A new hybrid load balancing algorithm in grid computing systems

Grid computing systems are distributed systems developed by the integration of heterogeneous resources with various characteristics. These heterogeneous computing resources are used to run highly complex programs that require very high processing power and huge volume of input data. Therefore, as a result of a large number of resources and their heterogeneity administration of these resources is an important issue in computing systems. Our intention is to develop a new algorithm for creating load balancing in these systems. In this paper, we have presented a new algorithm which is a combination of static and dynamic load balancing. In this algorithm, we have defined a time range called Update Interval which in the basis of Update Interval, the information in the table of effective nodes is updated. The advantage of this method is that, it reduces the delay and deadlock significantly. Simulation results indicate that our proposed algorithm can reduce the wait time of the tasks and subsequently their completion time and the delay in execution time of the tasks decreased

A macroeconomic model for inflation control in market-based grid environment

Providers and consumers as two main elements in economic grid environment try to reach the maximum efficiency of the environment. Providers attempt to obtain the maximum income using a suitable pricing mechanism. Consumers also seek resources with the minimum cost. Due to the autonomous nature of grid environment, providers may price their resources without taking the consumers conditions into account. Therefore, consumers face budget deficit for buying their required resources. So, the number of unused resources will increase. As a result, consumers satisfaction rate and providers efficiency will decrease. This issue leads to the inefficiency of market-based economic grid environment. In this study, a model is presented based on the macroeconomics concepts in order for the providers and consumers to price and budget, respectively, considering the expected rate of inflation. The obtained results demonstrate the usefulness of this model

W_SR: A QoS Based Ranking Approach for Cloud Computing Service

Cloud computing is a kind of computing model that promise accessing to information resources in request time and subscription basis. In this environment, there are different type of user’s application with different requirements. In addition, there are different cloud Service providers which present spate services with various qualitative traits. Therefore determining the best cloud computing service for users with specific applications is a serious problem. Service ranking system compares the different services based on quality of services (QoS), in order to select the most appropriate service. In this paper, we propose a W_SR (Weight Service Rank) approach for cloud service ranking that uses from QoS features. Comprehensive experiments are conducted employing real-world QoS dataset, including more than 2500 web services over the world. The experimental results show that execution time of our approach is less than other approaches and it is more flexible and scalable than the others with increase in services or users

An online learning model based on episode mining for workload prediction in cloud

The resource provisioning is one of the challenging problems in the cloud environment. The resources should be allocated dynamically according to the demand changes of the applications. Over-provisioning increases energy wasting and costs. On the other hand, under-provisioning causes Service Level Agreements (SLA) violation and Quality of Service (QoS) dropping. Therefore the allocated resources should be close to the current demand of applications as much as possible. Thus, the prediction of the future workload of applications is an essential step before the resource provisioning. In our previous work, we proposed a Prediction mOdel based on SequentIal paTtern mINinG (POSITING), which considers the correlation between different resources and extracts behavioural patterns of applications independently of the fixed pattern length explicitly. Although POSITING provides reliable results, it is not able to adapt according to the workload variations. The application behaviour might change and drift due to the dynamic nature of cloud. For this purpose, we investigate the capabilities of online learning for POSITING. This paper proposes a Prediction mOdel based on epIsode miNing with the capabiliTy of onlIne learNinG (RELENTING) based on POSITING. Thus, in addition to the accuracy, adaptability, one of the most important characteristics of the application prediction models, is fulfilled. The performance of the proposed model is evaluated based on both real and synthetic workloads. The experimental results show that the proposed model adapts to the behavioural changes of the application and learns the new behavioural patterns rapidly in comparison to the other state-of-the-art methods such as moving average, linear regression, neural networks and hybrid prediction approaches