Performance Modeling and Optimization of Resource Allocation in Cloud Computing Systems

Cloud computing offers on-demand network access to the computing resources through virtualization. This paradigm shifts the computer resources to the cloud, which results in cost savings as the users leasing instead of owning these resources. Clouds will also provide power constrained mobile users accessibility to the computing resources. In this thesis, we develop performance models of these systems and optimization of their resource allocation. In the performance modeling, we assume that jobs arrive to the system according to a Poisson process and they may have quite general service time distributions. Each job may consist of multiple number of tasks with each task requiring a virtual machine (VM) for its execution. The size of a job is determined by the number of its tasks, which may be a constant or a variable. In the case of constant job size, we allow different classes of jobs, with each class being determined through their arrival and service rates and number of tasks in a job. In the variable case a job generates randomly new tasks during its service time. The latter requires dynamic assignment of VMs to a job, which will be needed in providing service to mobile users. We model the systems with both constant and variable size jobs using birth-death processes. In the case of constant job size, we determined joint probability distribution of the number of jobs from each class in the system, job blocking probabilities and distribution of the utilization of resources for systems with both homogeneous and heterogeneous types of VMs. We have also analyzed tradeoffs for turning idle servers off for power saving. In the case of variable job sizes, we have determined distribution of the number of jobs in the system and average service time of a job for systems with both infinite and finite amount of resources. We have presented numerical results and any approximations are verified by simulation. The performance results may be used in the dimensioning of cloud computing centers. Next, we have developed an optimization model that determines the job schedule, which minimizes the total power consumption of a cloud computing center. It is assumed that power consumption in a computing center is due to communications and server activities. We have assumed a distributed model, where a job may be assigned VMs on different servers, referred to as fragmented service. In this model, communications among the VMs of a job on different servers is proportional to the product of the number of VMs assigned to the job on each pair of servers which results in a quadratic network power consumption in number of job fragments. Then, we have applied integer quadratic programming and the column generation method to solve the optimization problem for large scale systems in conjunction with two different algorithms to reduce the complexity and the amount of time needed to obtain the solution. In the second phase of this work, we have formulated this optimization problem as a function of discrete-time. At each discrete-time, the job load of the system consists of new arriving jobs during the present slot and unfinished jobs from the previous slots. We have developed a technique to solve this optimization problem with full, partial and no migration of the old jobs in the system. Numerical results show that this optimization results in significant operating costs savings in the cloud computing systems

Maximizing space utilization in unit-load warehouses.

In a unit-load warehouse, products are stored and retrieved in pallet quantities. Examples include retail distribution centers (DC), third-party DCs, and transshipment hubs in freight transportation. Expenses related to space are a significant component of the operational cost of unit-load warehouses; therefore, maximizing space utilization is important. Moreover, the continuing revolution of retail e-commerce is changing the role and design of modern distribution centers (Boysen et al., 2018). An important trend with serious implications for design is the desire of many distributors to locate DCs in or near metropolitan areas in order to support same-day service or better (Thuermer, 2018). Land in these areas is very expensive, so there is a need to make the best use of existing space. The ability to store more products in the same space increases inventory availability and therefore service, and the ability to store the same inventory in a smaller footprint reduces costs. In this dissertation, we propose two strategies to improve space utilization in unit-load warehouses. We aim to minimize what we called loss of vertical space within slots (LVS)—the mismatch between the height of the pallet and the height of the slot where it is stored. LVS is a significant problem because it is standard practice to design storage racks in unit-load warehouses with all slots of equal height (maximum pallet height) such that every pallet can fit in every slot; however, pallet heights vary greatly. We propose the use of storage racks with multiple slot heights so that slot heights can better match the distribution of pallet heights. We analyzed historic (forecasted) inventory levels and the pallet heights to determine a robust design that guarantees a desired storage service level. Our method addresses the new warehouse design decisions that arise when having multiple slot heights: How to arrange the different slot heights in the rack-bays? How to organize the layout? How to avoid storage shortages? How do different slot heights affect travel times? We found that using multiple slot heights in unit-load warehouses has significant benefits in terms of footprint, expected travel time, and racking cost. For a typical warehouse, we expect space savings of 25–35 percent, depending on the number of slot types, and savings of 15–25 percent in annual operating cost. Although using multiple slot heights significantly decreases the loss of vertical space within slots, it does not completely eliminate it, and in warehouses where inventory levels are highly variable or product mixes change rapidly, this wasted space can still be significant. Examples of this situation in practice include warehouses with correlated order profiles, demands with seasonal peaks, new product launches, and distribution network consolidations. For such business environments, we propose pallet racks with dynamic heights as a way to maximize space utilization. Contrary to traditional pallet racks, the uprights and beams of pallet racks with dynamic heights are equipped with a mechanism to adjust slot heights easily. We found that pallet racks with dynamic heights have expected space savings of 16–30 percent when compared to traditional pallet racks

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

Solving transportation scheduling problems : models and algorithms

Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved by developing better models and algorithms for transportation planning and scheduling. The main challenges include the mathematical modeling of operational rules, uncertainties in operations, and large-scale problem size. This dissertation addresses crew scheduling in freight railways and vehicle routing problems (VRP) for mail processing and distribution centers (P&DCs). Our goal is to develop models and algorithms that improve efficiency and reduce operating costs. In Chapter 2, we propose an optimization model to support real-time freight railway crew assignment decisions. Due to workload balance requirements and operating regulations, the optimization model is difficult to solve for realistic instances. Hence, we propose model improvements and develop effective solution techniques to find optimal or near-optimal solutions very quickly. Chapter 3 extends the freight rail crew scheduling problem by incorporating uncertainty in train arrival and departure times. We propose a stochastic programming model, but this model is solvable only the number of scenarios is small. As a consequence, we develop heuristics that use an analytical model to calculate the expected total cost of a given choice of crew deadheads. Using this cost evaluator, we develop four local search based heuristic algorithms to sequentially improve crew scheduling decisions under uncertainty. In Chapter 4, we first cluster the pickup and drop off points in mail P&DCs into zones and then minimize the number of vehicles required and the total distance traveled to meet daily transport demand. The clustering is performed with a greedy randomized adaptive search procedure, and two heuristics are developed to find solutions to the VRP, which proved intractable for realistic instances. The heuristics are optimization-based within a rolling horizon framework. An extensive analysis is undertaken to evaluate the relative performance of the two heuristics. The contributions of this dissertation include modeling, algorithmic development, computational testing, and validation using real and randomly generated data.Mechanical Engineerin

Optimal scope of supply chain network & operations design

The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are significant and that has attracted considerable research attention since the late 1990s. This doctoral thesis focuses on developing manageable and realistic optimization models for solving four contemporary and interrelated supply chain network and operations design problems. Each requires an integrated decision-making approach for advancing supply chain effectiveness and efficiency. The first model formulates the strategic robust downsizing of a global supply chain network, which requires an integrated decision-making on resource allocation and network reconfiguration, given certain financial constraints. The second model also looks at the strategic supply chain downsizing problem but extends the first model to include product portfolio selection as a downsizing decision. The third model concerns the redesign of a warranty distribution network, which requires an integrated decision-making on strategic network redesign and tactical recovery process redesign. The fourth model simultaneously determines the operational-level decisions on job assignment and process sequence in order to improve the total throughput of a production facility unit

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems