Tasks scheduling technique using league championship algorithm for makespan minimization in IaaS cloud

Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud

Optimum Resource Allocation using Specification Matching and Priority Based Method in Cloud

Cloud computing is summed up as a different model for allowing favorable, network as per demand to use shared devices of computational resources which are collected and then released with marginal management effort or interaction with any client or any service provider. Cloud computing is a well-known technology in the pasture of information technology that provides computing as a service. In cloud computing environment the resources are provisioned on the basis of demand, as and when required. A large number of cloud users can request a number of cloud services at the same time. Due to increase in the usage of cloud computing there is a need for a efficient and effective resource allocation algorithm which can be used for proper usage of the resources and also check that the resource is not wastage. In this we propose a priority based resource allocation algorithm which can be used for proper allocation of resources and also the resources are allocated efficiently and effectively. In this paper, two strategies are proposed for the purpose of optimum resource allocation in which the first approach uses the concept of specification matching and second uses the concept of priority based approach. In the first approach, different types of resources (virtual machine) are allocated by taking three parameters into consideration: processing element, main memory, and network bandwidth. In the second approach, one parameter is considered namely: Priority. In both strategies, users are allowed to submit the parameters during cloudlet submission. The user inserted parameters will then be considered while allocating resources to them. The objectives of this research are to improve utilization of resources and reduce the request loss

Client-side resource management on the cloud: survey and future directions

Cloud computing and how to bridge the gap between various providers is getting increasing attention. In this context, efficiently scheduling tasks on heterogeneous resources is of extreme importance. The state-of-the-art for this field has been continuously growing during the last years and has reached a point in which a comprehensive overview indicating current solutions and ongoing challenges is of extreme importance for researchers. This paper aims to offer this analysis from a client-side scheduling perspective in which emphasis is not put on physical resource selection but on task to virtual machine mappings and virtual machine allocation. It provides a taxonomy for the current state-of-the-art and a unified model concerning the various metrics and goals used throughout literature. This model is designed to be sufficiently generic, extensible, and comprehensive to support most of the future work in the field. Several promising research directions and existing challenges are described

Load balancing optimization in cloud computing: Applying Endocrine-particale swarm optimization

© 2015 IEEE. Load balancing optimization is categorized as NP-hard problem, playing an important role in enhancing the cloud utilization. Different methods have been proposed for achieving the system load balancing in cloud environment. VM migration is one of these techniques, proposed to improve the VMs' functionality. Despite of the advantageous of VM migration, there are still some drawbacks which urged researchers to improve VM migration methods. In this paper we propose a new load balancing technique, using Endocrine algorithm which is inspired from regulation behavior of human's hormone system. Our proposed algorithm achieves system load balancing by applying self-organizing method between overloaded VMs. This technique is structured based on communications between VMs. It helps the overloaded VMs to transfer their extra tasks to another under-loaded VM by applying the enhanced feed backing approach using Particle Swarm Optimization (PSO). To evaluate our proposed algorithm, we expanded the cloud simulation tool (Cloudsim) which is developed by University of Melbourne. The simulation result proves that our proposed load balancing approach significantly decreases the timespan compared to traditional load balancing techniques. Moreover it increases the Quality Of Service (QOS) as it minimizes the VMs' downtime

Scheduling Algorithms for Cloud: A Survey and Analysis

Cloud Computing is a fast growing computing paradigm due to the vast benefits it provides to the users. Scheduling becomes one of the key aspects due to the pay-as-you-go nature of the Cloud. The factors affecting the technique of scheduling applied change with change in scenarios. For instance for scheduling in hybrid clouds the data transfer speed has to be taken into consideration whereas for mobile environments scheduling becomes dependent on context change. Moreover scheduling can be improvised on many fronts such as energy efficiency, cost minimization, Maximization of resource utilization, etc. This paper surveys scheduling techniques in various Cloud Computing scenarios and sites the most efficient scheduling technique available for a particular set of user needs by comparing various techniques and the problems they address

Prediction-based resource allocation model for real time tasks

High performance computing (HPC) platforms provides computing, storage and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule applications tasks offline, but they usually take longer in processing which results in deadlines miss when tasks need some data from remote storage locations. In this paper, we propose a prediction-based model which analyze task feasibility before scheduling on the HPC resources when tasks have data-intensive constraints. The main advantage of the prediction analysis modules is to save time by refraining further analysis on non-scheduled tasks. The model helps in searching suitable resources and improved resource utilization by considering task workload in advance

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling