Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM: Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM

This paper presents an avoid deadlock resource allocation (ADRA) for model V VM-out-of-N PM since cloud computing is a new computing paradigm composed of grid computing, distributed computing and utility concepts. Cloud computing presents a different resource allocation paradigm than either grids or distributed systems. Cloud service providers dynamically scale virtualized computing resources as a service over the internet. Due to variable number of users and limited resources, cloud is prone to deadlock at very large scale. Resource allocation and the associated deadlock avoidance is problem originated in the design and the implementation of the distributed computing, grid computing. In this paper, a new concept of free space cloud is proposed to avoid deadlock by collecting available free resource from all allocated users. New algorithms are developed for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform.  An experiment is tested in CloudSim. The performance of resource pool manager is evaluated by using CloudSim and resource utilization and indicating good results

An Auction Mechanism for Resource Allocation in Mobile Cloud Computing Systems

A mobile cloud computing system is composed of heterogeneous services and resources to be allocated by the cloud service provider to mobile cloud users. On one hand, some of these resources are substitutable (e.g., users can use storage from different places) that they have similar functions to the users. On the other hand, some resources are complementary that the user will need them as a bundle (e.g., users need both wireless connection and storage for online photo posting). In this paper, we first model the resource allocation process of a mobile cloud computing system as an auction mechanism with premium and discount factors. The premium and discount factors indicate complementary and substitutable relations among cloud resources provided by the service provider. Then, we analyze the individual rationality and incentive compatibility (truthfulness) properties of the users in the proposed auction mechanism. The optimal solutions of the resource allocation and cost charging schemes in the auction mechanism is discussed afterwards

Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneous Clouds

Resource Allocation and Task scheduling are the most important key words in today’s dynamic cloud based applications. Task scheduling involves assigning tasks to available processors with the aim of producing minimum execution time, whereas resource allocation involves deciding on an allocation policy to allocate resources to various tasks so as to have maximum resource utilization. Algorithms used for scheduling resources for virtual machines are designed for both homogeneous and heterogeneous environments. Majority of the algorithms focus on processing ability often neglecting other features such as network bandwidth and actual resource requirements. One of the major pitfalls in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation is performed with the objective of minimizing the costs associated with it. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper we will focus on the challenges faced in task scheduling and resource allocation in dynamic heterogeneous clouds

EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced Computing and Applications, Journal of Science and Technology, Vietnamese Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201

Security-aware autonomic allocation of cloud resources: A model, research trends, and future directions

Cloud computing has emerged as a dominant platform for computing for the foreseeable future. A key factor in the adoption of this technology is its security and reliability. Here, this article addresses a key challenge which is the secure allocation of resources. The authors propose a security-based resource allocation model for execution of cloud workloads called STARK. The solution is designed to ensure security against probing, User to Root (U2R), Remote to Local (R2L) and Denial of Service (DoS) attacks whilst the execution of heterogeneous cloud workloads. Further, this paper highlights the promising directions for future research

Demand-driven Gaussian window optimization for executing preferred population of jobs in cloud clusters

Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency

Resource allocation in cloud computing using advanced imperialist competitive algorithm

Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms

Fault aware task scheduling in cloud using min-min and DBSCAN

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib = g bang Big Crunch) and WHO(Whale optimization algorithm)

DBSCAN inspired task scheduling algorithm for cloud infrastructure

Cloud computing in today\u27s computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling