Combinatorial Double Auction Based Meta-scheduler for Medical Image Analysis Application in Grid Environment

Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

IRMM: An Integrated Resource Management Model for Grid Computing

Grid computing is a collection of heterogeneous systems or heterogeneous objects that are geographically distributed over a network. Resource management is a process in which various activities like allocation of resources and scheduling are performed for handling issues like load balancing, reliability, scalability, maximum, throughput, minimum expectation time and security. There are several factors that make resource management difficult as different system may have different requirements, properties, conditions and different access and cost models. Resource management in Grid is the method of identifying requirements, finding corresponding resources to the applications, allocating those matching resources, scheduling and monitoring. In Grid resource management resource broker plays the very important role. Users communicate with a resource broker to access the grid information. Resource broker discover the resource that are available and negotiates with their owners or their agents to get the reservation of resources. Number of approaches exists through which one can develop grid resource management systems. In this paper a new architectural model has been implemented for grid resource management which is based on the characteristics of both the Economical model and Hierarchical model.

Diversified Quality Aware Ensemble Resource Scheduling (DQAERS) for IAAS with Massive Load of Tasks and Resources in Cloud Computing

Identifying a deterministic approach to perform resource scheduling in cloud computing is crucial requirement, which is since, the volume of the anomalies and the high dimensionality of the values projected to these anomalies observed during resource scheduling. The volume of tasks that evinces flash-crowd state at resource broker of the IAAS, and high dimensionality of the anomalies projected for resource quality factors are out of scope in regard to contemporary resource scheduling strategies contributed in recent past. Hence’ the resource scheduling by contemporary methods in such conditions are insignificant as the resource scheduling optimality observed as probabilistic. In order to optimize the resource scheduling in the context of aforesaid properties high volume of tasks (flash-crowd state at resource broker) and high dimensional projection of anomalies, this manuscript derived an ensemble resource scheduling strategy, which fall in to the category of batch scheduling. The experimental study outlined that the proposal is most prominent and robust to deliver optimal resource scheduling in the context of anomalies of high volume and dimensionality that compared to the contemporary method.