Hierarchical Approach for Green Workload Management in Distributed Data Centers

Forestiero, Agostino; Mastroianni, Carlo; Meo, Michela; Papuzzo, Giuseppe; Sheikhalishahi, Mehdi

doi:10.1007/978-3-319-14325-5_28

Cited by 20 publications

(16 citation statements)

References 9 publications

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“…The scenario under analysis is the same as in [38,39], with four interconnected DCs and values of the PUE as reported in Table 1; time zones are also indicated with respect to UTC, assuming that the DC locations are, respectively, California, Ontario (Canada), the U.K. and Germany. Figure 4 reports energy prices in a 24-h interval; again, time is expressed in UTC.…”

Section: Resultsmentioning

confidence: 99%

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Laganá¹,

Mastroianni²,

Meo

et al. 2018

Algorithms

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The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.

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Section: Resultsmentioning

confidence: 99%

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Laganá¹,

Mastroianni²,

Meo

et al. 2018

Algorithms

Self Cite

View full text Add to dashboard Cite

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“…The geographical distribution of data, the emerging cloud-edge technology, heterogeneous data, and the efficient management of distributed data pose an important set of challenges even for large companies with a lot of resources at their disposal [18]. The enormous amount of workload must be processed, with the request to accomplish multiple often conflicting criteria, such as load balancing, cost reduction, overall execution time reduction, energy efficiency and green operation (reduction in the carbon pollution) [17,19]. These challenges must be tackled with the development of the new algorithms tailored to address multiple opposing goals while keeping in mind scalability as one of the priorities.…”

Section: Swarm Intelligence Applications In the Cloud Scheduling Domainmentioning

confidence: 99%

Modified firefly algorithm for workflow scheduling in cloud-edge environment

Bačanin

Živković

Bezdan

et al. 2022

Neural Comput & Applic

105

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Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

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“…Nonetheless, the authors of [19,20] addressed the challenges involved when the gateways are geographically dispersed on a large scale. In [19], the authors proposed a hierarchical structure, wherein the upper layer supervises the workload at data centers in the lower layer and triggers the migration of applications when necessary.…”

Section: Related Workmentioning

confidence: 99%

EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

et al. 2021

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With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

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Hierarchical Approach for Green Workload Management in Distributed Data Centers

Cited by 20 publications

References 9 publications

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Modified firefly algorithm for workflow scheduling in cloud-edge environment

EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

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