Efficient Heuristics for Profit Optimization of Virtual Cloud Brokers

Nesmachnow, Sergio; Iturriaga, Santiago; Dorronsoro, Bernabè

doi:10.1109/mci.2014.2369893

Cited by 35 publications

(11 citation statements)

References 27 publications

(38 reference statements)

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“…Nesmachnow, Iturriaga and Dorronsoro [30] propose a set of heuristics and a local search for allocation of users VM requests to instances reserved by a broker. Tordsson et al [39] solve the VM placement brokering problem using integer programming provided by the CPLEX solver 1 .…”

Section: Related Workmentioning

confidence: 99%

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Musiał

Kieffer

Guzek

et al. 2019

Foundations of Computing and Decision Sciences

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Cloud computing has become one of the major computing paradigms. Not only the number of offered cloud services has grown exponentially but also many different providers compete and propose very similar services. This situation should eventually be beneficial for the customers, but considering that these services slightly differ functionally and non-functionally -wise (e.g., performance, reliability, security), consumers may be confused and unable to make an optimal choice. The emergence of cloud service brokers addresses these issues. A broker gathers information about services from providers and about the needs and requirements of the customers, with the final goal of finding the best match. In this paper, we formalize and study a novel problem that arises in the area of cloud brokering. In its simplest form, brokering is a trivial assignment problem, but in more complex and realistic cases this does not longer hold. The novelty of the presented problem lies in considering services which can be sold in bundles. Bundling is a common business practice, in which a set of services is sold together for the lower price than the sum of services’ prices that are included in it. This work introduces a multi-criteria optimization problem which could help customers to determine the best IT solutions according to several criteria. The Cloud Brokering with Bundles (CBB) models the different IT packages (or bundles) found on the market while minimizing (maximizing) different criteria. A proof of complexity is given for the single-objective case and experiments have been conducted with a special case of two criteria: the first one being the cost and the second is artificially generated. We also designed and developed a benchmark generator, which is based on real data gathered from 19 cloud providers. The problem is solved using an exact optimizer relying on a dichotomic search method. The results show that the dichotomic search can be successfully applied for small instances corresponding to typical cloud-brokering use cases and returns results in terms of seconds. For larger problem instances, solving times are not prohibitive, and solutions could be obtained for large, corporate clients in terms of minutes.

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Section: Related Workmentioning

confidence: 99%

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Musiał

Kieffer

Guzek

et al. 2019

Foundations of Computing and Decision Sciences

View full text Add to dashboard Cite

show abstract

“…9 An alternative approach to solving NP-hard problems is to use problem-specific heuristics. 10 Another line of research focuses on the brokering market environment. In these works, the different actors in a brokering scenario are modeled as agents.…”

Section: Cloud Brokering Researchmentioning

confidence: 99%

Cloud Brokering: Current Practices and Upcoming Challenges

et al. 2015

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This invited article looks at the practical and legal implications of cloud brokering, in which cloud service brokers act as intermediaries between cloud service providers and customers.he International Organization for Standardization defi nes a cloud service broker (CSB) as a "cloud service partner that negotiates relationships between cloud service customers and cloud service providers." 1 A cloud service partner is further explained as a "party which is engaged in support of, or auxiliary to, activities of either the cloud service provider or the cloud service customer or both." The second type of partner described in the standard is the cloud auditor. In other words, cloud brokering encompasses a wide range of activities. Essentially, it includes all intermediaries that stand between a cloud service provider (CSP) and a cloud service customer (CSC). The negotiation of relationships is most often understood as a proposition of contract that's satisfying for both customers and providers. Sustainable broker business models must create added value to ensure that CSCs have real interest in using broker services.Motivations for using broker services vary. First, using these services might be more advantageous from an economical viewpoint: CSBs might offer better conditions to customers than CSPs. On the other hand, CSBs might create new channel and marketing opportunities for CSPs, resulting in a growth of sales.A CSB might also take care of additional customer demands. For example, the data sent to the cloud might be subject to special security or compliancy regulations, such as specifi c requirements for data location, encryption, or format. A CSB could select services that fulfi ll these demands. It might also select offers compatible with the other products and services currently used by the consumer, minimizing the time and costs of transitioning to a new cloud. The CSB's selection could also be motivated by additional aspects, including the trust, reputation, environment-awareness (for example, use of green energy), or social responsibility of CSPs.

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“…As the connecting link between infrastructure providers and customers, cloud service providers are of great importance [5]. Moreover, profits are the foundation of the normal operation of the cloud computing platform, which consist of the revenues from customers and the costs to infrastructure providers [6]. In this paper, we focus on the research of profit maximization of cloud service providers.…”

Section: Introductionmentioning

confidence: 99%

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

et al. 2020

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Cloud computing has attracting more and more attention for its flexibility and economic benefits. To maintain the supply-demand relationship among different participants in cloud computing environment, the exchange of value is the inner drive. From the perspective of cloud service provider, its primary concern is to earn the profit, which can be obtained by finishing the tasks published from customers. In this paper, we consider each task consists of numbers of sub-tasks in the logical order, each sub-task corresponds to a type of service requests, which can be served in unique multi-server system. On this basis, we propose a profit maximization problem in the multistage multi-server queue systems, in which customers are served at more than one stage, arranged in a series structure. Moreover, a deadline constraint is taken into consideration, which demonstrates the maximum tolerance degree that the customers can wait. Therefore, how to configure the parameters in multistage multi-server queue systems to maximize profit on the premise of reducing the waiting times of customers is a critical issue for cloud service provider. To address this problem, we first discuss the probability distribution function of the waiting time for single multi-server system and multistage multi-server queue systems respectively, and then propose a profit maximization model under the deadline constraint. Due to the complexity of this model, the analytical solution can hardly be obtained, we study a heuristic method to search for the optimal solution. At last, a series of numerical simulations are implemented to describe the performance of the proposed profit maximization scheme, the results show that not only the profit can be maximized, but also the waiting time of customers have been reduced effectively. INDEX TERMS Cloud computing, deadline, queueing model, multi-server system, profit maximization, waiting time.

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Efficient Heuristics for Profit Optimization of Virtual Cloud Brokers

Cited by 35 publications

References 27 publications

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Cloud Brokering: Current Practices and Upcoming Challenges

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

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