been developed based on two approaches: (1) Genetic Algorithm (GA) and (2) Iterated Local Search (ILS). We are presenting the performances of our optimization algorithm module with a comparison of the two approaches. Our results show that the GA approach is much more efficient and faster than the ILS approach.

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Optimizing Resource Allocation for Virtualized Network Functions in a Cloud Center Using Genetic Algorithms

Cited by 139 publications

References 25 publications

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Experimental Study on an Efficient Dengue Disease Management System

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