International Journal of Emerging Engineering Science and Technology Volume 1 Issue 1-2015
EFFICIENT RESOURCE SCHEDULING UNDER VARYING WORK LOADS IN CLOUD DATA CENTER 1
Bhavani B H1 , H S Guruprasad2 Assistant Professor, Department of CSE, JSS Academy of Technical Education, Bangalore, Karnataka 2 Professor and Head, Department of CSE, B M S College of Engineering, Bangalore, Karnataka 1
[email protected] 2
[email protected]
Abstract: Efficient Resource Scheduling is one of the major challenges in Cloud Computing. There are multiple users requesting for the cloud services. With the limited resources, it is challenging for cloud service provider to allocate resources to the jobs submitted by the cloud user. A PLW scheduling algorithm is proposed in order to efficiently schedule resources to jobs under varying workloads in cloud datacenter. Weights are assigned dynamically to all the jobs considering their priority, job length and waiting time. The job with the highest weight is scheduled for execution than the jobs with lesser weight. Cloud sim simulation tool is used to run this algorithm and experimental results show that the resources are used efficiently. Keywords: Resource Allocation, Resource Utilization, Job Scheduling, Priority, Waiting Time
I. INTRODUCTION Cloud computing has gained popularity as a resource platform for on demand, highly available and highly scalable access to resources. It represents a new kind of computational model, providing better use of distributed resources with dynamic flexible infrastructures and Quality of Service (QoS) guaranteed services. From a hardware point of view, the users feel that huge computing resources are available on-demand. The infrastructure is usually managed as a whole by the cloud service provider, who relies on a single resource management substrate. Thus, the substrate must be general and expressive to accommodate a wide range of possible policies for different use cases, and be easily customizable and extensible. It also needs to be fair to orchestrate the needs and interests of both the cloud service providers and cloud users. The resource management algorithm needs to be efficient enough to handle large scale problems. By using cloud based technologies, users can have easy access to huge resources with the ability to scale up and down the computing resources according to the applications needs. Virtualized cloud resources enable performance isolation, on-demand creation and customization of execution environments, from which users can benefit. From the users point of view, cloud computing provides many resources when they need them, how much they need them and for as long as they need them. Resource Allocation and Job Scheduling are the major challenges in cloud computing. As there are limited resources in the cloud data center, we need to
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find mechanisms to efficiently allocate the resources and schedule the jobs to the resources. There are many resource allocation algorithms existing which focus on few (QoS) parameters like throughput, response time, execution time, availability, reliability. Maximizing resource utilization is one of the important constraints for both cloud service provider as well as cloud user. The cloud service provider has to gain maximum profit with the available resources and the Cloud user has to get high QoS with no Service Level Agreement (SLA) violation. The organization of the paper is as follows. Section I gives the Literature Survey. Section II discusses about proposed PLW algorithm. Section III presents results and discussions. Section IV concludes the paper with future research directions. II. LITERATURE SURVEY Chi Chung et al [1] has proposed Auction based Resource provisioning with SLA consideration for multi-cloud systems. When cloud user submits more and more jobs to the cloud service provider, available resource may not be enough for completing these jobs. So, the Cloud Service Providers has to lease resource from other cloud service providers. In order to solve this problem a combinatorial auction-based approach for dynamic provisioning and allocation considering the cloud user requests with deadline. This approach will gain maximize cloud service provider’s profit and meets the deadline and minimizes SLA violation of the cloud user request. This approach also improves resource utilization.
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International Journal of Emerging Engineering Science and Technology Volume 1 Issue 1-2015 Jieqian Wu et al [2] propose elastic resource allocation in cloud. In traditional cloud computing, elasticity can be provided by increasing or decreasing the number of virtual machines (VM). However, the resource needed by applications change rapidly. This resource allocation approach is not a perfect way to satisfy the demand of the users workload in short term. In this paper, a fine grained resource allocation method is proposed in virtualized servers. Hierarchical Resource Management System (HRMS) based on the adjustable resource in virtualization environments is designed and implemented. This method uses a real-time control model in order to meet the demands of varying workload by sizing the VMs dynamically. This approach is compared, evaluated and analyzed. Experimental results confirm that this approach guarantees performance and resource efficiency for applications. Altino M. Sampaio et al [3] propose dynamic poweraware and failure-aware Cloud resource allocation for a set of independent tasks. Failure occurrences in these networked computing systems can lead to substantial negative impact on system performance, deviating the system from the initial objectives. In this work, adapted algorithms are proposed to dynamically map virtual machines to physical hosts, in order to improve cloud resources ’powerefficiency, with less effect on users required performance. These decision making algorithms have proactive fault-tolerance techniques to deal with systems failures, with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that these algorithms perform better targeting power-efficiency and SLA fulfillment, in the direction of cloud infrastructure failures. Xue Wang et al [4] discuss five resource allocation strategies for the cloud infrastructure. There is a general expectation that cloud based application is going to experience improved end to end performance assurance while maintaining a competitive cost for the services provided. In this paper, the authors explore the performance and cost benefits, which may exist when applying joint optimization resource allocation strategies to the cloud infrastructure in the presence of dynamic application requests. There are five resource allocation strategies defined and they are compared to estimate relative performance gains in a number of resource (network bandwidth, data center CPU, memory and storage) distribution scenarios. Sheng Zhang et al [5] propose stable resource allocation in geographically distributed cloud. Cloud service providers typically deploy have small sized data centers which are geographically distributed to improve data center power usage and locate resource
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closer to users. But here the major challenge is resource allocation. Many results have been obtained regarding this issue from the point of virtual machine integration, network-aware virtual machine placement, traffic, dynamic provisioning, and so on. However, there was no focus on stable resource allocations, where no resource user data center has any migration benefits. This allocation proposes two algorithms for 1-d and 2-d cases respectively which focus on stable resource allocation. Experimental results show that the proposed algorithms have good convergence and scalability. Aaron Blojay Grant et al [6] focus on virtual machines competing for limited resources and underlines effective resource management in Cloud environments. Two Cloud simulations namely Cloud Sim and Cloud Analyst are used to analyze the way virtual machines(VMs) are mapped to physical machines, the distribution of loads and management of physical resources among VMs as well as their behavioral patterns under various deployment configurations. Real-world examples are considered and a resource management strategy performed on a personal computer (PC) using activity monitor and VMware fusion running three different operating systems (OSs). Amazon Elastic Compute Cloud (EC2) is used to evaluate and analyze resource consumption via Cloud Watch tool. Results obtained shows that effective resource management increases the Quality of Service (QoS) offered by Cloud Service Providers (CSPs) as well as reduces the execution and response times. This effectively reduces the cost of consumption; a great benefit to CSPs, and for large-scale applications. ZheGao[7] discusses improved ant colony optimization algorithm to compute the allocation of the cloud computing resource and to analyze the effect of bandwidth, the network load and response time on the cloud resource. Simulation results show that the throughput is increased and the response time is reduced based on the improved ACO compared with the routing algorithm (OSPF). Jiajia Sun et al [8] propose intelligent resource allocation mechanism in cloud computing environment based on double combinatorial auction. A feedback based system is implemented to avoid malicious behavior, and a decision technique on price based on a BP (back propagation) neural network is proposed to make decisions efficiently. Group search optimization algorithm is introduced to achieve optimal allocation with the optimization goals being market surplus and total reputation. Studies show that the proposed mechanism is feasible and effective.
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International Journal of Emerging Engineering Science and Technology Volume 1 Issue 1-2015
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Load calculating function L(t):Calculates load of the data center at time ‘t’ dynamically by considering the user requests and type of the tasks. · Threshold Calculating function Th(t): Calculates threshold Th1(t) and Th2(t)at time ‘t’ dynamically depending on the load of the data center, priority, length and waiting time of tasks. · Weight assigning Function Wt(p,t): Sets Weight of the task p at time ‘t’ dynamically upon the following criteria: if L(t)< Th1(t) Weights are assigned to all the tasks, with priority as the preference. else if L(t)>=Th1(t) and L(t)
