A Trusted Architecture for Virtual Machines on Cloud Servers with Trusted Platform Module and Certificate Authority

Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer's credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.

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.

Infrastructure-as-a-Service (IaaS) is a widespread cloud computing provisioning model where ICT infrastructure, including servers, storage and networking, is supplied on-demand, in a pay-as-you-go fashion. IaaS cloud providers give their clients virtual machines (VMs) that are controlled by cloud administrators who can run, stop, restore and migrate the VMs. A typical threat to IaaS is unauthorized access of untrustworthy administrators to cloud users' sensitive information residing in VMs' memory. In this paper we focus on the threat of users' cryptographic keys being stolen from the RAM of the VM they provision. We propose a decrypt-scatter/gather-decrypt technique that allows users to carry our encryption/decryption while protecting keys from unauthorized peeks on the part of cloud administrators. Our technique does not require modification to the current cloud architecture, but only the availability of a Trusted Platform Module (TPM) capable of creating and holding a TPM-protected public/private key pair. It lends itself to security-as-a-service scenarios where third parties perform encryption/decryption on behalf of data owners.

For the proper provision of online services, service providers need to collect some personal data from their customers; for instance, an address is collected in order to deliver goods to the right customer. Here the service provider and customer are called data collector (DC) and data subject (DS) respectively. After receiving the personal data, the DC is free to use them as he likes: he may process them for purposes which are not consented by the DS, and even share them with third parties (TPs). Researchers have paid attention to this problem, but previously proposed solutions do not guarantee that, after they have been disclosed to DCs, personal data can only be used as specified by DSs. These solutions require good behaving DCs and assume that DCs’ behavior is verifiable, but do not actually show what happens after DCs get the data. In this paper, we propose a solution that guarantees this by enforcing sticky policies along communication chains composed of a DS, a DC and one (or more) TPs. Our solution uses trusted platform modules (TPMs) and virtual machines (VMs).KeywordsSticky policiesTPMVirtual machinePrivacy

Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level implementation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving such kind of problem can reduce under or over utilization of resources, increase user satisfaction by serving more users during peak hours, reduce implementation cost for providers and service cost for users. Existing works on cloud computing focuses to accurate estimation of the capacity needs, static or dynamic VM (Virtual Machine) creation and scheduling. But significant amount of time is required to create and destroy VMs which could be used to serve more user requests. In this paper, an adaptive QoS (Quality of Service) aware VM provisioning mechanism is developed that ensures efficient utilization of the system resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is ensured by serving all the tasks within the requirements described in SLA. Tasks are separated using multilevel queue and the most urgent task is given high priority. The simulation-based experimental results shows that a great number of tasks can be served compared to others which will help to satisfy customers during the peak hour.

The concept Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how business potential extends today. In recent advancements, Cloud computing applications are provided as services to end-users. The application services hosted under Cloud computing model have complex provisioning, configuration, and deployment requirements How to use Cloud computing resources efficiently and gain the maximum profits with efficient utilization of resources is one of the Cloud computing service providers' ultimate goals. Repetitive evaluation of the performance of Cloud provisioning policies, application workload models, and resources performance models in dynamic system are difficult to achieve and rather a time consuming and costly approach. To overcome this challenge, Cloud analyst simulator based on Cloud Sim has been proposed which enables the modeling and simulation in cloud's ambience. The objective of this paper is to prove that the choice of VM Scheduling Policy in Cloud computing model significantly improves the application performance under resource and service demand variations. We will discuss different Virtual Machine (VM) Scheduling Policies implemented and their performance analysis in Virtual environment of cloud computing in order to achieve better Quality of Service (QoS).

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

Cloud computing provides facilities. These facilities increased demand for its using as institutions and individuals moved to the cloud service. Therefore, cloud service providers must provide services to users based on the expected quality. One of the main challenges presented by the cloud computing is the Quality of Service management. QoS management is defined as allocating resources to applications to ensure service based on reliability, performance and availability. It is necessary to allocate resources based on load balancing that allows avoiding overloading or low loading in virtual machines, and this is a challenge for researchers in the field of cloud computing. This research highlights the importance of cloud computing, its types and importance, It also reviews some researches in the field of quality assurance of service in computing.

Due to economic benefits of cloud computing, consumers have rushed to adopt Cloud Computing. Apart from rushing into cloud, security concerns are also raised. These security concerns cause trust issue in adopting cloud computing. Enterprises adopting cloud, will have no more control over data, application and other computing resources that are outsourced from cloud computing provider. In this paper we propose a novel technique that will not leave consumer alone in cloud environment. Firstly we present theoretical analysis of selected state of the art technique and identified issues in IaaS cloud computing. Secondly we propose Distributed Trust Protocol for IaaS Cloud Computing in order to mitigate trust issue between cloud consumer and provider. Our protocol is distributed in nature that lets the consumer to check the integrity of cloud computing platform that is in the premises of provider's environment. We follow the rule of security duty separation between the premises of consumer and provider and let the consumer be the actual owner of the platform. In our protocol, user VM hosted at IaaS Cloud Computing uses Trusted Boot process by following specification of Trusted Computing Group (TCG) and by utilizing Trusted Platform Module (TPM) Chip of the consumer. The protocol is for the Infrastructure as a Service IaaS i.e. lowest service delivery model of cloud computing.

The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud.

Inclusion of quality criteria in public bus service contracts in metropolitan areas

: The design of a trusted system based on the Trusted Computing Group's Trusted Platform Module (TPM) was analyzed to understand the role and trust relationships of the TPM, firmware, and software modules involved. The objective was to confirm that the measurements stored and reported by the TPM can successfully discriminate a normal boot sequence, which leaves trusted system software in control, from an insecure one, where some trusted modules might have been replaced by malicious ones. The principal tool used in the analysis was the SMV symbolic model checker.

Service level agreement (SLA) is a contract between cloud computing customer and provider. Improving the customers' trust with cloud computing requires monitoring and ensuring the quality of the provided services continuously. Therefore, this paper provides a model for monitoring the SLA which belongs to the same customer and a specific provider of multiple virtual machines (VMs) in the same environment. The core foundations for this model are: SLA, quality of service (QoS) parameters, multiple VMs and violations' penalty assessment. The proposed model monitors the QoS parameters within multiple VMs environment dynamically and checks for any violation. Moreover; the penalty for each violation will be calculated and informed to the customer and provider at the same time. The proposed model depends on the time factor while running the VMs in sequentially manner with a short period of time as a gap between each VM. This model prevents any interception between VMs and can maintain synchronisation.