QoS-oriented sesource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.

QoS-oriented resource allocation and scheduling of multiple composite Web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most cases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinci Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species. © 2010 by the Ecological Society of America.

Short- and long-term consequences of food resources on Ural owl Strix uralensis reproduction

Life-history theory states that although natural selection would favour a maximisation of both reproductive output and life-span, such a combination can not be achieved in any living organism. According to life-history theory the reason for the fact that not all traits can be maximised simultaneously is that different traits compete with each other for resources. These relationships between traits that constrain the simultaneous evolution of two or more traits are called trade-offs. Therefore, during different life-stages an individual needs to optimise its allocation of resources to life-history components such as growth, reproduction and survival. Resource limitation acts on these traits and therefore investment in one trait, e.g. reproduction, reduces the resources available for investment in another trait, e.g. residual reproduction or survival. In this thesis I study how food resources during different stages of the breeding event affect reproductive decisions in the Ural owl (Strix uralensis) and the consequences of these decisions on parents and offspring. The Ural owl is a suitable study species for such studies in natural populations since they are long-lived, site-tenacious, and feed on voles. The vole populations in Fennoscandia fluctuate in three- to four-year cycles, which create a variable food environment for the Ural owls to cope with. The thesis gives new insight in reproductive costs and their consequences in natural animal populations with emphasis on underlying physiological mechanisms. I found that supplementary fed Ural owl parents invest supplemented food resources during breeding in own self-maintenance instead of allocating those resources to offspring growth. This investment in own maintenance instead of improving current reproduction had carry-over effects to the following year in terms of increased reproductive output. Therefore, I found evidence that reduced reproductive costs improves future reproductive performance. Furthermore, I found evidence for the underlying mechanism behind this carry-over effect of supplementary food on fecundity. The supplementary-fed parents reduced their feeding investment in the offspring compared to controls, which enabled the fed female parents to invest the surplus resources in parasite resistance. Fed female parents had lower blood parasite loads than control females and this effect lasted until the following year when also reproductive output was increased. Hence, increased investment in parasite resistance when resources are plentiful has the potential to mediate positive carry-over effects on future reproduction. I further found that this carry-over effect was only present when potentials for future reproduction were good. The thesis also provides new knowledge on resource limitation on maternal effects. I found that increased resources prior to egg laying improve the condition and health of Ural owl females and enable them to allocate more resources to reproduction than control females. These additional resources are not allocated to increase the number of offspring, but instead to improve the quality of each offspring. Fed Ural owl females increased the size of their eggs and allocated more health improving immunological components into the eggs. Furthermore, the increased egg size had long-lasting effects on offspring growth, as offspring from larger eggs were heavier at fledging. Limiting resources can have different short- and long-term consequences on reproductive decisions that affect both offspring number and quality. In long-lived organisms, such as the Ural owl, it appears to be beneficial in terms of fitness to invest in long breeding life-span instead of additional investment in current reproduction. In Ural owls, females can influence the phenotypic quality of the offspring by transferring additional resources to the eggs that can have long-lasting effects on growth.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Optimal resource allocation in conflicts with the Lanchester linear law (2,1) model

This paper presents a detailed analysis of a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts in an area fire situation. Lanchester linear law attrition model is used to develop the dynamical equations governing the variation in force strength. Here we address a static resource allocation problem namely, Time-Zero-Allocation (TZA) where the resource allocation is done only at the initial time. Numerical examples are given to support the analytical results.

Customer asset management in action: using customers portfolio for allocating resources across business-to-business relationships for improved shareholder value

Suvi Nenonen Customer asset management in action: using customer portfolios for allocating resources across business-to-business relationships for improved shareholder value Customers are crucial assets to all firms as customers are the ultimate source of all cash flows. Regardless this financial importance of customer relationships, for decades there has been a lack of suitable frameworks explaining how customer relationships contribute to the firm financial performance and how this contribution can be actively managed. In order to facilitate a better understanding of the customer asset, contemporary marketing has investigated the use of financial theories and asset management practices in the customer relationship context. Building on this, marketing academics have promoted the customer lifetime value concept as a solution for valuating and managing customer relationships for optimal financial outcomes. However, the empirical investigation of customer asset management lags behind the conceptual development steps taken. Additionally, the practitioners have not embraced the use of customer lifetime value in guiding managerial decisions - especially in the business-to-business context. The thesis points out that there are fundamental differences between customer relationships and investment instruments as investment targets, effectively eliminating the possibility to use financial theories in a customer relationships context or to optimize the customer base as a single investment portfolio. As an alternative, the thesis proposes the use of customer portfolio approach for allocating resources across the customer base for improved shareholder value. In the customer portfolio approach, the customer base of a firm is divided into multiple portfolios based on customer relationships’ potential to contribute to the shareholder value creation. After this, customer management concepts are tailored to each customer portfolio, designed to improve the shareholder value in their own respect. Therefore, effective customer asset management with the customer portfolio approach necessitates that firms are able to manage multiple parallel customer management concepts, or business models, simultaneously. The thesis is one of the first empirical studies on customer asset management, bringing empirical evidence from multiple business-to-business case studies on how customer portfolio models can be formed, how customer portfolios can be managed, and how customer asset management has contributed to the firm financial performance.

Customer asset management in action: using customers portfolio for allocating resources across business-to-business relationships for improved shareholder value

Suvi Nenonen Customer asset management in action: using customer portfolios for allocating resources across business-to-business relationships for improved shareholder value Customers are crucial assets to all firms as customers are the ultimate source of all cash flows. Regardless this financial importance of customer relationships, for decades there has been a lack of suitable frameworks explaining how customer relationships contribute to the firm financial performance and how this contribution can be actively managed. In order to facilitate a better understanding of the customer asset, contemporary marketing has investigated the use of financial theories and asset management practices in the customer relationship context. Building on this, marketing academics have promoted the customer lifetime value concept as a solution for valuating and managing customer relationships for optimal financial outcomes. However, the empirical investigation of customer asset management lags behind the conceptual development steps taken. Additionally, the practitioners have not embraced the use of customer lifetime value in guiding managerial decisions - especially in the business-to-business context. The thesis points out that there are fundamental differences between customer relationships and investment instruments as investment targets, effectively eliminating the possibility to use financial theories in a customer relationships context or to optimize the customer base as a single investment portfolio. As an alternative, the thesis proposes the use of customer portfolio approach for allocating resources across the customer base for improved shareholder value. In the customer portfolio approach, the customer base of a firm is divided into multiple portfolios based on customer relationships’ potential to contribute to the shareholder value creation. After this, customer management concepts are tailored to each customer portfolio, designed to improve the shareholder value in their own respect. Therefore, effective customer asset management with the customer portfolio approach necessitates that firms are able to manage multiple parallel customer management concepts, or business models, simultaneously. The thesis is one of the first empirical studies on customer asset management, bringing empirical evidence from multiple business-to-business case studies on how customer portfolio models can be formed, how customer portfolios can be managed, and how customer asset management has contributed to the firm financial performance.

Optimal crop planning and conjunctive use of water resources in a coastal river basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management are imperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture (rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. The models developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern for various scenarios of river flow and groundwater availability was obtained.

A Convex Optimization Framework for Almost Budget Balanced Allocation of a Divisible Good

We address the problem of allocating a single divisible good to a number of agents. The agents have concave valuation functions parameterized by a scalar type. The agents report only the type. The goal is to find allocatively efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria are of interest: the maximum ratio of budget surplus to efficient surplus, and the expected budget surplus, within the class of linear rebate functions. The goal is to minimize them. Assuming that the valuation functions are known, we show that both problems reduce to convex optimization problems, where the convex constraint sets are characterized by a continuum of half-plane constraints parameterized by the vector of reported types. We then propose a randomized relaxation of these problems by sampling constraints. The relaxed problem is a linear programming problem (LP). We then identify the number of samples needed for ``near-feasibility'' of the relaxed constraint set. Under some conditions on the valuation function, we show that value of the approximate LP is close to the optimal value. Simulation results show significant improvements of our proposed method over the Vickrey-Clarke-Groves (VCG) mechanism without rebates. In the special case of indivisible goods, the mechanisms in this paper fall back to those proposed by Moulin, by Guo and Conitzer, and by Gujar and Narahari, without any need for randomization. Extension of the proposed mechanisms to situations when the valuation functions are not known to the central planner are also discussed. Note to Practitioners-Our results will be useful in all resource allocation problems that involve gathering of information privately held by strategic users, where the utilities are any concave function of the allocations, and where the resource planner is not interested in maximizing revenue, but in efficient sharing of the resource. Such situations arise quite often in fair sharing of internet resources, fair sharing of funds across departments within the same parent organization, auctioning of public goods, etc. We study methods to achieve near budget balance by first collecting payments according to the celebrated VCG mechanism, and then returning as much of the collected money as rebates. Our focus on linear rebate functions allows for easy implementation. The resulting convex optimization problem is solved via relaxation to a randomized linear programming problem, for which several efficient solvers exist. This relaxation is enabled by constraint sampling. Keeping practitioners in mind, we identify the number of samples that assures a desired level of ``near-feasibility'' with the desired confidence level. Our methodology will occasionally require subsidy from outside the system. We however demonstrate via simulation that, if the mechanism is repeated several times over independent instances, then past surplus can support the subsidy requirements. We also extend our results to situations where the strategic users' utility functions are not known to the allocating entity, a common situation in the context of internet users and other problems.

Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks

We consider a network in which several service providers offer wireless access to their respective subscribed customers through potentially multihop routes. If providers cooperate by jointly deploying and pooling their resources, such as spectrum and infrastructure (e.g., base stations) and agree to serve each others' customers, their aggregate payoffs, and individual shares, may substantially increase through opportunistic utilization of resources. The potential of such cooperation can, however, be realized only if each provider intelligently determines with whom it would cooperate, when it would cooperate, and how it would deploy and share its resources during such cooperation. Also, developing a rational basis for sharing the aggregate payoffs is imperative for the stability of the coalitions. We model such cooperation using the theory of transferable payoff coalitional games. We show that the optimum cooperation strategy, which involves the acquisition, deployment, and allocation of the channels and base stations (to customers), can be computed as the solution of a concave or an integer optimization. We next show that the grand coalition is stable in many different settings, i.e., if all providers cooperate, there is always an operating point that maximizes the providers' aggregate payoff, while offering each a share that removes any incentive to split from the coalition. The optimal cooperation strategy and the stabilizing payoff shares can be obtained in polynomial time by respectively solving the primals and the duals of the above optimizations, using distributed computations and limited exchange of confidential information among the providers. Numerical evaluations reveal that cooperation substantially enhances individual providers' payoffs under the optimal cooperation strategy and several different payoff sharing rules.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

Elastic Resources Framework in IaaS, preserving performance SLAs

Elasticity in cloud systems provides the flexibility to acquire and relinquish computing resources on demand. However, in current virtualized systems resource allocation is mostly static. Resources are allocated during VM instantiation and any change in workload leading to significant increase or decrease in resources is handled by VM migration. Hence, cloud users tend to characterize their workloads at a coarse grained level which potentially leads to under-utilized VM resources or under performing application. A more flexible and adaptive resource allocation mechanism would benefit variable workloads, such as those characterized by web servers. In this paper, we present an elastic resources framework for IaaS cloud layer that addresses this need. The framework provisions for application workload forecasting engine, that predicts at run-time the expected demand, which is input to the resource manager to modulate resource allocation based on the predicted demand. Based on the prediction errors, resources can be over-allocated or under-allocated as compared to the actual demand made by the application. Over-allocation leads to unused resources and under allocation could cause under performance. To strike a good trade-off between over-allocation and under-performance we derive an excess cost model. In this model excess resources allocated are captured as over-allocation cost and under-allocation is captured as a penalty cost for violating application service level agreement (SLA). Confidence interval for predicted workload is used to minimize this excess cost with minimal effect on SLA violations. An example case-study for an academic institute web server workload is presented. Using the confidence interval to minimize excess cost, we achieve significant reduction in resource allocation requirement while restricting application SLA violations to below 2-3%.

Study on Water Resources Regulation and Reservoir Operating Prediction for Suppression of Salt Water Intrusion in the Pearl River Estuary

Salt water intrusion occurred frequently during dry season in Modaomen waterway of the Pearl River Estuary. With the development of region's economy and urbanization, the salt tides affect the region's water supply more and more seriously in recent years. Regulation and allocation of freshwater resources of the upper rivers of the estuary to suppress the salt tides is becoming important measures for ensuring the water supply security of the region in dry season. The observation data analysis showed that the flow value at the Wuzhou hydrometric station on the upper Xijiang river had a good correlation with the salinity in Modaomen estuary. Thus the flow rate of Wuzhou has been used as a control variable for suppression of salt tides in Modaomen estuary. However, the runoff at Wuzhou mainly comes from the discharge of Longtan reservoir on the upper reaches of Xijiang river and the runoff in the interval open valley between Longtan and Wuzhou sections. As the long distance and many tributaries as well as the large non-controlled watershed between this two sections, the reservoir water scheduling has a need for reasonable considering of interaction between the reservoir regulating discharge and the runoff process of the interval open watershed while the deployment of suppression flow at Wuzhou requires longer lasting time and high precision for the salt tide cycles. For this purpose, this study established a runoff model for Longtan - Wuzhou interval drainage area and by model calculations and observation data analysis, helped to understand the response patterns of the flow rate at Wuzhou to the water discharge of Longtan under the interval water basin runoff participating conditions. On this basis, further discussions were taken on prediction methods of Longtan reservoir discharge scheduling scheme for saline intrusion suppression and provided scientific and typical implementation programs for effective suppression flow process at the Wuzhou section.