Energy optimized container placement for cloud data centers: a meta-heuristic approach

Containers are recognized for their lightweight and virtualization efficiency, making them a vital element in modern application orchestration. In this context, the scheduler is crucial in strategically distributing containers across diverse computing nodes. This paper presents a novel two-stage container scheduling solution that addresses node imbalances and efficiently deploys containers. The proposed solution formulates the scheduling process as an optimization problem, integrating various objective functions and constraints to enhance server consolidation and minimize energy consumption. The confidentiality of migrated containers is ensured through encryption, and the associated costs are incorporated into the optimization constraints. This approach ensures security in container scheduling, considering container attributes as input features in our proposed attributes-based encryption model. By carefully selecting containers and destination nodes, this work seeks to establish balance within cloud-based clusters. This contributes to the improvement of container orchestration systems and their effectiveness in real-world scenarios. The proposed solution’s efficacy is demonstrated in its ability to efficiently deploy containers in multi-data center cloud environments and seamlessly migrate them between hosts within the same data center or across different data centers. Our results show optimal consolidation with a reduction in the number of running hosts, ranging from 4% to over 18%. Additionally, the solution promotes minimal total power consumption with savings ranging from 3.5 to 16.25 megawatts, while also ensuring balanced server loads.

Optimized Encryption-Integrated Strategy for Containers Scheduling and Secure Migration in Multi-Cloud Data Centers

Cloud computing emerges as a paradigm that facilitates on-demand access to technological resources through the mechanism of service virtualization.This virtualization enables the partitioning of hardware resources among applications that are organized into distinct independent modules.The concept of microservice architecture takes advantage of virtualization capabilities to embrace a software architecture strategy focused on the development of applications as assemblies of several interdependent but loosely coupled modules.Nonetheless, the adoption of microservices architecture is accompanied by substantial energy demands to meet the desired standards of performance and availability.Existing research within the domain of microservices has explored various topics pertinent to energy consumption, including elasticity, reliability, performance, and availability.Yet, the diversity of challenges and solutions presents a complex landscape for identifying prevailing research trends and unaddressed gaps in the context of microservices.This study aims to methodically discern, evaluate, and juxtapose the existing research trends and voids concerning energy consumption within microservices.It elucidates a systematic review on the subject of energy consumption in microservices architectures, offering a compilation of references to facilitate more directed future investigations.The initial selection encompassed 3625 articles, which were subsequently narrowed down through three stages of refinement, resulting in 37 articles chosen for an exhaustive review.These selected studies were cataloged and analyzed based on various criteria, including metrics, evaluation methodologies, and architectural typologies, thus uncovering research gaps and emerging trends related to energy consumption in microservice architectures.Furthermore, this inquiry delineates significant research challenges and prospective directions, structured around the key metrics that underpin the reviewed studies: performance, elasticity, scalability, reliability, sustainability, and availability. 

Energy Consumption in Microservices Architectures: A Systematic Literature Review

/pdf/meta-heuristic-and-heuristic-algorithms-for-forecasting-2xi1qd8b.pdf

Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers

Several emerging non-volatile (NV) memory technologies are rising as interesting alternatives to build the Last-Level Cache (LLC). Their advantages, compared to SRAM memory, are higher density and lower static power, but write operations wear out the bitcells to the point of eventually losing their storage capacity. In this context, this paper presents a novel LLC organization designed to extend the lifetime of the NV data array and a procedure to forecast in detail the capacity and performance of such an NV-LLC over its lifetime. From a methodological point of view, although different approaches are used in the literature to analyze the degradation of an NV-LLC, none of them allows to study in detail its temporal evolution. In this sense, this work proposes a forecasting procedure that combines detailed simulation and prediction, allowing an accurate analysis of the impact of different cache control policies and mechanisms (replacement, wear-leveling, compression, etc.) on the temporal evolution of the indices of interest, such as the effective capacity of the NV-LLC or the system IPC. We also introduce L2C2, a LLC design intended for implementation in NV memory technology that combines fault tolerance, compression, and internal write wear leveling for the first time. Compression is not used to store more blocks and increase the hit rate, but to reduce the write rate and increase the lifetime during which the cache supports near-peak performance. In addition, to support byte loss without performance drop, L2C2 inherently allows N redundant bytes to be added to each cache entry. Thus, L2C2+N, the endurance-scaled version of L2C2, allows balancing the cost of redundant capacity with the benefit of longer lifetime. For instance, as a use case, we have implemented the L2C2 cache with STT-RAM technology. It has affordable hardware overheads compared to that of a baseline NV-LLC without compression in terms of area, latency and energy consumption, and increases up to 6-37 times the time in which 50% of the effective capacity is degraded, depending on the variability in the manufacturing process. Compared to L2C2, L2C2+6 which adds 6 bytes of redundant capacity per entry, that means 9.1% of storage overhead, can increase up to 1.4-4.3 times the time in which the system gets its initial peak performance degraded.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0278346&type=printable

L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime

Currently, the energy consumption in the data centers is a major problem due to the increase in the number of servers and services. Physical machines represent huge energy consumers who work continuously to ensure quality in services through virtual instances. The placement of these instances as containers in hosts has an impact on the minimization of energy consumption. Precisely, the guarantee of an efficient placement without wasting material resources of the hosts, makes it possible to minimize the number of active hosts and thus reduce energy consumption. In this article, we will calculate the energy consumed by a data center using CloudSim before and after applying the placement of containers in the hosts by the First Fit Decreasing algorithm (FFD) of Bin Packing and Ant Colony Optimization (ACO). The results show the FFD's superiority over the ACO in minimizing the energy consumed by any type of Cloud system.

Forecasting the Energy Consumption of Cloud Data Centers Based on Container Placement with Ant Colony Optimization and Bin Packing

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

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Forecasting the Energy Consumption of Cloud Data Centers Based on Container Placement with Ant Colony Optimization and Bin Packing

Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI