A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

A resource allocation mechanism based on cost function synthesis in complex systems

While the management of resources in computer systems can greatly impact the usefulness and integrity of the system, finding an optimal solution to the management problem is unfortunately NP hard. Adding to the complexity, today\u27s \u27modern\u27 systems - such as in multimedia, medical, and military systems - may be, and often are, comprised of interacting real and non-real-time components. In addition, these systems can be driven by a host of non-functional objectives – often differing not only in nature, importance, and form, but also in dimensional units and range, and themselves interacting in complex ways. We refer to systems exhibiting such characteristics as Complex Systems (CS). We present a method for handling the multiple non-functional system objectives in CS, by addressing decomposition, quantification, and evaluation issues. Our method will result in better allocations, improve objective satisfaction, improve the overall performance of the system, and reduce cost -in a global sense. Moreover, we consider the problem of formulating the cost of an allocation driven by system objectives. We start by discussing issues and relationships among global objectives, their decomposition, and cost functions for evaluation of system objective. Then, as an example of objective and cost function development, we introduce the concept of deadline balancing. Next, we proceed by proving the existence of combining models and their underlying conditions. Then, we describe a hierarchical model for system objective function synthesis. This synthesis is performed solely for the purpose of measuring the level of objective satisfaction in a proposed hardware to software allocation, not for design of individual software modules. Then, Examples are given to show how the model applies to actual multi-objective problems. In addition the concept of deadline balancing is extended to a new scheduling concept, namely Inter-Completion-Time Scheduling (ICTS. Finally, experiments based on simulation have been conducted to capture various properties of the synthesis approach as well as ICTS. A prototype implementation of the cost functions synthesis and evaluation environment is described, highlighting the applicability and usefulness of the synthesis in realistic applications

Real-time operating system support for multicore applications

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2014Plataformas multiprocessadas atuais possuem diversos níveis da memória cache entre o processador e a memória principal para esconder a latência da hierarquia de memória. O principal objetivo da hierarquia de memória é melhorar o tempo médio de execução, ao custo da previsibilidade. O uso não controlado da hierarquia da cache pelas tarefas de tempo real impacta a estimativa dos seus piores tempos de execução, especialmente quando as tarefas de tempo real acessam os níveis da cache compartilhados. Tal acesso causa uma disputa pelas linhas da cache compartilhadas e aumenta o tempo de execução das aplicações. Além disso, essa disputa na cache compartilhada pode causar a perda de prazos, o que é intolerável em sistemas de tempo real críticos. O particionamento da memória cache compartilhada é uma técnica bastante utilizada em sistemas de tempo real multiprocessados para isolar as tarefas e melhorar a previsibilidade do sistema. Atualmente, os estudos que avaliam o particionamento da memória cache em multiprocessadores carecem de dois pontos fundamentais. Primeiro, o mecanismo de particionamento da cache é tipicamente implementado em um ambiente simulado ou em um sistema operacional de propósito geral. Consequentemente, o impacto das atividades realizados pelo núcleo do sistema operacional, tais como o tratamento de interrupções e troca de contexto, no particionamento das tarefas tende a ser negligenciado. Segundo, a avaliação é restrita a um escalonador global ou particionado, e assim não comparando o desempenho do particionamento da cache em diferentes estratégias de escalonamento. Ademais, trabalhos recentes confirmaram que aspectos da implementação do SO, tal como a estrutura de dados usada no escalonamento e os mecanismos de tratamento de interrupções, impactam a escalonabilidade das tarefas de tempo real tanto quanto os aspectos teóricos. Entretanto, tais estudos também usaram sistemas operacionais de propósito geral com extensões de tempo real, que afetamos sobre custos de tempo de execução observados e a escalonabilidade das tarefas de tempo real. Adicionalmente, os algoritmos de escalonamento tempo real para multiprocessadores atuais não consideram cenários onde tarefas de tempo real acessam as mesmas linhas da cache, o que dificulta a estimativa do pior tempo de execução. Esta pesquisa aborda os problemas supracitados com as estratégias de particionamento da cache e com os algoritmos de escalonamento tempo real multiprocessados da seguinte forma. Primeiro, uma infraestrutura de tempo real para multiprocessadores é projetada e implementada em um sistema operacional embarcado. A infraestrutura consiste em diversos algoritmos de escalonamento tempo real, tais como o EDF global e particionado, e um mecanismo de particionamento da cache usando a técnica de coloração de páginas. Segundo, é apresentada uma comparação em termos da taxa de escalonabilidade considerando o sobre custo de tempo de execução da infraestrutura criada e de um sistema operacional de propósito geral com extensões de tempo real. Em alguns casos, o EDF global considerando o sobre custo do sistema operacional embarcado possui uma melhor taxa de escalonabilidade do que o EDF particionado com o sobre custo do sistema operacional de propósito geral, mostrando claramente como diferentes sistemas operacionais influenciam os escalonadores de tempo real críticos em multiprocessadores. Terceiro, é realizada uma avaliação do impacto do particionamento da memória cache em diversos escalonadores de tempo real multiprocessados. Os resultados desta avaliação indicam que um sistema operacional "leve" não compromete as garantias de tempo real e que o particionamento da cache tem diferentes comportamentos dependendo do escalonador e do tamanho do conjunto de trabalho das tarefas. Quarto, é proposto um algoritmo de particionamento de tarefas que atribui as tarefas que compartilham partições ao mesmo processador. Os resultados mostram que essa técnica de particionamento de tarefas reduz a disputa pelas linhas da cache compartilhadas e provê garantias de tempo real para sistemas críticos. Finalmente, é proposto um escalonador de tempo real de duas fases para multiprocessadores. O escalonador usa informações coletadas durante o tempo de execução das tarefas através dos contadores de desempenho em hardware. Com base nos valores dos contadores, o escalonador detecta quando tarefas de melhor esforço o interferem com tarefas de tempo real na cache. Assim é possível impedir que tarefas de melhor esforço acessem as mesmas linhas da cache que tarefas de tempo real. O resultado desta estratégia de escalonamento é o atendimento dos prazos críticos e não críticos das tarefas de tempo real.Abstracts: Modern multicore platforms feature multiple levels of cache memory placed between the processor and main memory to hide the latency of ordinary memory systems. The primary goal of this cache hierarchy is to improve average execution time (at the cost of predictability). The uncontrolled use of the cache hierarchy by realtime tasks may impact the estimation of their worst-case execution times (WCET), specially when real-time tasks access a shared cache level, causing a contention for shared cache lines and increasing the application execution time. This contention in the shared cache may leadto deadline losses, which is intolerable particularly for hard real-time (HRT) systems. Shared cache partitioning is a well-known technique used in multicore real-time systems to isolate task workloads and to improve system predictability. Presently, the state-of-the-art studies that evaluate shared cache partitioning on multicore processors lack two key issues. First, the cache partitioning mechanism is typically implemented either in a simulated environment or in a general-purpose OS (GPOS), and so the impact of kernel activities, such as interrupt handlers and context switching, on the task partitions tend to be overlooked. Second, the evaluation is typically restricted to either a global or partitioned scheduler, thereby by falling to compare the performance of cache partitioning when tasks are scheduled by different schedulers. Furthermore, recent works have confirmed that OS implementation aspects, such as the choice of scheduling data structures and interrupt handling mechanisms, impact real-time schedulability as much as scheduling theoretic aspects. However, these studies also used real-time patches applied into GPOSes, which affects the run-time overhead observed in these works and consequently the schedulability of real-time tasks. Additionally, current multicore scheduling algorithms do not consider scenarios where real-time tasks access the same cache lines due to true or false sharing, which also impacts the WCET. This thesis addresses these aforementioned problems with cache partitioning techniques and multicore real-time scheduling algorithms as following. First, a real-time multicore support is designed and implemented on top of an embedded operating system designed from scratch. This support consists of several multicore real-time scheduling algorithms, such as global and partitioned EDF, and a cache partitioning mechanism based on page coloring. Second, it is presented a comparison in terms of schedulability ratio considering the run-time overhead of the implemented RTOS and a GPOS patched with real-time extensions. In some cases, Global-EDF considering the overhead of the RTOS is superior to Partitioned-EDF considering the overhead of the patched GPOS, which clearly shows how different OSs impact hard realtime schedulers. Third, an evaluation of the cache partitioning impacton partitioned, clustered, and global real-time schedulers is performed.The results indicate that a lightweight RTOS does not impact real-time tasks, and shared cache partitioning has different behavior depending on the scheduler and the task's working set size. Fourth, a task partitioning algorithm that assigns tasks to cores respecting their usage of cache partitions is proposed. The results show that by simply assigning tasks that shared cache partitions to the same processor, it is possible to reduce the contention for shared cache lines and to provideHRT guarantees. Finally, a two-phase multicore scheduler that provides HRT and soft real-time (SRT) guarantees is proposed. It is shown that by using information from hardware performance counters at run-time, the RTOS can detect when best-effort tasks interfere with real-time tasks in the shared cache. Then, the RTOS can prevent best effort tasks from interfering with real-time tasks. The results also show that the assignment of exclusive partitions to HRT tasks together with the two-phase multicore scheduler provides HRT and SRT guarantees, even when best-effort tasks share partitions with real-time tasks

Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

SUPPLY CHAIN SCHEDULING FOR MULTI-MACHINES AND MULTI-CUSTOMERS

Manufacturing today is no longer a single point of production activity but a chain of activities from the acquisition of raw materials to the delivery of products to customers. This chain is called supply chain. In this chain of activities, a generic pattern is: processing of goods (by manufacturers) and delivery of goods (to customers). This thesis concerns the scheduling operation for this generic supply chain. Two performance measures considered for evaluation of a particular schedule are: time and cost. Time refers to a span of the time that the manufacturer receives the request of goods from the customer to the time that the delivery tool (e.g. vehicle) is back to the manufacturer. Cost refers to the delivery cost only (as the production cost is considered as fi xed). A good schedule is thus with short time and low cost; yet the two may be in conflict. This thesis studies the algorithm for the supply chain scheduling problem to achieve a balanced short time and low cost. Three situations of the supply chain scheduling problem are considered in this thesis: (1) a single machine and multiple customers, (2) multiple machines and a single customer and (3) multiple machines and multiple customers. For each situation, di fferent vehicles characteristics and delivery patterns are considered. Properties of each problem are explored and algorithms are developed, analysed and tested (via simulation). Further, the robustness of the scheduling algorithms under uncertainty and the resilience of the scheduling algorithms under disruptions are also studied. At last a case study, about medical resources supply in an emergency situation, is conducted to illustrate how the developed algorithms can be applied to solve the practical problem. There are both technical merits and broader impacts with this thesis study. First, the problems studied are all new problems with the particular new attributes such as on-line, multiple-customers and multiple-machines, individual customer oriented, and limited capacity of delivery tools. Second, the notion of robustness and resilience to evaluate a scheduling algorithm are to the best of the author's knowledge new and may be open to a new avenue for the evaluation of any scheduling algorithm. In the domain of manufacturing and service provision in general, this thesis has provided an e ffective and effi cient tool for managing the operation of production and delivery in a situation where the demand is released without any prior knowledge (i.e., on-line demand). This situation appears in many manufacturing and service applications

Optimization Models and Algorithms for Prototype Vehicle Test Scheduling

Automotive makers conduct a series of tests at pre-production phases of each new vehicle model development program. The main goal of those tests is to ensure that the vehicle models meet all design requirements by the time they reach the production phase. These tests target different vehicle components or functions, such as powertrain systems, electrical systems, safety aspects, etc. However, one big issue is that the cost of the resources, mainly prototype vehicles, invested in the testing process is exceedingly expensive. An individual prototype vehicle can cost over 5 times its counterpart’s price in the commercial market because many of the parts and the prototype vehicles themselves are highly customized and produced in small batches. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible time-window constraints for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. What makes the problem even more difficult is that in addition to the prototype vehicle resources, there are other constrained supporting resources involved during the execution of those tests, such as testing facilities, instruments and equipment like cameras and sensors, human-power availability, etc. An efficient way to conquer the problem is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time while balancing the loads of other supporting resources. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests. In this thesis, we show how to mathematically model these test scheduling problems as optimization problems. We develop corresponding solution approaches that enable quick generation of an efficient schedule to execute all tests while respecting all constraints. Our models and algorithms save test planners’ and engineers’ time, increase q their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137071/1/yuhuishi_1.pd