C loud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.
The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms.The first aim was achieved in two steps: adaptation of state-of-the-art multiobjective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.
In heterogeneous computing systems it is crucial to schedule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems performance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as operational costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related approaches, demonstrating their applicability for the considered problem and its good scalability.
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.
SUMMARYVirtualization is emerging as the prominent approach to mutualise the energy consumed by a single server running multiple Virtual Machines (VMs) instances. The efficient utilization of virtualized servers and/or computing resources requires understanding of the overheads in energy consumption and the throughput, especially on high-demanding High Performance Computing (HPC) platforms. In this paper, a novel holistic model for the power of virtualized computing nodes is proposed. Moreover, we create and validate instances of the proposed model using concrete measures taken during a benchmarking process that reflects an HPC usage, i.e. HPCC, IOZone and Bonnie++, conducted using two different hardware configurations on Grid5000 platform, based on Intel and AMD processors, and three widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi. The proposed holistic model of machine power takes into account the impact of utilisation metrics of the machine's components, as well as the employed application, virtualization, and hardware. The model is further derived using tools such as multiple linear regressions or neural networks that prove its elasticity, applicability and accuracy. The purpose of the model is to enable the estimation of energy consumption of virtualized platforms, aiming to make possible the optimization, scheduling or accounting in such systems, or their simulation.
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