A Variable Service Broker Routing Policy for data center selection in cloud analyst

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

Internet is a great marvel of science. Internet has changed the face of world. It is most essential and revolutionary creation in this world. Cloud computing is associated with internet computing. Cloud computing has undoubtedly benefited both service provider and clients in great extent. There is rapid increase in cloud's customers constantly. Although, the cloud data centers comprised of tremendous power but due to expeditious requests of users there is sudden need of balancing load. However, load balancing emerged as the conspicuous issue in the cloud heterogeneous environment. This study highlights the performance analysis of load balancing policies which are taken in a combination with service broker policy. In Fact, this study addresses that there can be reduction in response time and data center request processing time by using efficient load balancing policies. These all evaluations and results are carried out using cloud analyst simulation tool.

The aim of the paper is to analyze different service broker policies that can be used with cloud and are been used in cloud analyst. With the advancement in the cloud computing technologies cloud is becoming more popular these days. With the increase in the number of clients and servers there is also arising needs of service broker. Broker is been used to assign a server to the client on clients request. Here I will first discuss about cloud computing, and then cloud analyst and the different broker policies embedded in it under round robin load balancing policy. Then with the help of simulation results will propose the best broker policy on the basis of performance.

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers' overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users' requests and 10% of total energy consumption reduction in the data center.

The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.

SummaryIn a digital world changing at high speed, data centers still play a major role in new network architectures. Software‐defined network (SDN) technology is considered as a suitable solution to solve and overcome several limitations of standard data centers including flexibility, scalability, and ease of management. However, in an SDN with a distributed control plane, the assignment of switches could be a cause of generating significant latency that could degrade the Quality of Experience (QoE) of certain services and applications hosted on the data center. In this paper, we propose a matching game algorithm ensuring a dynamic assignment of switches. Its main characteristic is to achieve a fair assignment while balancing the load generated by the data plane among all controllers resulting in a balanced response time. The numerical results show that our algorithm outperforms other existing state‐of‐the‐art algorithms in terms of minimizing controllers' response time.

In addition to causing large-scale catastrophic damage to forests, wind can also cause damage to individual trees or small groups of trees. Over time, the cumulative effect of this wind-induced attrition can result in a significant reduction in yield in managed forests. Better understanding of the extent of these losses and the factors associated with them can aid better forest management. Information on wind damage attrition is often captured in long-term growth monitoring plots but analysing these large datasets to identify factors associated with the damage can be problematic. Machine learning techniques offer the potential to overcome some of the challenges with analysing these datasets. In this study, we applied two commonly-available machine learning algorithms (Random Forests and Gradient Boosting Trees) to a large, long-term dataset of tree growth for radiata pine (Pinus radiata D. Don) in New Zealand containing more than 157 000 observations. Both algorithms identified stand density and height-to-diameter ratio as being the two most important variables associated with the proportion of basal area lost to wind. The algorithms differed in their ease of parameterization and processing time as well as their overall ability to predict wind damage loss. The Random Forest model was able to predict ~43 per cent of the variation in the proportion of basal area lost to wind damage in the training dataset (a random sample of 80 per cent of the original data) and 45 per cent of the validation dataset (the remaining 20 per cent of the data). Conversely, the Gradient Boosting Tree model was able to predict more than 99 per cent of the variation in wind damage loss in the training dataset, but only ~49 per cent of the variation in the validation dataset, which highlights the potential for overfitting models to specific datasets. When applying these techniques to long-term datasets, it is also important to be aware of potential issues with the underlying data such as missing observations resulting from plots being abandoned without measurement when damage levels have been very high.

Feasibility of using renewable energy to supply data centers in 60[formula omitted] north latitude

The submission of tasks to a data center plays a crucial role to achieve the services like scheduling, processing in a cloud computing environment. Energy consumption of a data center must be considered for task processing as it results in high operational expenditures and bad environmental impact. Unfortunately, none of the current research works focus on energy factor while submitting tasks to a cloud. In this paper a framework is proposed to select a data center with minimum energy consumption. The service provider has to register all the data centers in a registry. The energy consumed by task processing using virtualization and energy of IT equipments like routers, switches is calculated. The data center selection framework finally selects the data center with minimum energy consumption for task processing. The experimental results indicate that the proposed idea results in a less energy when compared to the existing algorithms for selection of data centers.

In Ondo State, some businesses, telecommunication agencies and government agencies are not utilizing Information Technology (IT) services to its maximum due to challenges faced by IT penetration in the state. This paper presents the design of a Government Cloud (G-Cloud) network for Ondo State Government which will provide Infrastructure as a Service (IaaS) and Software as Service (SaaS) to major government establishments and citizens within Ondo State. In designing the G-Cloud a mathematical model of cloud computing data center was adapted to design the network; and specifications from cloud management software were used to deploy the (AMD)-Virtualized server. The simulation results from CloudAnalyst and Cloudsim show that utilizing one data center with 50Vitual Machines (VMs) the response time is 201.10ms and on increasing the number of data center to five with 50VMs per data center, the response time reduced to 56.10ms. The paper proposes a G- Cloud with five data centers that can be adopted by the state government to close the digital divide gap, or a start-up G-Cloud with one data center at a lower cost. http://dx.doi.org/10.4314/njt.v35i3.20

Cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud environment comprises of abundant nodes with distributed computing resources in different geographic locations. The main aim is to maintain all the data centres to be available and also to distribute the load uniformly to all the data centres. Load balancing with switching mechanism contains two different algorithms in which, one algorithm is used to distribute the jobs in idle state divisions and another, token-based load balancing (TLB) algorithm is used to distribute the jobs in the normal state divisions. The basic demand of each user is to complete requested jobs in the short time, so the public cloud need a mechanism, which can complete the jobs of all users with reasonable response time. The proposed work chooses different strategies for different load conditions. Whenever the server is overloaded, the TLB algorithm migrates the job to the available data centre (DC) either within a cloud division or to the data centre in other cloud division.

Cloud computing represents a new way to deploy computing technology to give users the ability to access, work on, share, and store information using the Internet. Developments in Cloud computing gain rapid attention of the researcher to maximize the utilization of computing resources such as storage, CPUs, and network bandwidth as service-by-service providers at less cost. The optimization models aims to optimize both resource centric such as utilization, availability, reliability and user centric like response time, budget spent fairness, and since the broker is responsible of route the user request to the most appropriate datacenter in the cloud system, choosing the right broker policy affect directly the optimization of the resources, in this paper we explore various number of existing broker policy and compare them in different scenarios in order to show the advantages and limitations of each one of them due there impact on the response time and the load distribution over the system.

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.