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A major challenge of running applications in clouds is to determine the right number of resources (virtual machines or VMs) to rent in terms of both performance and cost. Such a challenge becomes greater if the application requires to run across multiple resources. In this paper, we address the problem of scheduling scientific workflow applications. The structure of workflows, dictated by precedence/data...
Cloud computing is attracting an increased number of researches in delivering modeling and simulation abilities as a service. Among which, simulation execution as a service (EaaS) is a hot spot. It aims at releasing users from complex running configurations and meanwhile guaranteeing the QoS requirements. Under the motivation, focusing on EaaS for parallel and distributed simulation (PADS) application,...
In cloud environments, trust is necessary because services have the characteristics of uncertainty, dynamic, false or fraudulent which usually make users difficult to obtain desired services. In this paper, we consider trust-oriented workflow scheduling with temporal constraints including service setup times and workflow deadlines. The considered problem is mathematically modeled. A behavior-based...
Cloud computing resource scheduling is a complex NP problem and difficult to solve. In order to shorten task completion time in cloud resource scheduling, an improved LDW-PSO (linearly decreasing weight-particle swarm optimization) algorithm is proposed. Firstly, for the fact that PSO algorithm is easy to fall into local convergence, based on the linearly decreasing weight strategy, the constant disturbance...
In the paper, we consider the dynamic, elastic and flexible task scheduling problem in hybrid clouds. Tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. The objective is to finish all jobs before their deadlines with renting virtual machines as less as possible. Firstly, we propose two simple and fast dispatching...
In the cloud computing environment, how to schedule tasks efficiently according to users' service requirements and improve the quality of service have become a hot research topic. Based on the research of independent task scheduling model and AMM algorithm (Average Minus Minimum), a new PAW(Priority, AMM and Weight)algorithm combined with Priority, AMM and Weight is proposed. Through quantifying task...
The quality of service is one of challenges posed by the Cloud Computing. This issue plays an important role in making the Cloud services acceptable to customers, denotes the levels of performance, reliability, and availability offered by Cloud services. Literature has reported many implementations for measuring and ensuring QoS in Cloud Computing systems to achieve better results and meet the needs...
Scheduling in Cloud computing infrastructure contain several challenging issues like computation time, budget, load balancing etc. Out of them, load balancing is one the major challenges for Cloud platform. Load balancing basically balances the load to achieve higher throughput and better resource utilization. Since scheduling task is NP-complete problem, so heuristic and meta heuristic approaches...
The advent of Cloud computing has provided a promising methodology for usage of distributed resources for complex scientific workflow applications. Due to the unique features of cloud technology, such as the pay-as-you-go pricing model and scaling, efficient workflow scheduling is a critical research topic. While most workflow scheduling algorithms are proposed to minimize the overall execution time,...
Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques,...
Effective management of resources on a cloud or cluster is crucial for achieving the quality of service requirements of users, which are typically captured in service level agreements (SLAs). This paper focuses on improving the robustness of resource allocation and scheduling techniques that process an open stream of MapReduce jobs with SLAs, by introducing techniques to handle errors/inaccuracies...
In the highly competitive business environment of Software as a Service (SaaS) clouds, Quality of Service (QoS) and fair pricing are of paramount importance for differentiating between similar cloud providers. In such platforms, the workload computational demand variability may have a significant impact on the system performance and thus on the provider's Service Level Agreement (SLA) commitments...
The creation of virtual machines (VMs) is one of the procedures of resource scheduling which is the key technology in cloud computing systems. Currently it is rarely studied independently, and it is always set as a static model where the number and the type of VMs are predefined before scheduling. However, with the static model, it is difficult to consider the overall optimization for the scheduling...
Fog computing preserves benefits of cloud computing and is strategically positioned to address effectively many local and performance issues because its resources and specific services are virtualized and located at the edge of the customer premises. Resource management is a critical issue affecting system performance significantly. Due to the complex distribution and high mobility of fog devices,...
As scientific data analysis applications become more and more complex, there is a great need to simplify the definition and execution of such applications, particularly when dealing with large datasets. The Data Mining Cloud Framework (DMCF) is a system allowing domain experts to design and execute complex data analysis workflows on cloud platforms, relying on cloud storage services for every I/O...
With the flourishing of smart mobile devices (e.g., smartphones, tablets), development of mobile cloud computing has received more and more attentions from both industry and academia. Compared with the traditional way of executing large- scale computational tasks on powerful desktop computers and the cloud, mobile cloud computing is featured by the ubiquitous availability, flexibility, and low-cost...
Mobile devices are now capable of handling many daily computing tasks that used to be accomplished by desktops or servers. However, these improvements also introduce resource-hungry mobile applications that require richer resource-hungry computing features and more complex functions. Mobile Cloud Computing (MCC) addresses these limitations considering the nature of mobility; this innovative strategy...
Hadoop is a popular open source framework that supports the processing and storage of large data sets using the MapReduce programming model. To run intensive-data that changes over time, Hadoop decoupling resource management and processing framework is used. Hadoop workloads can run on both Homogeneous and Heterogeneous infrastructures, but running workload in Heterogeneous cluster leads to several...
Cloud computing is one of the emerging technology which is rapidly developing nowadays in the current environment. The Large Scale Organizations are shifting their databases to cloud due to the various services provided by the Cloud Platforms. IAAS is the one of the services which is intended to provide the Client Centric Service to the client as per their need. Large amount of Virtual Machines (VM)...
Optimizing security dynamic scheduling method in cloud storage is significant for improving resource date throughput and storage space of a cloud storage system. The paper presents a self-adaptive layered sleep vision-based method for security dynamic scheduling in cloud storage. A decision-making tree model is used for feature classification of cloud storage resources scheduling, top-down analytical...
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