19,446 research outputs found
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Recent technology advancements in the areas of compute, storage and
networking, along with the increased demand for organizations to cut costs
while remaining responsive to increasing service demands have led to the growth
in the adoption of cloud computing services. Cloud services provide the promise
of improved agility, resiliency, scalability and a lowered Total Cost of
Ownership (TCO). This research introduces a framework for minimizing cost and
maximizing resource utilization by using an Integer Linear Programming (ILP)
approach to optimize the assignment of workloads to servers on Amazon Web
Services (AWS) cloud infrastructure. The model is based on the classical
minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin
On Reliability-Aware Server Consolidation in Cloud Datacenters
In the past few years, datacenter (DC) energy consumption has become an
important issue in technology world. Server consolidation using virtualization
and virtual machine (VM) live migration allows cloud DCs to improve resource
utilization and hence energy efficiency. In order to save energy, consolidation
techniques try to turn off the idle servers, while because of workload
fluctuations, these offline servers should be turned on to support the
increased resource demands. These repeated on-off cycles could affect the
hardware reliability and wear-and-tear of servers and as a result, increase the
maintenance and replacement costs. In this paper we propose a holistic
mathematical model for reliability-aware server consolidation with the
objective of minimizing total DC costs including energy and reliability costs.
In fact, we try to minimize the number of active PMs and racks, in a
reliability-aware manner. We formulate the problem as a Mixed Integer Linear
Programming (MILP) model which is in form of NP-complete. Finally, we evaluate
the performance of our approach in different scenarios using extensive
numerical MATLAB simulations.Comment: International Symposium on Parallel and Distributed Computing
(ISPDC), Innsbruck, Austria, 201
Optimal Placement Algorithms for Virtual Machines
Cloud computing provides a computing platform for the users to meet their
demands in an efficient, cost-effective way. Virtualization technologies are
used in the clouds to aid the efficient usage of hardware. Virtual machines
(VMs) are utilized to satisfy the user needs and are placed on physical
machines (PMs) of the cloud for effective usage of hardware resources and
electricity in the cloud. Optimizing the number of PMs used helps in cutting
down the power consumption by a substantial amount.
In this paper, we present an optimal technique to map virtual machines to
physical machines (nodes) such that the number of required nodes is minimized.
We provide two approaches based on linear programming and quadratic programming
techniques that significantly improve over the existing theoretical bounds and
efficiently solve the problem of virtual machine (VM) placement in data
centers
Cargo Consolidation and Distribution Through a Terminals-Network: A Branch-And-Price Approach
Less-than-truckload is a transport modality that includes many practical variations to convey a number of transportation-requests from the origin locations to their destinations by using the possibility of goods-transshipments on the carrier?s terminals-network. In this way logistics companies are required to consolidate shipments from different suppliers in the outbound vehicles at a terminal of the network. We present a methodology for finding near-optimal solutions to a less-than-truckload shipping modality used for cargo consolidation and distribution through a terminals-network. The methodology uses column generation combined with an incomplete branch-and-price procedure.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin
Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty
The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-016-1065-xReducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.Peer ReviewedPostprint (author's final draft
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