UDRF: Multi-resource Fairness for Complex Jobs with Placement Constraints

In this paper, we study the problem of multi- resource fairness in systems running complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements. Because of special demands on particular hardware or software, tasks may have placement constraints limiting the type of machines they can run on. We develop User-Dependence Dominant Resource Fairness (UDRF), a generalized version of max-min fairness that combines graph theory and the notion of dominant re- source shares to ensure multi-resource fairness between complex workflows. UDRF satisfies several desirable properties including strategy proofness, which ensures that users do not benefit from misreporting their true resource demands. We propose an offline algorithm that computes optimal UDRF allocation. But optimality comes at a cost, especially for systems where schedulers need to make thousands of online scheduling decisions per second. Therefore, we develop a lightweight online algorithm that closely approximates UDRF. Besides that, we propose a simple mechanism to decentralize the UDRF scheduling process across multiple schedulers. Large-scale simulations driven by Google cluster-usage traces show that UDRF achieves better resource utilization and throughput compared to the current state-of-the-art in fair resource allocation

Scheduling Self-Suspending Tasks: New and Old Results

In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community. This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art

UDRF: Multi-Resource Fairness for Complex Jobs with Placement Constraints

© 2015 IEEE.In this paper, we study the problem of multi-resource fairness in systems with multiple users. Each user requires to run one or more complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements. Because of special demands on particular hardware or software, tasks can have placement constraints limiting the type of machines they can run on. We develop User-Dependence Dominant Resource Fairness (UDRF), a generalized version of max-min fairness that combines graph theory and the notion of dominant resource shares to ensure multi- resource fairness between users with complex jobs. UDRF satisfies several desirable properties including strategy proofness, which ensures that users do not benefit from misreporting their true resource demands. We propose an offline algorithm that computes optimal UDRF allocation while the scheduling process can be to be decentralize across multiple schedulers. But optimality comes at a cost, especially for systems where schedulers need to make thousands of online scheduling decisions per second. Therefore, we develop a lightweight online algorithm that closely approximates UDRF. Large-scale simulations driven by Google cluster- usage traces show that UDRF achieves better resource utilization and throughput compared to the current state-of-the-art in multi-resource fair allocation

ABSTRACT Production scheduling is an important aspect to support the success of a manufacturing company in achieving the target of production planning and affects the implementation of the production process over a period of time. The amount of idle time in scheduling are considered less effective in the production line and detailed production scheduling becomes a problem to be solved with rearrangment of the daily activities in PT. MDS. Modeling proper scheduling is a means to help develop the daily production schedule. Preliminary research results indicate that modeling of production scheduling in PT. MDS are influenced by demand of products, stock products, number of production, downtime of machine cause of change over time, the order of production scheduling, transportation delay between machines, and working time of machine. The selection of the method of calculation in the preparation of scheduling models must be adapted to the conditions preliminary research to give solutions of scheduling problems in PT. MDS. A Critical ratio method, the next grouping of products, and re-sequencing of production process with Shortest Processing Time method is the right method to re-sequencing of scheduling models in response to a scheduling problem in PT. MDS. A Critical ratio method is useful to sort the product type, while the grouping of products and the Shortest Processing Time method useful to maximize the use of equipments as well as reducing of idle time. The results showed a decreased machine utilization of 64% to 60% and downtime of machine decreased to 5%. Key words : utilization of machine, idle time, production sequence, critical ratio, shortest processing time, productivity

ABSTRACT Production scheduling is an important aspect to support the success of a manufacturing company in achieving the target of production planning and affects the implementation of the production process over a period of time. The amount of idle time in scheduling are considered less effective in the production line and detailed production scheduling becomes a problem to be solved with rearrangment of the daily activities in PT. MDS. Modeling proper scheduling is a means to help develop the daily production schedule. Preliminary research results indicate that modeling of production scheduling in PT. MDS are influenced by demand  of products, stock products, number of production, downtime of machine cause of change over time, the order of production  scheduling, transportation delay between machines, and working time of machine. The selection of the method of calculation in the preparation of scheduling models must be adapted to the conditions preliminary research to give solutions of  scheduling problems in PT. MDS. A  Critical ratio method, the next grouping of products, and  re-sequencing of production process with Shortest Processing Time method is the right method to re-sequencing of scheduling models in response to a scheduling problem in PT. MDS. A Critical ratio method is useful to sort the product type, while the grouping of products and the Shortest Processing Time method useful to maximize the use of equipments as well as reducing of idle time. The results showed a decreased machine utilization of 64% to 60% and downtime of machine decreased to 5%.                                                                                                                Key words : utilization of machine, idle time, production sequence, critical ratio, shortest processing time,               productivit

Algorithms and complexity analyses for some combinational optimization problems

The main focus of this dissertation is on classical combinatorial optimization problems in two important areas: scheduling and network design. In the area of scheduling, the main interest is in problems in the master-slave model. In this model, each machine is either a master machine or a slave machine. Each job is associated with a preprocessing task, a slave task and a postprocessing task that must be executed in this order. Each slave task has a dedicated slave machine. All the preprocessing and postprocessing tasks share a single master machine or the same set of master machines. A job may also have an arbitrary release time before which the preprocessing task is not available to be processed. The main objective in this dissertation is to minimize the total completion time or the makespan. Both the complexity and algorithmic issues of these problems are considered. It is shown that the problem of minimizing the total completion time is strongly NP-hard even under severe constraints. Various efficient algorithms are designed to minimize the total completion time under various scenarios. In the area of network design, the survivable network design problems are studied first. The input for this problem is an undirected graph G = (V, E), a non-negative cost for each edge, and a nonnegative connectivity requirement ruv for every (unordered) pair of vertices &ruv. The goal is to find a minimum-cost subgraph in which each pair of vertices u,v is joined by at least ruv edge (vertex)-disjoint paths. A Polynomial Time Approximation Scheme (PTAS) is designed for the problem when the graph is Euclidean and the connectivity requirement of any point is at most 2. PTASs or Quasi-PTASs are also designed for 2-edge-connectivity problem and biconnectivity problem and their variations in unweighted or weighted planar graphs. Next, the problem of constructing geometric fault-tolerant spanners with low cost and bounded maximum degree is considered. The first result shows that there is a greedy algorithm which constructs fault-tolerant spanners having asymptotically optimal bounds for both the maximum degree and the total cost at the same time. Then an efficient algorithm is developed which finds fault-tolerant spanners with asymptotically optimal bound for the maximum degree and almost optimal bound for the total cost

Minimizing Mean Flowtime and Makespan on Master-Slave Systems

The master-slave scheduling model is a new model recently introduced by Sahni. It has many important applications in parallel computer scheduling and industrial settings such as semiconductor testing, machine scheduling, etc. In this model each job is associated with a preprocessing task, a slave task and a postprocessing task that must be executed in this order. While the preprocessing and postprocessing tasks are scheduled on the master machine, the slave tasks are scheduled on the slave machines. In this paper we consider scheduling problems on single-master masterslave systems. We first strengthen some previously known complexity results for makespan problems, by showing them to be strongly NP-hard. We then show that the problem of minimizing the mean flowtime is strongly NP-hard even under severe constraints. Finally, we propose some heuristics for the mean flowtime and makespan problems subject to some constraints, and we analyze the worst-case performance of these heuristics