Van Son Le

Algorithmic approach to deadlock detection for resource allocation in heterogeneous platforms

An allocation of resources to a virtual machine specifies the maximum amount of each individual element of each resource type that will be utilized, as well as the aggregate amount of each resource of each type. An allocation is thus represented by two vectors, a maximum elementary allocation vector and an aggregate allocation vector. There are more general types of resource allocation problems than those we consider here. In this paper, we present an approach for improving parallel deadlock detection algorithm, to schedule the policies of resource which supply for resource allocation in heterogeneous distributed platform. Parallel deadlock detection algorithm has a run time complexity of O(min(m,n)), where m is the number of resources and n is the number of processes. We propose the algorithm for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. The experiments also compare the performance of the proposed approach with other related work.

Applying Ant Colony System algorithm in multi-objective resource allocation for virtual services*

ABSTRACT Over the past few years, using cloud computing technology has become popular. With the cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption which in turn enhance the performance of data centres. However, using a minimum of physical machines to allocate resources for virtual services can result in system overload and break the SLA of service. Consequently, providing resources for virtual services which do not only satisfy the constraint of reducing the energy consumption but also ensure the load balancing of the whole system is necessary. In this study, we present the multi-objective resource allocation problem for virtual services. This problem aims at both reducing the energy consumption and balancing the load of physical machines. The MORA-ACS algorithm is proposed to resolve the problem by the Ant Colony System method. The experimental results show that in the CloudSim environment, the MORA-ACS algorithm could balance the load as well as reduce the energy consumption better than the Round Robin algorithm.

Deadlock Detection for Resource Allocation in Heterogeneous Distributed Platforms

An allocation of resources to a virtual machine specifies the maximum amount of each individual element of each resource type that will be utilized, as well as the aggregate amount of each resource of each type. An allocation is thus represented by two vectors, a maximum elementary allocation vector and an aggregate allocation vector. There are more general types of resource allocation problems than those we consider here. In this paper, we present an approach for improving deadlock detection algorithm, to schedule the policies of resource supply for resource allocation on heterogeneous. Deadlock detection algorithm using two way has run time complexity of O(min(m 1/2 ,n 2/3 ), where m is the number of resources and n is the number of processes. We propose the algorithm for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. The experiments also compare the performance of the proposed approach with other related work.

Resource Allocation for Virtual Service Based on Heterogeneous Shared Hosting Platforms

Nowadays, one of the issues in cloud computing is resource optimizing for virtual services to enhance IaaS service performance and meet the requirements of resource exploitation effectively. In this paper, we seek the approach of multi-dimensional resource allocation based on heterogeneous shared hosting platforms for virtual services. We construct the problem as an optimized formulation that uses a linear programming to minimize the number of physical machines. The solution for this formulation is applying the Greedy algorithms to solve and evaluate via emulation-based program.

Energy-Efficient Resource Allocation for Virtual Service in Cloud Computing Environment

For the past several years, using cloud computing technology has become popular. With the cloud computing service providers, reducing the physical machine number providing resources for virtual service in cloud computing is one of the efficient ways to decrease the energy consumption amount which in turn enhance the performance of data centers. In this study, we propose the resource allocation problem to reduce the energy consumption. \(ECRA-SA\) algorithm was designed to solve and evaluate through CloudSim simulation tool compared with an FFD algorithm. The experimental results indicate that the proposed \(ECRA-SA\) algorithm yields a higher performance in comparison with an FFD algorithm.

Fragmentation in Distributed Database Design Based on KR Rough Clustering Technique

Knowledge mining according to rough set approach is an effective method for large datasets containing many different types of data. Rough clustering, as in rough set theory, using lower approximation and upper approximation, allows objects to belong to multiple clusters in a dataset. KR Rough Clustering Technique (K-Means Rough) we propose in this paper follows k-Means primitive clustering algorithm improvement approach by combining distance, similarity with upper approximation and lower approximation. In particular, appropriate focuses will be calculated to determine whether an object will be assigned to lower approximation or upper approximation of each cluster.

The Optimal Solution of Communication Resource Allocation in Distributed System Integrated on Cloud Computing

Cloud computing has been growing rapidly in the world over the past decade. The Studies and development of this system has met the demand of large number of users in the world. In order to share shared resources, most applications are deployed in the cloud under the control of distributed systems. The distributed system deployment on the SaaS layer responds to the maximum user access through coordination between servers. This coordinate control messages moving across servers to ensure conherence, transparency for user. However, disadvantage of coordination is that communication between servers in the cloud occupies large bandwidth; not to mention overlap of information at destination by multicast transmission. In this paper, we present optimal solution of communication resource allocation (CRA) in distributed system integrated on cloud computing based on network coding technique to ensure maximum throughput and avoid overlap of information at destination.

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Abstract This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Deadlock prevention for resource allocation in model nVM-out-of-1PM

The cloud computing paradigm shift brings about disruptive changes to the traditional business models in the infocomm sector, especially for existing software licensing models and server purchase or lease models. Vietnam Posts and Telecommunications Group (VNPT) have ramped up their cloud computing efforts as a strategic response to protect their current market share. This paradigm shift also has major separability on Vietnams infocomm industry, which is heavily dependent on these traditional VNPT. Since 2014, Viet Nam has started offering services in domestic and international network in the country. The Global Data Services Joint Stock Company (GDS) is a joint venture between NTT Communications (NTT Com) and Vietnam Posts Telecommunications (VNPT). Although, a number of studies were published by third parties on how Vietnam has fared in its cloud journey. While such studies use different definitions and yardsticks, their results nonetheless are instructive in helping us to understand the progress Vietnam has made and to highlight areas for improvement. We have a few studies resources allocation such infrastructure as a services. With the aim to share research solutions and seek opportunities for local businesses who want to invest in infrastructure cloud from the Open Source duty. In this paper, we proposes an algorithm deadlock prevention the model n VM - out - of 1 PM to separate and manage resources. The algorithms for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platforms. This paper presents an algorithm prevention deadlock used to reschedule the policies of resource supply for resource allocation. In the current scenario, deadlock prevention algorithm using method global state prevention based. We have implemented and performed our algorithm proposed by using CloudSim simulator.