Performance analysis of allocation policies for interGrid resource provisioning

Several Grids have been established and used for varying science applications during the last years. Most of these Grids, however, work in isolation and with different utilisation levels. Previous work has introduced an architecture and a mechanism to enable resource sharing amongst Grids. It has demonstrated that there can be benefits for a Grid to offload requests or provide spare resources to another Grid. In this work, we address the problem of resource provisioning to Grid applications in multiple-Grid environments. The provisioning is carried out based on availability information obtained from queueing-based resource management systems deployed at the provider sites which are the participants of the Grids. We evaluate the performance of different allocation policies. In contrast to existing work on load sharing across Grids, the policies described here take into account the local load of resource providers, imprecise availability information and the compensation of providers for the resources offered to the Grid. In addition, we evaluate these policies along with a mechanism that allows resource sharing amongst Grids. Experimental results obtained through simulation show that the mechanism and policies are effective in redirecting requests thus improving the applications’ average weighted response time.     

A taxonomy of market‐based resource management systems for utility‐driven cluster computing

In utility‐driven cluster computing, cluster Resource Management Systems (RMSs) need to know the specific needs of different users in order to allocate resources according to their needs. This in turn is vital to achieve service‐oriented Grid computing that harnesses resources distributed worldwide based on users' objectives. Recently, numerous market‐based RMSs have been proposed to make use of real‐world market concepts and behavior to assign resources to users for various computing platforms. The aim of this paper is to develop a taxonomy that characterizes and classifies how market‐based RMSs can support utility‐driven cluster computing in practice. The taxonomy is then mapped to existing market‐based RMSs designed for both cluster and other computing platforms to survey current research developments and identify outstanding issues. Copyright © 2006 John Wiley & Sons, Ltd.     

Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology, and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud.     

Container-based cluster orchestration systems: A taxonomy and future directions

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.     

C2OF2N: a low power cooperative code offloading method for femtolet-based fog network

Power and delay aware cloud service provisioning to mobile devices has become a promising domain today. This paper proposes and implements a cooperative offloading approach for indoor mobile cloud network. In the proposed work mobile devices register under femtolet which is a home base station with computation and data storage facilities. The resources of the mobile devices are collaborated in such a way that different mobile devices can execute different types of computations based on cooperative federation. The proposed offloading scheme is referred as cooperative code offloading in femtolet-based fog network. If none of the mobile device can execute the requested computation, then femtolet executes the computation. Use of femtolet provides the mobile devices voice call service as well as cloud service access. Femtolet is used as the fog device in our approach. The proposed model is simulated using Qualnet version 7. The simulation results demonstrate that the proposed scheme minimizes the energy by 15% and average delay up to 12% approximately than the existing scheme. Hence, the proposed model is referred as a low power offloading approach.     

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.     

Coordinated rescheduling of Bag‐of‐Tasks for executions on multiple resource providers

Metaschedulers can distribute parts of a Bag‐of‐Tasks (BoT) application among various resource providers in order to speed up its execution. The expected completion time of the user application is then calculated based on the run‐time estimates of all applications running and waiting for resources. However, because of inaccurate run time estimates, initial schedules are not those that provide users with the earliest completion time. These estimates increase the time distance between the first and last tasks of a BoT application, which increases average user response time, especially in multi‐provider environments. This paper proposes a coordinated rescheduling algorithm to handle inaccurate run‐time estimates when executing BoT applications in multi‐provider environments. The coordinated rescheduling defines which tasks can have start time updated based on the expected completion time of the entire BoT application. We have also evaluated the impact of system‐generated run‐time estimates to schedule BoT applications on multiple providers. We performed experiments using simulations and a real distributed platform, Grid'5000. From our experiments, we obtained reductions of up to 5 and 10% for response time and slowdown metrics, respectively, by using coordinated rescheduling over a traditional rescheduling solution. Moreover, coordinated rescheduling requires little modification of existing scheduling systems. System‐generated predictions, on the other hand, are more complex to be deployed and may not reduce response times as much as coordinated rescheduling. Copyright © 2011 John Wiley & Sons, Ltd.     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.