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Multiagent Scheduling : Models and Algorithms
Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion. In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.
Book
Introduction to Scheduling
Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material. The text introduces methods for solving various scheduling problems, including resource-constrained project scheduling, machine scheduling, and job scheduling. It covers both the foundations in scheduling and modern developments, such as online scheduling. Along with a number of examples, theorems, and pedagogical proofs, the book provides in-depth coverage of key application fields.
eBook
Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents
Due to different heterogeneous cloud resources and diverse and complex applications of the users, an optimal task scheduling, which can satisfy users and cloud service providers with energy-saving and cost-effective use of resources, is a major issue in cloud computing. On the one hand, network users are demanding the quality assurance of their requested services, minimizing their costs, and their own data security, and on the other hand, the service providers consider less power consumption, more efficient use of resources, and optimal utilization. In dependent tasks dealing with massive data, resource scheduling is considered as an important challenge. Due to the time limitation of online scheduling process of dependent tasks, many existing methods of the literature are not able to guarantee the best resource utilization. In this paper, a reinforcement learning approach is exploited in a multi-agent system for task scheduling and resource provisioning, in order to reduce the makespan, minimize the required power, optimize the cost of using the resources, and maximize the utilization of the resources (considering their expiration time), simultaneously. The proposed algorithm has two phases. In the first phase, the tasks are scheduled using reinforcement learning techniques, and in the second one, considering the information obtained from the scheduling phase, resources are allocated in a multi-agent environment. The results of experiments show that this method improves the efficiency of the use of resources and reduces their costs. Moreover, the expiration time of the tasks is observed and the total execution time and energy consumption will be significantly reduced.
Journal Article
A study of performance and power of Java Virtual machines and Garbage Collectors
With CPUs dominating the power consumption of a datacenter, improving energy efficiency is a primary design goal for current processors, servers, and even datacenters. Power management schemes such as clock and power gating have been proposed. These schemes put the processor in different sleep states depending on the frequency and intensity that the program requires. Existing power management solutions that focus on predicting which idle or active state the processor should be running at a certain time are all hardware solutions. Our research focuses on how software, specifically the Java Virtual Machine (JVM), can help the power management system predict which state to put the processor on in a more power efficient manner. This dissertation is a start towards achieving this goal. It will begin presenting a tool for measuring power consumed by a JVM which will be used to provide useful data to come up with conclusions. This tool assisted in analyzing which type of programs consume the most power and which JVM is the most efficient. The later section of this dissertation focuses on how using different garbage collectors while running different types of workloads can affect both power and performance.
Dissertation
Decision support system for appointment scheduling and overbooking under patient no-show behavior
Data availability enables clinics to use predictive analytics to improve appointment scheduling and overbooking decisions based on the predicted likelihood of patients missing their appointment (no-shows). Analyzing data using machine learning can uncover hidden patterns and provide valuable business insights to devise new business models to better meet consumers’ needs and seek a competitive advantage in healthcare. The innovative application of machine learning and analytics can significantly increase the operational efficiency of online scheduling. This study offers an intelligent, yet explainable, analytics framework in scheduling systems for primary-care clinics considering individual patients’ no-show rates that may vary for each appointment day and time while generating appointment and overbooking decisions. We use the predicted individual no-show rates in two ways: (1) a probability-based greedy approach to schedule patients in time slots with the lowest no-show likelihood, and (2) marginal analysis to identify the number of overbookings based on the no-show probabilities of the regularly-scheduled patients. We find that the summary measures of profit and cost are considerably improved with the proposed scheduling approach as well as an increase in the number of patients served due to a substantial decrease in the no-show rate. Sensitivity analysis confirms the effectiveness of the proposed dynamic scheduling framework even further.
Journal Article
QoS-aware online scheduling of multiple workflows under task execution time uncertainty in clouds
Cloud computing, with elasticity and pay-as-you-go pricing, is a suitable platform for executing workflow applications. Workflow as a Service (WaaS) systems provide scientists with an easy-to-use, and cost-effective platform to execute their workflow applications in the cloud at any time or location worldwide. Quality of Service (QoS) is recognized as a key requirement in WaaS. Monetary cost and time are two primary QoS from a clients’ perspective; whereas, energy consumption is considered a significant problem for cloud providers’ profitability and ability to provide low-cost services. Most workflow scheduling studies assume that workflow tasks have a deterministic Execution Time (ET), which is generally unrealistic in the real world. However, there are few approaches for scheduling in WaaS considering deadlines, and monetary costs with uncertain task ET. These studies typically assume that a cloud resource can execute all types of workflow applications without any need for additional software components. However, using containers is a suitable solution to provide an executable environment for the execution of any workflow type on cloud resources. To this end, we present two cost and energy-aware workflow scheduling that consider the uncertainty in tasks’ ETs. Both solutions are designed for WaaS, leveraging containers to enhance resource utilization rate and reduce energy consumption, resource monetary cost, and workflows deadline violations. Simulated experiments demonstrate that our proposed methods outperform two recent state-of-the-art scheduling algorithms in terms of success rate, monetary cost, energy consumption, and resource utilization rate.
Journal Article
Energy-efficient DAG scheduling with DVFS for cloud data centers
With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption.
Journal Article
Optimized Cloud Resource Management and Scheduling
Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources in cloud data centers supporting scientific, industrial, business, and consumer applications.
eBook
Online scheduling to minimize maximum weighted flow-time on a bounded parallel-batch machine
An online scheduling problem on a bounded parallel-batch machine to minimize the maximum weighted flow-time is considered in this paper. Jobs arrive over time with the identical processing time. The maximum ratio between the weights of any two jobs is w. The parallel-batch machine can process at most b jobs simultaneously as a batch, and the jobs in a batch have the same starting time and the same completion time. For this problem, a deterministic online algorithm is presented. The algorithm is showed to be the best possible with a competitive ratio of 4w+1+12 when w∈[1,2], and to have a competitive ratio not greater than w when w∈(2,+∞).
Journal Article
Online scheduling on an unbounded parallel-batch machine to minimize the weighted makespan
In this paper we study the online over-time scheduling on an unbounded parallel-batch machine to minimize the weighted makespan. First, we show that the general problem has a low bound 2 and then design a 4-competitive online algorithm. Furthermore, we consider a special case in which the jobs have agreeable processing times and weights. When all jobs have the same weights (the task is to minimize the makespan), an online algorithm with the best possible competitive ratio
5
+
1
2
≈
1.618
has been established in the literature. We show that, after a slightly modification, this known online algorithm also has the best possible competitive ratio
5
+
1
2
≈
1.618
for our problem. Finally, we introduce limited restarts into the above special case and present an online algorithm with a better competitive ratio
11
7
≈
1.571
.
Journal Article