Results 1  10
of
42
Cloudward bound: Planning for beneficial migration of enterprise applications to the cloud
 In Proceedings of SIGCOMM
, 2010
"... In this paper, we tackle challenges in migrating enterprise services into hybrid cloudbased deployments, where enterprise operations are partly hosted onpremise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloudbased architectures, while honoring applicati ..."
Abstract

Cited by 32 (0 self)
 Add to MetaCart
In this paper, we tackle challenges in migrating enterprise services into hybrid cloudbased deployments, where enterprise operations are partly hosted onpremise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloudbased architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multitiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprisespecific constraints, cost savings, and increased transaction delays and widearea communication costs that may result from the migration. Evaluations based on real enterprise applications and Azurebased cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios.
Conic mixedinteger rounding cuts
 University of CaliforniaBerkeley
, 2006
"... Abstract. A conic integer program is an integer programming problem with conic constraints. Many important problems in finance, engineering, statistical learning, and probabilistic optimization are modeled using conic constraints. Here we study mixedinteger sets defined by secondorder conic constr ..."
Abstract

Cited by 17 (5 self)
 Add to MetaCart
Abstract. A conic integer program is an integer programming problem with conic constraints. Many important problems in finance, engineering, statistical learning, and probabilistic optimization are modeled using conic constraints. Here we study mixedinteger sets defined by secondorder conic constraints. We introduce generalpurpose cuts for conic mixedinteger programming based on polyhedral conic substructures of secondorder conic sets. These cuts can be readily incorporated in branchandbound algorithms that solve continuous conic programming or linear programming relaxations of conic integer programs at the nodes of the branchandbound tree. Central to our approach is a reformulation of the secondorder conic constraints with polyhedral secondorder conic constraints in a higher dimensional space. In this representation the cuts we develop are linear, even though they are nonlinear in the original space of variables. This feature leads to computationally efficient implementation of nonlinear cuts for conic mixedinteger programming. The reformulation also allows the use of polyhedral methods for conic integer programming. Our computational experiments show that conic mixedinteger rounding cuts are very effective in reducing the integrality gap of continuous relaxations of conic mixedinteger programs and, hence, improving their solvability.
Efficient and safe global constraints for handling numerical constraint systems
 SIAM J. NUMER. ANAL
, 2005
"... Numerical constraint systems are often handled by branch and prune algorithms that combine splitting techniques, local consistencies, and interval methods. This paper first recalls the principles of Quad, a global constraint that works on a tight and safe linear relaxation of quadratic subsystems ..."
Abstract

Cited by 15 (3 self)
 Add to MetaCart
Numerical constraint systems are often handled by branch and prune algorithms that combine splitting techniques, local consistencies, and interval methods. This paper first recalls the principles of Quad, a global constraint that works on a tight and safe linear relaxation of quadratic subsystems of constraints. Then, it introduces a generalization of Quad to polynomial constraint systems. It also introduces a method to get safe linear relaxations and shows how to compute safe bounds of the variables of the linear constraint system. Different linearization techniques are investigated to limit the number of generated constraints. QuadSolver, a new branch and prune algorithm that combines Quad, local consistencies, and interval methods, is introduced. QuadSolver has been evaluated on a variety of benchmarks from kinematics, mechanics, and robotics. On these benchmarks, it outperforms classical interval methods as well as constraint satisfaction problem solvers and it compares well with stateoftheart optimization solvers.
MixedInteger Nonlinear Programming Models and Algorithms for LargeScale Supply
 Chain Design with Stochastic Inventory Management. Industrial & Engineering Chemistry Research 2008
"... An important challenge for most chemical companies is to simultaneously consider inventory optimization and supply chain network design under demand uncertainty. This leads to a problem that requires integrating a stochastic inventory model with the supply chain network design model. This problem ca ..."
Abstract

Cited by 6 (5 self)
 Add to MetaCart
An important challenge for most chemical companies is to simultaneously consider inventory optimization and supply chain network design under demand uncertainty. This leads to a problem that requires integrating a stochastic inventory model with the supply chain network design model. This problem can be formulated as a large scale combinatorial optimization model that includes nonlinear terms. Since these models are very difficult to solve, they require exploiting their properties and developing special solution techniques to reduce the computational effort. In this work, we analyze the properties of the basic model and develop solution techniques for a joint supply chain network design and inventory management model for a given product. The model is formulated as a nonlinear integer programming problem. By reformulating it as a mixedinteger nonlinear programming (MINLP) problem and using an associated convex relaxation model for initialization, we first propose a heuristic method to quickly obtain good quality solutions. Further, a decomposition algorithm based on Lagrangean relaxation is developed for obtaining global or nearglobal optimal solutions. Extensive computational examples with up to 150 distribution centers and 150 retailers are presented to illustrate the performance of the algorithms and to compare them with the fullspace solution. To whom all correspondence should be addressed.
Cyclic ShortTerm Scheduling Of Multiproduct Batch Plants Using ContinuousTime Representation
, 2004
"... The idea of cyclic scheduling is commonly utilized to address shortterm scheduling problems for multiproduct batch plants under the assumption of relatively stable operations and product demands. It requires the determination of optimal cyclic schedule, thus greatly reducing the size of the overall ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
The idea of cyclic scheduling is commonly utilized to address shortterm scheduling problems for multiproduct batch plants under the assumption of relatively stable operations and product demands. It requires the determination of optimal cyclic schedule, thus greatly reducing the size of the overall scheduling problems with large time horizon. In this paper a new cyclic scheduling approach is proposed based on the statetask network (STN) representation of the plant [Comput. Chem. Eng. 17 (1993) 211] and a continuoustime formulation [Ind. Eng. Chem. Res. 37 (1998a) 4341]. Assuming that product demands and prices are not fluctuating along the time horizon under consideration, the proposed formulation determines the optimal cycle length as well as the timing and sequencing of tasks within a cycle. This formulation corresponds to a nonconvex mixed integer nonlinear programming (MINLP) problem, for which local and global optimization algorithms are used and the results are illustrated for various case studies. 2004 Elsevier Ltd. All rights reserved.
Copositive optimization – recent developments and applications
 European Journal of Operational Research
, 2012
"... Due to its versatility, copositive optimization receives increasing interest in the Operational Research community, and is a rapidly expanding and fertile field of research. It is a special case of conic optimization, which consists of minimizing a linear function over a cone subject to linear const ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
Due to its versatility, copositive optimization receives increasing interest in the Operational Research community, and is a rapidly expanding and fertile field of research. It is a special case of conic optimization, which consists of minimizing a linear function over a cone subject to linear constraints. The diversity of copositive formulations in different domains of optimization is impressive, since problem classes both in the continuous and discrete world, as well as both deterministic and stochastic models are covered. Copositivity appears in local and global optimality conditions for quadratic optimization, but can also yield tighter bounds for NPhard combinatorial optimization problems. Here some of the recent success stories are told, along with principles, algorithms and applications. 1.
An interval partitioning approach for continuous constrained optimization
 Models and Algorithms in Global Optimization
, 2006
"... Constrained Optimization Problems (COP’s) are encountered in many scientific fields concerned with industrial applications such as kinematics, chemical process optimization, molecular design, etc. When nonlinear relationships among variables are defined by problem constraints resulting in nonconv ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Constrained Optimization Problems (COP’s) are encountered in many scientific fields concerned with industrial applications such as kinematics, chemical process optimization, molecular design, etc. When nonlinear relationships among variables are defined by problem constraints resulting in nonconvex feasible sets, the problem of identifying feasible solutions may become very hard. Consequently, finding the location of the global optimum in the COP is more difficult as compared to boundconstrained global optimization problems. This chapter proposes a new interval partitioning method for solving the COP. The proposed approach involves a new subdivision direction selection method as well as an adaptive search tree framework where nodes (boxes defining different variable domains) are explored using a restricted hybrid depthfirst and bestfirst branching strategy. This hybrid approach is also used for activating local search in boxes with the aim of identifying different feasible stationary points. The proposed search tree management approach improves the convergence speed of the interval partitioning method that is also supported by the new parallel subdivision direction selection rule
GLOBAL OPTIMIZATION OF EXPLICIT STRONGSTABILITYPRESERVING RUNGEKUTTA METHODS
"... Abstract. Strongstabilitypreserving RungeKutta (SSPRK) methods are a type of time discretization method that are widely used, especially for the time evolution of hyperbolic partial differential equations (PDEs). Under a suitable stepsize restriction, these methods share a desirable nonlinear sta ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract. Strongstabilitypreserving RungeKutta (SSPRK) methods are a type of time discretization method that are widely used, especially for the time evolution of hyperbolic partial differential equations (PDEs). Under a suitable stepsize restriction, these methods share a desirable nonlinear stability property with the underlying PDE; e.g., positivity or stability with respect to total variation. This is of particular interest when the solution exhibits shocklike or other nonsmooth behaviour. A variety of optimality results have been proven for simple SSPRK methods. However, the scope of these results has been limited to loworder methods due to the detailed nature of the proofs. In this article, global optimization software, BARON, is applied to an appropriate mathematical formulation to obtain optimality results for general explicit SSPRK methods up to fifthorder and explicit lowstorage SSPRK methods up to fourthorder. Throughout, our studies allow for the possibility of negative coefficients which correspond to downwindbiased spatial discretizations. Guarantees of optimality are obtained for a variety of third and fourthorder schemes. Where optimality is impractical to guarantee (specifically, for fifthorder methods and certain lowstorage methods), extensive numerical optimizations are carried out to derive numerically optimal schemes. As a part of these studies, several new schemes arise which have theoretically improved timestepping restrictions over schemes appearing in the recent literature. 1.
The Optimization Test Environment
"... Testing is a crucial part of software development in general, and hence also in mathematical programming. Unfortunately, it is often a time consuming and little exciting activity. This naturally motivated us to increase the e ciency in testing solvers for optimization problems and to automatize as m ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Testing is a crucial part of software development in general, and hence also in mathematical programming. Unfortunately, it is often a time consuming and little exciting activity. This naturally motivated us to increase the e ciency in testing solvers for optimization problems and to automatize as much of the procedure as possible. Keywords: test environment, optimization, solver benchmarking, solver comparison The testing procedure typically consists of three basic tasks: a) organize test problem sets, also called test libraries; b) solve selected test problems with selected solvers; c) analyze, check and compare the results. The Test Environment is a graphical user interface (GUI) that enables to manage the tasks a) and b) interactively, and task c) automatically. The Test Environment is particularly designed for users who seek to 1. adjust solver parameters, or 2. compare solvers on single problems, or 3. evaluate solvers on suitable test sets.
Cuts for conic mixedinteger programming
 FORTHCOMING IN PROCEEDINGS OF IPCO 2007
, 2007
"... A conic integer program is an integer programming problem with conic constraints. Conic integer programming has important applications in finance, engineering, statistical learning, and probabilistic integer programming. Here we study mixedinteger sets defined by secondorder conic constraints. We ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
A conic integer program is an integer programming problem with conic constraints. Conic integer programming has important applications in finance, engineering, statistical learning, and probabilistic integer programming. Here we study mixedinteger sets defined by secondorder conic constraints. We describe generalpurpose conic mixedinteger rounding cuts based on polyhedral conic substructures of secondorder conic sets. These cuts can be readily incorporated in branchandbound algorithms that solve continuous conic programming relaxations at the nodes of the search tree. Our preliminary computational experiments with the new cuts show that they are quite effective in reducing the integrality gap of continuous relaxations of conic mixedinteger programs.