Service Oriented Resilience Strategy for Cloud Data Center

As active-active data center has sprung up since the popularization of cloud computing services, it keeps on a flourishing development due to the advanced active-active mechanism. In order to provide high-quality service to customers, the service reliability based IT infrastructure that delivers the service and implements the active-active mechanism is a key concern. In order to evaluate and further improve the service reliability of the cloud data center under active-active mechanism, modeling is absolutely necessary but complex. In this paper, a novel colored generalized stochastic petri net (CGSPN) model based on IT infrastructures is build up, which clearly reflect the dynamic behavior and service request processing procedure under the active-active mechanism. During the modeling, the hierarchical modeling method is applied. The outline net illustrates the processing procedure and the subnets consider the failure and resource usage of each node. Afterwards, the simulation is carried out through CPN Tools and applied in a use case, which verifies the validity of our approach.

Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service (QoS). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem (LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the QoS among tenants, and improves tenants' overall QoS in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients’ tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.

This research is an effort to develop a Risk Management‐based Decision Analysis (RMDA) framework based on the common fundamental elements that define the nature of resilience in Maritime Infrastructure and Transportation Systems (MITS). The framework develops a systemic approach to the decision making process in regards to investing on resilience strategies. It also enables stakeholders and decision makers of maritime systems to identify, analyze, and prioritize risks involved in MITS operations; to define ways for risk mitigation, plan for contingencies, and devise mechanisms for continuously monitoring and controlling risk factors and threats to the system; and to value the adopted resilience investment plans and strategies. Our suggested RMDA framework is developed based on the Six‐element Flexibility Framework, which is offered for assessing the value of flexibility strategies in Space Systems, and utilizes a Decision Tree Analysis (DTA) methodology for assessing the cost‐effectiveness of the devised strategies.

Sustainable resilience of hydrogen energy system

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

Nowadays, Cloud Service Providers (CSPs) provide flexible architectures and reliable internet accessories in cloud datacenters, where different types of applications are hosted. In order to achieve higher resource utilization and better quality of service, virtualization is widely used and implemented with virtual machines (VMs). However, most studies focus on VM management for utilization and energy, not yet considering the cost for emerging internet applications. Therefore, to further reduce network cost, we propose utilization-and-network-driven (UND) and only-network-driven (OND) strategies on top of the common minimal-migration (MM) based VM management as UND-MM and OND-MM. According to simulation results, both UND-MM and OND-MM strategies effectively reduce network cost by averagely 7% and 37%, respectively, of the original MM while retaining comparable resource utilization in CPU and memory for normally-loaded and overloaded datacenters. Moreover, the OND-MM, which is simpler, outperforms the UND-MM, and thus becomes more preferable for VM management in cloud datacenters.

Stochastic Petri Net-based performance evaluation of hybrid traffic for social networks system

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual-based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. The appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value.

The article examines the importance of container migration within the context of container placement tasks in cloud data centers. Cloud computing offers organizations significant benefits such as flexibility, scalability, and reduced costs for equipment, energy, and maintenance. Cloud providers face the challenge of not only efficiently placing containers initially but also dynamically reallocating them, along with virtual machines, in response to changing demands and workloads. This necessitates the development and application of migration algorithms. The study analyzes how migration is a critical component for ensuring optimal resource use, maintaining high service availability, and reducing service level agreement (SLA) violations. The research aims to enhance the productivity of cloud data centers in the task of container placement by utilizing container mi-gration algorithms. This objective is driven by the need for cloud providers to use their resources effectively to provide higher quality service and maintain competitive pricing. The use of migrations prevents overloading or underloading of servers and virtual machines, which is especially impor-tant in the highly dynamic environment of cloud computing. The research developed a hybrid mi-gration algorithm that includes the migration of both containers and virtual machines. Mathemati-cal aspects of the container placement task are described, and the migration algorithm is detailed. Experiments showed that implementing this algorithm reduces operational costs by 24% compared to traditional methods that do not use migration, and by 9% compared to less flexible migration strategies. There was also a demonstrated reduction in SLA violation times by 40%, indicating im-provements in service quality and reliability of cloud services. The conclusions of this study confirm that container and virtual machine migration is necessary for effective management of dynamic re-sources in cloud data centers in the context of container placement tasks.

The Performability Manager (PM) is a distributed system component which maintains the application-requested Quality of Service (QoS) by dynamically reconfiguring ANSAware-based distributed applications, using a model-based optimization procedure. The PM receives information about the ANSAware-based application from a distributed monitoring process based on JEWEL and DEMON. With this information, and using predefined stochastic Petri net (SPN) models of ANSAware applications, the PM automatically constructs an overall SPN performability model which is subsequently used for the determination of the provided QoS. Based on the analysis results, the PM can decide to initiate on-line system reconfigurations, if such is needed for maintaining the requested QoS. ANSAware provides facilities for these dynamic reconfigurations.

Cloud record facilities provide flexible and scalable surroundings for executing loads of workloads. However, efficient useful resource allocation is crucial for the effective transport of cloud offerings, making sure they're fee-efficient and keep Quality of Service (QoS). Multi-Objective Scheduling Algorithms (MOSAs) have emerged as a promising solution to balance performance, value, and QoS metrics in cloud environments. This study investigates the effectiveness of numerous MOSAs for optimizing useful resource allocation in cloud workloads. We behavior a comparative has a look at of several algorithms, assessing their overall performance, fee implications, and QoS metrics across various cloud situations. Our method includes a quantitative analysis of execution time and fee, along with a qualitative assessment of QoS attributes inclusive of availability, reliability, and response time. Additionally, we discover how wonderful workload traits along with batch processing, real-time processing, and fact-intensive analytics impact the performance of those algorithms. The findings will provide valuable insights into the strengths and weaknesses of various scheduling algorithms and inform the future layout of greater effective cloud scheduling structures

Introduction: Data traffic in multiplayer games consists of realtime exchanges of commands between servers and players’ devices, which demand an immediate response. As the data volume increases, the system becomes overloaded, resulting in high latency and packet loss. Objective: This work proposes a new Stochastic Petri Net (SPN) model to evaluate the performance of this traffic in Fog–Cloud architectures designed for large-scale multiplayer games. Methodology: We built a SPN model and validated a simplified version; the model computes utilization, mean response time, drop probability, and throughput, allowing us to analyze the impact of different capacity configurations and load-distribution strategies. Results: The results reveal a direct relationship among processing capacity, load balancing, and quality of service: increasing the number of cloud GPUs reduces latency and packet loss while boosting throughput.

This paper proposes a privacy-aware collaborative energy management framework for cloud data centers (CDCs) operating within an electricity market environment. Each CDC's energy agent (CDCEA) minimizes the total CDC's energy cost by optimizing its energy profile in response to dynamic locational marginal prices and the nodal carbon intensity of the power system issued by the independent system operator (ISO), who clears the energy market. CDCEAs integrate the on-site energy resources of CDCs, such as renewable energy and energy storage systems, and leverage the spatiotemporal flexibility of computing workloads through coordination with cloud service providers (CSPs). CSPs manage privacy-preserving workload scheduling and resource allocation by collaborating with associated CDCEAs and with other CSPs, while ensuring quality of service and adherence to service-level agreements. A two-stage optimization model is developed to coordinate decision-making between the ISO and CDCEAs in energy market in the first stage, and to address the coordination of CSPs with associated CDCEAs and other CSPs in the second stage. The proposed two-stage model is solved using a three-nested distributed iterative algorithm that combines privacy-aware hierarchical and peer-to-peer ADMM methods with an analytical target cascading (ATC) approach. The method is validated using a real-world case study in Northern Virginia, USA, demonstrating its effectiveness in reducing costs and enhancing sustainability.