Performance evaluation of Virtual Machine (VM) scheduling policies in Cloud computing (spaceshared & timeshared)

Cloud computing (CC) services can offer substantial cost-effective global operational and relationship benefits if the cooperation between logistics and CC services are resilient. Potential vulnerabilities to cooperation of CC and logistics service providers can occur with respect to vital factors such as security and trust. Extant studies have demonstrated CC benefits as well as few challenges associated with CC services application. However, no extant study has examined the inter-organisational benefits based on cooperative resilience between CC and logistics service providers in terms of both capability and trust vulnerability factors. This study examines the cooperative resilience of logistics and CC service providers based on innovation diffusion theory within a supply chain risk assessment framework. Using structural equation modelling techniques, we investigate the relationship between the vulnerability factor (trust), capability factor (security) and collaboration benefits (relationship and operational) offered by CC service providers based on 236 Chinese logistics service firms’ perceptions of CC adoption. The results indicate Chinese logistics companies perceive security impediments as a major factor affecting cooperative resilience between logistics service and CC service providers.

Job scheduling system problem is a core and challenging issue in cloud computing. How to use cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the cloud computing service providers' ultimate goals. In this paper, firstly, by analysis the differentiated QoS requirements of cloud computing resources users' jobs, we build the corresponding non-preemptive priority M/G/1 queuing model for the jobs. Then, considering cloud computing service providers' destination which is to gain the maximum profits by offering cloud computing resources, we built the system cost function for this queuing model. After that, based on the queuing model and system cost function, considering the goals of both the cloud computing service users and providers, we gave the corresponding strategy and algorithm to get the approximate optimistic value of service for each job in the corresponding no-preemptive priority M/G/1 queuing model. Finally, we also provide corresponding simulations and numerical results. Analysis and number results show that our approach for job scheduling system can not only guarantee the QoS requirements of the users' jobs, but also can make the maximum profits for the cloud computing service providers.

Background: The Open Cloud Computing Alliance (OCCA) strives for more Cloud Computing Service Providers (CCSP) to join the alliance. OCCA only requires CCSP to provide virtual computing resources and does not care about the methods of the underlying implementation, which leads the open-source cloud computing to a larger scale and more efficient. Due to the differences in service modes and service categories, the cloud computing platforms formed by CCSP are heterogeneous. How to implement tasks across platforms and ensure the quality of migration are the key issue for sharing the OCCA platform. Methods: The Mobile Agent technology based on a domain is introduced. User tasks are encapsulated into Mobile agent packets by domain client, which realizes the migration of user tasks from one platform to another, and makes it possible to interoperate between OCCA virtual machines. To ensure the service quality of OCCA better, a five-layer logical model of R-OCCA with high commercial availability is proposed, which defines the service content of each layer and gives the setting of key parameters. This paper introduces the architectural composition and operational mechanism of the model, which carries out a qualitative analysis of the model, and establishes an experimental prototype to verify the feasibility of the model on the virtual machine platform. Results: Experiments show that it is feasible to implement Cloud Computing Alliance among cloud computing platforms through Mobile Agent under the existing technical conditions. Conclusion: To better guarantee the quality of OCCA service, a five-level R-OCCA logic model with strong commercial availability is proposed. The service content of each level is defined and the key parameters are given. From the CCSP income, the rationality of the model set is explained. The feasibility of the model was analyzed. The architectural composition and operational mechanisms of the model are introduced. The performance of the model was also analyzed.

Recent research advances in cloud computing and big data

Cloud computing provides a new way for industries to meet the emerging business need for agility. Many public clouds are available for developers to build web applications on cloud. The process of entering into the cloud is generally in the form of a queue, so that each user need to wait until the current user is being served. In the system, each Cloud Computing User (CCU) requests Cloud Computing Service Provider (CCSP) for use of resources. If CCU finds the server busy, then the user has to wait till the current user completes the job. This may result in increase of queue length as well as waiting time, which may lead to request drop. To handle this problem, CCSP needs to find ways to reduce waiting time. We propose a finite multiserver queueing model with queue dependent heterogeneous servers where the web applications are modeled as queues and the virtual machines are modeled as service providers. CCSP's can use multiple servers and the number of busy servers changes depending on the queue length for reducing queue length and waiting time. This helps us to dynamically create and remove virtual machines in order to scaling up and down. We develop a recursive method to obtain the system steady-state probabilities. Various performance measures of the proposed scheme have been described and evaluated. Computational experiences in the form of graphs are presented.

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.

Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of data centers in cloud computing, power consumption has recently become an important issue. However, data centers hosting Cloud applications consume huge amounts of electrical energy and contributing to high operational costs to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. Live migration of virtual machines and their scheduling and embedding lead to enhanced efficiency of dynamic resources. The guarantee of service quality and service reliability is an indispensable and irrevocable requirement with respect to service level agreement. Hence, providing a method for reducing costs of power consumption, data transmission, bandwidth and, also, for enhancing quality of service (QoS) in cloud computing is critical. In this paper, a Big Bang–Big Crunch (BB-BC) based algorithm for embedding virtual machines in cloud computing was proposed. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. Simulation results indicate that the proposed method not only enhances service quality, thanks to the reduction of agreement violation, but also reduces power consumption.

Background: Load balancing of communication-intensive applications, allowing efficient resource utilization and minimization of power consumption is a challenging multi-objective virtual machine (VM) placement problem. The communication among inter-dependent VMs, raises network traffic, hampers cloud client’s experience and degrades overall performance, by saturating the network. Introduction: Cloud computing has become an indispensable part of Information Technology (IT), which supports the backbone of digitization throughout the world. It provides shared pool of IT resources, which are: always on, accessible from anywhere, at anytime and delivered on demand, as a service. The scalability and pay-per-use benefits of cloud computing has driven the entire world towards on-demand IT services that facilitates increased usage of virtualized resources. The rapid growth in the demands of cloud resources has amplified the network traffic in and out of the datacenter. Cisco Global Cloud Index predicts that by the year 2021, the network traffic among the devices within the datacenter will grow at Compound Annual Growth Rate (CAGR) of 23.4% Methods: To address these issues, a communication cost aware and resource efficient load balancing (CARE-LB) framework is presented, that minimizes communication cost, power consumption and maximize resource utilization. To reduce the communication cost, VMs with high affinity and inter-dependency are intentionally placed closer to each other. The VM placement is carried out by applying the proposed integration of Particle Swarm Optimization and non-dominated sorting based Genetic Algorithm i.e. PSOGA algorithm encoding VM allocation as particles as well as chromosomes. Results: The performance of proposed framework is evaluated by the execution of numerous experiments in the simulated datacenter environment and it is compared with the state-of-the-art methods like, Genetic Algorithm, First-Fit, Random-Fit and Best-Fit heuristic algorithms. The experimental outcome reveals that the CARE-LB framework improves 11% resource utilization, minimize 4.4% power consumption, 20.3% communication cost with reduction of execution time up to 49.7% over Genetic Algorithm based Load Balancing framework. Conclusion: The proposed CARE-LB framework provides promising solution for faster execution of data-intensive applications with improved resource utilization and reduced power consumption. Discussion: In the observed simulation, we analyze all the three objectives, after execution of the proposed multi-objective VM allocations and results are shown in Table 4. To choose the number of users for analysis of communication cost, the experiments are conducted with different number of users. For instance, for 100 VMs we choose 10, 20,...,80 users, and their request for VMs (number of VMs and type of VMs) are generated randomly, such that the total number of requested VMs do not exceed number of available VMs.

Interval-valued Fuzzy Logic approach for overloaded hosts in consolidation of virtual machines in cloud computing

In recent times cloud computing is transpiring as a new model for hosting and delivering user services through internet media. Cloud computing offers computing resources in the form of Virtual Machines (VMs) on demand and payment are made on the basis of the amount of resources used by user application. These unique feature attracted more number of users to host their requirements on Cloud Provider which has increased number of VM in data centers. This creates an issue of proper management of VM such that resources are efficiently utilized. Efficient utilization of resources has realized VM Consolidation which leads to efficient management of VM on as few hosts as possible, switching idle hosts (physical machines) into a power saving mode. Noteworthy research have been done in the area of efficient VM consolidation to reduce power utilization. VM migration is powerful utility to achieve VM Consolidation. But VM migration involves cost of Bandwidth and Resources between two machines. It leads to trade-off between energy utilized in migration vs energy utilized during workload. Our solution in this paper describes how to reduce trade off by efficiently migrating VM to proper Machine i.e. number of migration can be reduced.

Introduction to Cloud Computing

Cloud computing environment is complex and changeable. It needs to implement the application of large-scale, dynamic, and indefinite cloud users in the form of cloud tasks to a cloud resource environment with physical location separation, heterogeneous resources, and multiple constraints. In order to solve the resource scheduling problem of cloud computing, this paper meets the QoS requirements of cloud users and cloud computing service providers from multiple dimensions, and realizes ultra-large-scale, high-performance and energy-efficient cloud computing resource scheduling. In this paper, a multidimensional QoS cloud computing resource scheduling method with stakeholder perspective of cloud users and cloud computing service providers is implemented, which includes proposing a 2-level cloud computing resource scheduling structure, constructing a multidimensional QoS cloud computing resource scheduling model, designing an MQoS cloud computing resource scheduling optimization algorithm, and simultaneously optimizing multiple objective functions. Performance analysis show that MQoS has obvious advantages in multidimensional QoS performance compared with FIFO algorithm and Genetic algorithm, and achieves good cloud computing system utility to meet the interests of cloud users and cloud computing service providers. MQoS algorithm is also far superior to the traditional algorithms in cloud data center load balancing difference.

The concept of virtual machines is critical to manage the scalable utilization of the resources in cloud computing. Hence the scheduling and balancing the assigned job loads to virtual machines is essential and exclusive need of the cloud computing environment, which is a critical research objective since, improving the service provision in cloud computing is not only through scheduling the individual resources to the target jobs, also the scheduling the virtual machines and balancing the load at virtual machines. In the context of this argument, a method called, Hierarchical Tabu Search with Fuzzy Reasoning (HTSFR) is proposed to escalate the optimality of resource scheduling and load balancing that deals the virtual machines as resources. The performance analysis was done on simulated environment established by using Cloud SIM. The results obtained from proposal were compared to other contemporary models, which evinced the significant performance advantage of the proposal on other contemporary models compared.

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Although cloud computing is generally recognized as a technology which will has a significant impact on IT in the future. However, Cloud computing is still in its infancy, currently, there is not a standard available for it, portability and interoperability is also impossible between different Cloud Computing Service Providers, therefore, handicaps the widely deploy and quick development of cloud computing, there is still a long distance to the fine scenery which theoretically depicted by cloud computing. We analyze the problems in the current state of the art, put forward that Open Cloud Computing Federation is an inevitable approach for the widely use of cloud computing and to realize the greatest value of it. Accordingly, we proposal the MABOCCF (Mobile Agent Based Open Cloud Computing Federation) mechanism in this paper, it combines the advantages of Mobile Agent and cloud computing to provide a realization for the Open Cloud Computing Federation, MABOCCF can span over multiple heterogeneous Cloud Computing platforms and realizes portability and interoperability, it can be a beginning of open cloud computing federation and a future part of cloud computing. We also present in this paper the rationalities and the motivations for the combination of Mobile Agent and Cloud Computing, finally, a prototype is given with a performance analysis.