Physical Host Research Articles

Generative adversarial imitation learning assisted virtual network embedding algorithm for space-air-ground integrated network

The space-air-ground integrated network (SAGIN) comprises a multitude of interconnected and integrated heterogeneous networks. Its network is large in scale, complex in structure, and highly dynamic. Virtual network embedding (VNE) is designed to efficiently allocate resources within the physical host to diverse virtual network requests (VNRs) with different constraints while improving the acceptance ratio of VNRs. However, in a heterogeneous SAGIN environment, improving the utilization of network resources while ensuring the performance of the VNE algorithm is a very challenging topic. To address the aforementioned issues, we first introduce a services diversion strategy (SDS) to select embedded nodes based on different service types and network state, thereby alleviating the uneven use of resources in different network domains. Subsequently, we propose a VNE algorithm (GAIL-VNE) based on generative adversarial imitation learning (GAIL). We construct a generator network based on the actor-critic architecture, which can generate the probability of physical nodes being embedded based on the observed network state. Secondly, we construct a discriminator network to distinguish between generator samples and expert samples, which aids in updating the generator network. After offline training, the generator and discriminator reach a Nash equilibrium through game confrontation. During the embedding process of VNRs, the output of the generator provides an effective basis for generating VNE solutions. Finally, we verify the effectiveness of this method through experiments involving offline training and online embedding.

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Evaluation of time-based virtual machine migration as moving target defense against host-based attacks

Moving Target Defense (MTD) consists of applying dynamic reconfiguration in the defensive side of the attack-defense cybersecurity game. Virtual Machine (VM) migration could be used as MTD against specific host-based attacks in the cloud computing environment by remapping the distribution of VMs in the existing physical hosts. This way, when the attacker’s VM is moved to a different machine, the attack has to be restarted. However, one significant gap here is how to select a proper VM migration-based MTD schedule to reach the desired levels of system protection. This paper develops a Stochastic Petri Net (SPN) model to address this issue. The model leverages empirical knowledge about the dynamics of the attack defense in a VM migration-enabled setup. First, we present the results of an experimental campaign to acquire knowledge about the system’s behavior. The experiments provide insights for the model design. Then, based on the model, we propose a tool named PyMTDEvaluator, which provides a graphical interface that serves as a wrapper for the simulation environment of the model. Finally, we exercise the tool using Multi-Criteria Decision-Making methods to aid the MTD policy selection. Hopefully, our results and methods will be helpful for system managers and cybersecurity professionals.

Morpheus: An efficient timing-based attestation framework for safeguarding hypervisor integrity with dynamic trust

Hypervisor, the core software in cloud computing, is susceptible to malicious intrusions, potentially jeopardizing overall platform security. Physical hosts within the cloud computing environment constantly face persistent attacks, and not all hosts are equipped with essential security hardware. The prolonged latency of previous software-based detection methods could not comprehensively address these cloud threats. Furthermore, the widespread deployment of security hardware incurs substantial costs for hardware-based detection. To address these challenges, we introduce Morpheus, an efficient framework ensuring hypervisor integrity in the current cloud. This framework rapidly employs software-based methods to detect malicious hosts, utilizing a subset of hosts equipped with security hardware as the Root of Trust. Efficiency is augmented through a Neural Network scheduling module, and an embedded exponential aging mechanism fortifies time-aging trust against consistent cloud threats. Evaluation results demonstrate that it can promptly identify threatened hosts with acceptable system overhead loss, solidifying its position as a robust cloud security solution.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.
 OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.
 METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.
 RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment 
 CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Assessing In Vivo Bacterial Extracellular Vesicle (BEV) Biodistribution Using Fluorescent Lipophilic Membrane Stains.

Bacterial extracellular vesicles (BEVs) are nano-size vesicles containing a cargo of bioactive molecules that can play key roles in microbe-microbe and microbe-host interactions. In tracking their biodistribution in vivo, BEVs can cross several physical host barriers including the intestinal epithelium, vascular endothelium, and blood-brain-barrier (BBB) to ultimately accumulate in tissues such as the liver, lungs, spleen, and the brain. This tissue-specific dissemination has been exploited for the delivery of biomolecules such as vaccines for mucosal delivery. Although numerous strategies for labeling and tracking BEVs have been described, most have constraints that impact on interpreting in vivo bioimaging patterns. Here, we describe a general method for labeling BEVs using lipophilic fluorescent membrane stains which can be adopted by non-expert users. We also describe how the procedure can be used to overcome potential limitations. Furthermore, we outline methods of quantitative ex vivo tissue imaging that can be used to evaluate BEV organ trafficking.

Dynamic Optimization of Energy Efficiency and Service Level Agreement violation in Cloud Data Centers via Virtual Machine Consolidation

With the escalating proliferation of cloud computing services, cloud data center providers are actively in pursuit of solutions not only to curtail energy consumption but also to efficiently cater to the unique demands of their users, thereby enhancing user satisfaction. The process of consolidating virtual machines emerges as a viable strategy for striking a balance between energy consumption and Service Level Agreement violation (SLAv), potentially surmounting this predicament faced by cloud service providers. This study aims to minimize energy consumption and SLAv to their utmost feasible extent by utilizing the First Median Threshold (FMT) technique for physical host selection and virtual machine allocation, in conjunction with the Maximum Ratio of CPU utilization to memory Utilization (MRCU) method for virtual machine selection within the framework of virtual machine consolidation. This dual-pronged approach not only translates into cost savings for cloud providers but also ensures the delivery of optimal services to users. Furthermore, a novel method has been introduced for selecting virtual machines for migration and appropriately choosing physical hosts to achieve equilibrium between two critical parameters: energy consumption and SLAv. The proposed methodology is implemented and evaluated using the CloudSim simulator, utilizing real-world PlanetLab data. The comprehensive analysis of the simulation results reveals that the proposed approach can significantly reduce energy consumption while effectively mitigating SLAv for users.

Use of Enhanced Greedy Algorithm for Load Balancing in Cloud Computing

Because of the Internet’s phenomenal growth in recent years, computing resources are now more widely available. It led to the development of a new computing concept known as Cloud Computing, allowing users to share resources such as networks, servers, storage, applications, services, software, and data across multiple devices on demand for economical and fast. Load balancing is an important branch of cloud computing as it optimizes machine utilization by distributing tasks equally over resources. It occurs among physical hosts or Virtual Machines in a cloud environment. Round robin is a commonly used algorithm in load balancing. RR gives a time quantum for each task and is in circular order. It is noted that it suffers from many problems, such as the waste of time and the high cost. In the present study, the greedy algorithm was enhanced and implemented to allocate and schedule tasks that come to the cloud on Virtual Machines in balance. The task with the longest execution time is given to the virtual machine with the least load using an improved greedy algorithm. The outcomes demonstrate that the suggested algorithm outperformed round robin in makespan. Also, all Virtual Machines in the proposed algorithm finish their work simultaneously, whereas round robin is unbalanced.

Evidence for two dimensional anisotropic Luttinger liquids at millikelvin temperatures

Interacting electrons in one dimension (1D) are governed by the Luttinger liquid (LL) theory in which excitations are fractionalized. Can a LL-like state emerge in a 2D system as a stable zero-temperature phase? This question is crucial in the study of non-Fermi liquids. A recent experiment identified twisted bilayer tungsten ditelluride (tWTe2) as a 2D host of LL-like physics at a few kelvins. Here we report evidence for a 2D anisotropic LL state down to 50 mK, spontaneously formed in tWTe2 with a twist angle of ~ 3o. While the system is metallic-like and nearly isotropic above 2 K, a dramatically enhanced electronic anisotropy develops in the millikelvin regime. In the anisotropic phase, we observe characteristics of a 2D LL phase including a power-law across-wire conductance and a zero-bias dip in the along-wire differential resistance. Our results represent a step forward in the search for stable LL physics beyond 1D.

Three‐Step Regenerative Strategy: Multifunctional Bilayer Hydrogel for Combined Photothermal/Photodynamic Therapy to Promote Drug‐Resistant Bacteria‐Infected Wound Healing

AbstractThe drug‐resistant bacterial‐infected skin wound is still a severe healthcare problem. Uncontrolled bacterial infection, abundant reactive oxygen species (ROS) content, and prolonged inflammatory response are detrimental to wound healing. Moreover, excessive vessel growth can result in unsatisfactory scar formation. Herein, a three‐step regenerative strategy based on a bilayered gelatin/acryloyl β‐cyclodextrin (BGACD) hydrogel containing physical host–guest complexations and chemical crosslinks is proposed. The hydrogel is loaded with humic acids (HAs) and astragaloside IV (AS) in the lower layer and verteporfin (Vt) in the upper layer. Different gelatin/acryloyl β‐cyclodextrin ratios endow the lower and upper layers of the hydrogel with different degradation rates. Under light irradiation, the combination of HAs‐induced photothermal therapy (PTT) and verteporfin‐induced photodynamic therapy effectively inhibits MRSA growth. The HAs and astragaloside IV are released from the lower layer to scavenge ROS and promote M2 macrophage polarization and angiogenesis during the inflammation and early proliferation phases, while verteporfin releases from the upper layer suppress excessive vessel growth during the late proliferation and remodeling phases. The HAs‐AS@Vt@BGACD hydrogel successfully achieves rapid and scarless wound healing in an MRSA‐infected wound model in rats. Therefore, the HAs‐AS@Vt@BGACD hydrogel shows promising potential for the treatment of drug‐resistant bacteria‐infected skin wound healing.

SWARM INTELLIGENCE OPTIMIZATION FOR RESOURCE ALLOCATION IN CLOUD COMPUTING ENVIRONMENTS

Cloud computing has emerged as a powerful paradigm for resource allocation due to its scalability and flexibility. Efficient resource allocation is critical for optimizing the performance and utilization of cloud resources. In this context, swarm intelligence optimization algorithms, such as Salp Swarm Optimization (SSO), have shown promising results in solving complex optimization problems. This paper presents a novel approach that utilizes SSO for resource allocation in cloud computing environments. The proposed approach aims to maximize resource utilization, minimize response time, and improve overall system performance. The SSO algorithm is used to dynamically allocate virtual machines (VMs) to physical hosts based on their resource demands and availability. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of resource utilization and response time, thereby enhancing the efficiency of cloud computing environments.

BalCon — resource balancing algorithm for VM consolidation

Cloud providers handle substantial number of requests to create and delete virtual machines (VMs) on a daily basis, where the unknown sequence of requests eventually leads to resource fragmentation. To mitigate this issue, periodic consolidation of VMs into fewer number of physical hosts is an important cost-saving procedure, closely related to the vector bin-packing problem. In this paper, we propose the BalCon algorithm for consolidation that steadily reduces the number of active hosts and keeps migration costs low. BalCon classifies the cluster’s state and selects one of three heuristics to balance resources for superior consolidation. To evaluate BalCon’s performance with respect to optimality, we introduce integer programming models. BalCon finds 99.7% of the optimal solutions for over 750 problem instances. This outstanding result was achieved due to the Force Step of our algorithm, which is the key improvement detail for common heuristics. We compare BalCon with a modified Sercon heuristic using Huawei and synthetic datasets with two resources for allocation.

Towards optimal virtual machine placement methods in cloud environments

The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends.

An Efficient ECK-Secured FCM-Based Firefly Optimization Algorithm for Dynamic Resource Sharing in Multi-Tenant SaaS Service Clouds

Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.

Reframing Third Places: Environmental Changes of Merging Places During COVID-19.

The ubiquitous nature of technology is changing the way humans interact with interior space and redefining the third place, venues where individuals gather for socialization. During the COVID-19 pandemic, the fusion of physical and virtual space led to an overlapping of the first place (home) that served as the physical host for the virtual second (work), and third (social gathering) places. Most critically, the first place (home) became a proxy for second and third place experiences as we started connecting with the outside world, albeit virtually. The goal of this study was to determine the extent that individuals relied on technology to meet their socializing needs in response to COVID-19, verify if individuals are altering their environment as a result of the pandemic and whether these changes align with physical third-place characteristics, and inform interior designers on how to intentionally design physical space in ways that include virtual experiences. The researchers employed a mixed-methods approach by gathering data from an online survey, incorporating closed-ended and open-ended questions, using two different convenience sampling approaches (N = 229), and asking participants to submit photos to support their responses. Results illustrated that during the pandemic, virtual environments integrated with the home and became a substitute for physical third places. Four themes identified the modifications in physical space that occurred because of COVID-19 as individuals accommodated the merging of their home, work, school, and social places. Insights regarding the design of successful physical spaces that embrace virtual experiences are provided.

Edge Data Center Organization and Optimization by Using Cage Graphs

Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.

EVMPCSA: Efficient VM Packing mechanism in cloud computing using Chaotic Social Spider Algorithm

VM packing is one of prodigious challenge in cloud computing as it depends on the incoming workload onto virtual resources. Workload in the cloud is highly dynamic and it is difficult to predict the upcoming tasks and place them properly onto an appropriate VM is a challenging task. While scheduling workload onto VMs it is important to pack them in an appropriate physical machine because there is a chance of wastage of virtual resources and which leads to high energy consumption. Therefore, it is necessary to pack VMs into an appropriate Physical machines based on utilization of CPU. Many of authors proposed various consolidation techniques to assess parameters makespan, energy consumption, Througput but still there is a research gap and we can minimize energy consumption based on utilization of CPU in proposed approach i.e. EVMPCSA. Consolidation of VMs and chososing of VM for migration onto a Physical host is based on utilization of CPU. Chaotic Social Spider algorithm is used as a methodology for VM packing mechanism. EVMPCSA uses cpu utilization as constraint and used Chaotic Social Spider algorithm as methodology in this work to solve VM packing problem. It is simulated on Cloudsim and evaluated aganist existing algorithms named as PSO, CS and ACO. When it is compared with PSO, CS and ACO makespan is greatly minimizes by 30.25, 23.5% and 17.31% respectively and energy consumption is minimized for PSO, CS and ACO by 27.6%, 24.78% and 10.09% respectively.

Multi-Task Assignment Method of the Cloud Computing Platform Based on Artificial Intelligence.

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities.

Agent coalitions for load balancing in cloud data centers

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration.