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Web service and Cloud computing have significantly reformed the software industry. The need for web service allocation in the cloud environment is increasing dramatically. In order to reduce the cost for service providers as well as improve the utilization of cloud resource for cloud providers, this paper formulates the web service resource allocation in cloud environment problem as a two-level multi-objective...
Service Level Agreement (SLA) is one of the most important characteristics when discussing about Cloud scheduling. Modeling Cloud scheduling under SLA constraints represents one of the main research areas in the field of Cloud computing. One of the main ways to simulate the scheduling in the Cloud is by using a simulator that is verified by the academia and by the industry as one of the most accurate...
This paper presents a method of cloud resource allocation designed to take into account both consumers and providers' interests. This comes in contrast to today's provider centered models that subject users to more restrictive terms and conditions. Both parties' interests are computed in the form of integer constraints. Costs and availability are embedded as key objectives and performance criteria...
As compared to traditional distributed computing paradigms, a major advantage of the cloud computing is the ability to provide more reliable, affordable and flexible resources for the applications (or users). The need to manage the applications in the cloud computing creates the challenge of an on-demand resource provisioning and allotment in response to the dynamically changing workloads. Currently,...
Cloud data centres are faced with the serious problem of increasing energy consumption. Thus, the problem of virtual machine placement for energy saving is becoming a critical issue. Considering various requirements of cloud providers and users, a many-objective virtual machine placement model is built to minimize energy consumption and maximize load balance, resource utilization, and robustness....
In cloud systems, efficient resource provisioning is needed to maximize the resource utilization while reducing the Service Level Objective (SLO) violation rate, which is important to cloud providers for high profit. Several methods have been proposed to provide efficient provisioning. However, the previous methods do not consider leveraging the complementary of jobs' requirements on different resource...
The energy consumption of a data center and hence the carbon footprint from it largely depends on the energy consumption by its active Physical Machines (PMs). Researchers have taken many attempts to minimize the data center energy consumption through the Virtual Machines (VMs) allocation into a minimal number of PMs of homogeneous types. However, the current VM placement strategies do not consider...
Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers’ requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization...
Gomory-Hu based algorithms are proposed to select hosting resources and place services in a multiple providers federation context. The algorithms, use the Gomory-Hu tree, to detect critical nodes and edges in the end user requests to make selection and placement decisions. The algorithms take into account the providers' internal costs and network connectivity costs and hosting costs by the federation...
With the growing amount of information available online, context-aware recommender systems have emerged to improve the precision of recommendation. Matrix factorization models are the state-of-the-art in these systems, especially for multi-context recommendation. However, existing models ignored either context influence or entity sensitivity. That is, they assume that one entity (user or item) shares...
In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard...
Cloud computing datacenters dynamically provide millions of virtual machines in actual cloud computing markets and several challenging problems have to be addressed towards an efficient resource management of these infrastructures. In the context of resource allocation, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and a large number of existing...
A precise Quality-of-Service in cloud can be offered if the traffic management uncertainty issues are addressed properly. It is also found that developing a cost-efficient load balancing technique in cloud environment is still an unsolved problem after reviewing the work done in load balancing till date. Hence, we introduce a novel algorithm called as SBLB i.e. Stochastic Based Load balancing that...
Cloud computing is a distributed computing model which enables developers to automatically deploy applications during task allocation and storage distribution. Cloud computing intends to share a pool of virtualized computer resources and equipment's of computation, storage and information. Scheduling is the most important complex part in cloud computing. The ultimate aim of global scheduler is to...
The increase in the rates of energy consumption in cloud computing infrastructures, besides imposing scalability limits, raises concerns about monetary issues and damage to the environment. This paper proposes an algorithm named ε-PUC-Fit, which aims at improving energy efficiency in cloud computing environments through dynamic consolidation of virtual machines. The proposed algorithm has...
The exact methods do not scale well for resource management problems requiring to deploy a set of applications on some virtual machines (VMs) such that (1) each application is deployed on at least one VM, (2) there are no conflicting applications placed on the same VM, and (3) the number of VMs hosting at least one application is minimal. We overcame this difficulty by devising a hybrid approach combining...
In this paper, we introduce a pack-centric approach to data center resource management by abstracting a system as a pack of resources and considering the mapping of these packs onto physical data center resource groups, called swads. The assignment of packs/VMs to swads/PMs is formulated as an integer optimization problem that can capture constraints related to the available resources, data center...
The new optimization approach based on bio-inspired algorithms is the Cuckoo Search which is inspired from the life style of Cuckoo bird. In this paper, we explain the cuckoo search algorithm which comes under the evolutionary algorithms for virtual machine consolidation that focuses on minimum power consumption and resource wastage in cloud data centers. As the VM consolidation problem is similar...
The cloud providers provide computing, storage and network resources in an on-demand manner and pay as per usage mode. Nevertheless, the cloud consumers are facing issue to find out the suitable cloud resources that will meet their application requirements. Hence, the proposed research work introduces the cloud brokering concept that acts as the mediator between the user and the cloud provider to...
With the rapid grows of cloud-based internet application, a need for efficient resource allocation, load balance and cost management increases. In this paper, we propose a group-auction based mechanism for the cloud instance market to efficiently allocate resources. In the market system, resource providers offer resources in the form of virtual machine. Users submit their bids. The proposed system...
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