Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.

A Decision Support System for Corporate Planning in the New Zealand Dairy Industry

<p>Decisions; Support; Systems; Planning; Optimization; Heuristics; Network programming; Fixed Costs; Travelling salesman; Distances; Computer graphics. A Decision Support System (DSS) is described, the prime objective of which is to aid in the location of new investments in a multi-site, multi-product dairy processing company. A network program model is described which optimises the collection of milk from farm groups (netcells) and the allocation of the milk to a range of final products and byproducts through consideration of product prices, Process costs and transport costs. Constraints include process capacities, overtime capacities, and final product demands. Site dependant product yields are considered through use of an iteration procedure surrounding the network model. This procedure updates estimates of the mean company yield used to set upper and lower arc constraints in the product demand phase of the network model. Milk tanker collection distances are estimated by an expected travelling salesman distance method in conjunction with accurately measured netcell to factory 'bridging distances' and an inter-factory diversion network of road distances. To cope with daily fixed coat charges, a heuristic Procedure employing cost relaxations and a number of Pre-solution feasibility tests is used. Seasonally varying factors (milk supply, product yield and farms visited per tanker trip) are accommodated by solving the network model for the average day in each month for twelve months, then summing the results multiplied by the number of production days in each month. Implementation as a DSS was facilitated through use of an interactive computer system incorporating computer-generated graphic displays. Applications of the DSS to location planning, industry rationalization and other corporate planning activities are described. Recommendations on the use of the model to identify the feasible set of candidates for location studies are made, and methods for identifying the appropriate timing of investments are considered.</p>

A Decision Support System for Corporate Planning in the New Zealand Dairy Industry

<p>Decisions; Support; Systems; Planning; Optimization; Heuristics; Network programming; Fixed Costs; Travelling salesman; Distances; Computer graphics. A Decision Support System (DSS) is described, the prime objective of which is to aid in the location of new investments in a multi-site, multi-product dairy processing company. A network program model is described which optimises the collection of milk from farm groups (netcells) and the allocation of the milk to a range of final products and byproducts through consideration of product prices, Process costs and transport costs. Constraints include process capacities, overtime capacities, and final product demands. Site dependant product yields are considered through use of an iteration procedure surrounding the network model. This procedure updates estimates of the mean company yield used to set upper and lower arc constraints in the product demand phase of the network model. Milk tanker collection distances are estimated by an expected travelling salesman distance method in conjunction with accurately measured netcell to factory 'bridging distances' and an inter-factory diversion network of road distances. To cope with daily fixed coat charges, a heuristic Procedure employing cost relaxations and a number of Pre-solution feasibility tests is used. Seasonally varying factors (milk supply, product yield and farms visited per tanker trip) are accommodated by solving the network model for the average day in each month for twelve months, then summing the results multiplied by the number of production days in each month. Implementation as a DSS was facilitated through use of an interactive computer system incorporating computer-generated graphic displays. Applications of the DSS to location planning, industry rationalization and other corporate planning activities are described. Recommendations on the use of the model to identify the feasible set of candidates for location studies are made, and methods for identifying the appropriate timing of investments are considered.</p>

Heuristic Methods for Minimum-Cost Pipeline Network Design – a Node Valency Transfer Metaheuristic

Designing low-cost network layouts is an essential step in planning linked infrastructure. For the case of capacitated trees, such as oil or gas pipeline networks, the cost is usually a function of both pipeline diameter (i.e. ability to carry flow or transferred capacity) and pipeline length. Even for the case of incompressible, steady flow, minimizing cost becomes particularly difficult as network topology itself dictates local flow material balances, rendering the optimization space non-linear. The combinatorial nature of potential trees requires the use of graph optimization heuristics to achieve good solutions in reasonable time. In this work we perform a comparison of known literature network optimization heuristics and metaheuristics for finding minimum-cost capacitated trees without Steiner nodes, and propose novel algorithms, including a metaheuristic based on transferring edges of high valency nodes. Our metaheuristic achieves performance above similar algorithms studied, especially for larger graphs, usually producing a significantly higher proportion of optimal solutions, while remaining in line with time-complexity of algorithms found in the literature. Data points for graph node positions and capacities are first randomly generated, and secondly obtained from the German emissions trading CO2 source registry. As political will for applications and storage for hard-to-abate industry CO2 emissions is growing, efficient network design methods become relevant for new large-scale CO2 pipeline networks.

Optimization of Multi-Quality Water Networks: Can Simple Optimization Heuristics Compete with Nonlinear Solvers?

Optimal management of water systems tends to be very complex, especially when water quality aspects are included. This paper addresses the management of multi-quality water networks over a fixed time horizon. The problem is formulated as an optimization program that minimizes cost by determining the optimal flow distribution that satisfies the water quantity and quality requirement in the demand nodes. The resulted model is nonlinear and non-convex due to bilinear terms in the mass balance equations of blending multi-quality flow. This results in several local optima, making the process of solving large-scale problems to global optimality very challenging. One classical approach to deal with this challenge is to use a multi-start procedure in which off-the-shelf local optimization solvers are initialized with several random initial points. Then the final optimal solution is considered as the lowest objective value over the different runs. This will lead to a cumbersome and slow solution process for large-scale problems. In light of the above, this study supports using ultra-fast simple optimization heuristics, which despite their moderate accuracy, can still reach the optimum solution when run many times using a multi-start procedure. As such, the final solution from simple optimization heuristics can compete with off-the-shelf nonlinear solvers in terms of accuracy and efficiency. The paper presents a simple optimization heuristic, which is specially tailored for the problem and compares its performance with a state-of-the-art nonlinear solver on large-scale systems.

Choosing the Right Algorithm With Hints From Complexity Theory

Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established optimization heuristics. We demonstrate this approach on the recently proposed DLB benchmark, for which the only known results are O(n^3) runtimes for several classic evolutionary algorithms and an O(n^2 log n) runtime for an estimation-of-distribution algorithm. Our finding that the unary unbiased black-box complexity is only O(n^2) suggests the Metropolis algorithm as an interesting candidate and we prove that it solves the DLB problem in quadratic time. Since we also prove that better runtimes cannot be obtained in the class of unary unbiased algorithms, we shift our attention to algorithms that use the information of more parents to generate new solutions. An artificial algorithm of this type having an O(n log n) runtime leads to the result that the significance-based compact genetic algorithm (sig-cGA) can solve the DLB problem also in time O(n log n). Our experiments show a remarkably good performance of the Metropolis algorithm, clearly the best of all algorithms regarded for reasonable problem sizes.