Energy cost optimization of globally distributed Internet Data Centers by copula-based multidimensional correlation modeling

Lasemi, Mohammad Ali; Alizadeh, Shahin; Assili, Mohsen; Yang, Zhenyu; Baboli, Payam Teimourzadeh; Arabkoohsar, Ahmad; Raeiszadeh, Amin; Brand, Michael; Lehnhoff, Sebastian

doi:10.1016/j.egyr.2022.12.033

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…For the w th with the renown of the m th moth, R m refers to the shape of the logarithmic spirals, which is quantitatively presented in Equation (6). Furthermore, the number of flames over the length of t iterations can be calculated using Equation (7).…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Because it does not rely on previous information, the robust module is superior to the stationary module in terms of both its adaptability and its effectiveness. In cloud computing, load balancing is seen as a multidimensional challenge of resource allocation that requires optimal utilization of resources without compromising the quality of service offered to customers [6][7][8][9][10]. This requires optimal utilization of resources to fulfill the demands of the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, load balancing turns into a crucial component that carefully distributes network traffic and computational workloads among several servers to maximize resource efficiency and avoid overloading any one node. The variety of hardware configurations, the unpredictable nature of workloads, and the critical requirement for reliability are a few of the issues that come with these operations [7]. It can be difficult to forecast and successfully manage resource requirements when dealing with dynamic workloads, which change in response to user demands and application requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Load balancing follows host selection. The immediate effect can maximize resource consumption, but it does not ensure task execution performance [7]. Heuristics methods, such as artificial bee colony (ABC) and ant colony optimization (ACO) [11] readily acknowledge this reality.…”

Section: Introductionmentioning

confidence: 99%

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Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Nauman,

Zhang,

et al. 2024

Applied Sciences

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Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

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Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Nauman,

Zhang,

et al. 2024

Applied Sciences

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“…In order to further explore the collaborative planning method of IDC and BESS, some scholars conducted researches on scenarios such as AC and DC power supply and fault operation conditions from the perspective of power grid operation reliability, and verified the advantages of collaborative planning of IDC and BESS in improving power system reliability [15][16][17]. [18][19] incorporated the cost of IDC and BESS into the planning objectives, and through collaborative planning, reduced the total planning cost of IDC and BESS, reduced the number of idle servers, and reduced the configuration requirements of BESS. Although the above researches provide sufficient theoretical basis for solving the problem of collaborative planning between IDC and BESS, the impact of BESS life loss cost is not considered.…”

Section: Introductionmentioning

confidence: 99%

Collaborative planning of data center and energy storage based on i-C&CG solving algorithm

2024

Journal of Electrical Systems

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With the rapid development of new power systems and advanced technologies such as artificial intelligence, the penetration rate of renewable energy is increasing, and the electricity consumption required for computing power calculation is surging. On the one hand, the uncertainty of wind power output has in-creasingly serious impact on the operation safety of power system. On the other hand, internet data center (IDC) needs to consume a lot of power to maintain the computing power system. Therefore, a two-stage robust collaborative programming model of IDC and BESS with life constraints is proposed herein. Plan storage capacity and data center server configuration with the goal of minimizing system operation and planning costs. An inexact column-and-constraint generation (i-C&CG) algorithm is proposed herein to solve the problem, and the IEEE-30 nodes system is simulated. The planning considering the life constraints of BESS are more reasonable, and i-C&CG algorithm can reduce the difficulty of solving two-stage robust planning problem of power system.

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