17 research outputs found
Energy optimization methods for Virtual Machine Placement in Cloud Data Center
The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placementin Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp
Optimal Orchestration of Virtual Network Functions
-The emergence of Network Functions Virtualization (NFV) is bringing a set of
novel algorithmic challenges in the operation of communication networks. NFV
introduces volatility in the management of network functions, which can be
dynamically orchestrated, i.e., placed, resized, etc. Virtual Network Functions
(VNFs) can belong to VNF chains, where nodes in a chain can serve multiple
demands coming from the network edges. In this paper, we formally define the
VNF placement and routing (VNF-PR) problem, proposing a versatile linear
programming formulation that is able to accommodate specific features and
constraints of NFV infrastructures, and that is substantially different from
existing virtual network embedding formulations in the state of the art. We
also design a math-heuristic able to scale with multiple objectives and large
instances. By extensive simulations, we draw conclusions on the trade-off
achievable between classical traffic engineering (TE) and NFV infrastructure
efficiency goals, evaluating both Internet access and Virtual Private Network
(VPN) demands. We do also quantitatively compare the performance of our VNF-PR
heuristic with the classical Virtual Network Embedding (VNE) approach proposed
for NFV orchestration, showing the computational differences, and how our
approach can provide a more stable and closer-to-optimum solution
Functional model of a software system with random time horizon
Virtualization technologies are being actively used to design infrastructure of cloud computing systems. In this case applications can be duplicated and hosted in different virtual machines on different physical nodes. That defines various performance of applications which causes the problem of managing performance of the entire heterogeneous system. There are different ways of solving this problem, including queuing theory methods. However research of the threshold discipline in scope of queuing theory is not complete because of difficulty of gathering precise analytic values and building of precise mathematic model of the system. Another feature of heterogeneous systems is the finite random time of system functioning which is defined by random endogenous and exogenous factors. This paper gives an overview on a functional model of the system with two heterogeneous devices with random functioning time and different service disciplines. In scope of simulation statistic experiments for different service disciplines at random time interval an average time needed to process a single request is measured. A comparison of service disciplines is conducted. Authors also provide a working software implementation of the heterogeneous system and experiments with use of service disciplines is performed
Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers
In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC
Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing
Quantum cloud computing (QCC) offers a promising approach to efficiently
provide quantum computing resources, such as quantum computers, to perform
resource-intensive tasks. Like traditional cloud computing platforms, QCC
providers can offer both reservation and on-demand plans for quantum resource
provisioning to satisfy users' requirements. However, the fluctuations in user
demand and quantum circuit requirements are challenging for efficient resource
provisioning. Furthermore, in distributed QCC, entanglement routing is a
critical component of quantum networks that enables remote entanglement
communication between users and QCC providers. Further, maintaining
entanglement fidelity in quantum networks is challenging due to the requirement
for high-quality entanglement routing, especially when accessing the providers
over long distances. To address these challenges, we propose a resource
allocation model to provision quantum computing and networking resources. In
particular, entangled pairs, entanglement routing, qubit resources, and
circuits' waiting time are jointly optimized to achieve minimum total costs. We
formulate the proposed model based on the two-stage stochastic programming,
which takes into account the uncertainties of fidelity and qubit requirements,
and quantum circuits' waiting time. Furthermore, we apply the Benders
decomposition algorithm to divide the proposed model into sub-models to be
solved simultaneously. Experimental results demonstrate that our model can
achieve the optimal total costs and reduce total costs at most 49.43\% in
comparison to the baseline model.Comment: 30 pages and 20 figure