Task-level Scheduling Research Articles

Simultaneous project portfolio selection and scheduling from contractor perspective

Optimum project portfolio selection is one of the critical topics in project management. Scheduling selected projects to maximize profit and making project portfolio execution feasible are essential issues in this topic. This research presents a comprehensive model for simultaneous project portfolio selection and scheduling while maximizing the project portfolio’s net present value (PPNPV) and minimizing its resource fluctuations (RF). The presented study considers resource constraints and integrates the selection and scheduling process to form the portfolio with the highest efficiency among different possible solutions from the contractor’s perspective in construction management. The optimum project portfolio with its schedule, resource usage, cash flow and profit are outputted as the results. The developed model has been utilized on six hypothetical projects with diverse task numbers and networks containing all kinds of relations, lags and leads. The results presented an efficient point between fulfiling both goal functions by increasing 8.2% PPNPV and decreasing 8.06% and 13.76% of RF parameters for both resources, respectively. Moreover, the model’s superiority is demonstrated by comparing the results with the existing literature. The model is validated by considering different scenarios and comparing the results with what is expected. Considering task level scheduling, all four types of task precedence relation types and task lags lead to more realistic project networks, therefore, modelling the projects’ situations more appropriately. In addition, contractor’s resource sharing policies are engaged in this study to model contractor’s financial situation more realistically. This model views project portfolio selection and scheduling form contractor’s perspective, therefore, it lets contractors to benefit from using detailed scheduling and modelling contractor’s resource sharing policies. It is demonstrated that the presented model allows contractors to form an optimum project portfolio while considering financial and resource constraints.

Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

Joint Scheduling of Tasks and Network Flows in Big Data Clusters

As an increasing number of big data processing platforms like Hadoop, Spark, and Storm appear and normally share the resources in the data center, it has been important and challenging to schedule various jobs from these platforms onto the underlying data center resources such that the overall job completion time is minimized. To solve the problem, the existing work either focus on the task-level scheduling techniques, such as Quincy and delay scheduling, or focus on the network flow scheduling techniques, such as D3 and preemptive distributed quick. These works deal with the scheduling of tasks and network flows separately and cannot achieve optimal performance. The reason is that the task scheduling without regard of the available network bandwidths may generate the task placement that causes serious network congestions and thus leads to long data transmission time. In this paper, we propose the joint scheduling technique by coordinating the task placement and the scheduling of network flows arising from these tasks. We develop a software-defined network (SDN)-based online scheduling framework which selects the task placement based on the available bandwidth on the SDN switches and at meanwhile optimally allocates the bandwidth to each data flow. Comprehensive trace-driven simulations show that the joint scheduling technique can take full use of the network bandwidth and thus reduce the job completion time by 55% on average compared with the benchmark methods.

Profit-aware scheduling in task-level for datacenter networks

Applications perform massive and diverse tasks in data centers. Tasks completion condition seriously affects application performance. However, most existing flow-level or task-level scheduling methods treat flows in isolation, meanwhile, few works discuss the efficiency of task-level scheduling from the perspective of the task profit.In this paper, we introduce a profit-aware task-level scheduling scheme named PAT, whose target is to maximize the profit of completing tasks within their reasonable time. To this end, a maximizing profit optimization model is proposed on task-level, and an efficient approximate scheduling algorithm is presented. Furthermore, a situation of absent deadline information is discussed and an ePAT method is presented to solve this situation. Based on the proposed algorithm, we design and implement PAT and ePAT. Some comprehensive experiments are conducted to evaluate the performance of our methods. The experimental results show that our methods bring higher profit than other scheduling methods.

A Review: Cloud Computing using Various Task Scheduling Algorithms

Cloud Computing discusses to the use of computing, infrastructure, software, as a service. It’s a procedure of utility computing where the consumer need not own the essential infrastructure and pay for only what they use. Computing resources are transported as virtual machines. Proficient task scheduling apparatus can meet users' necessities, and advance the resource utilization, thereby improving the overall performance of the cloud computing environment. The role of task level scheduling in power organisation on multicore multiple voltage embedded systems. Task scheduling algorithms play an imperative role where the aim is to schedule the tasks efficiently so as to condense the improvement time and advance resource utilization. This paper represents two scheduling algorithms for scheduling tasks taking into attention their computational complexity and computing capacity of processing basics. Prior’s comparison studies have recurrently operated with shortening assumptions, such as independent tasks, affectedly generated difficulties or the assumption of zero communication delay.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

Budget-Driven Scheduling Algorithms for Batches of MapReduce Jobs in Heterogeneous Clouds

In this paper, we consider task-level scheduling algorithms with respect to budget and deadline constraints for a batch of MapReduce jobs on a set of provisioned heterogeneous (virtual) machines in cloud platforms. The heterogeneity is manifested in the popular “pay-as-you-go” charging model where the service machines with different performance would have different service rates. We organize the batch of jobs as a k-stage workflow and study two related optimization problems, depending on whether the constraints are on monetary budget or on scheduling length of the workflow. First, given a total monetary budget B, by combining an in-stage local greedy algorithm (whose optimality is also proven) and dynamic programming (DP) techniques, we propose a global optimal scheduling algorithm to achieve minimum scheduling length of the workflow within O(kB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Although the optimal algorithm is efficient when B is polynomially bounded by the number of tasks in the MapReduce jobs, the quadratic time complexity is still high. To improve the efficiency, we further develop two greedy algorithms, called Global Greedy Budget (GGB) and Gradual Refinement (GR), each adopting different greedy strategies. In GGB we extend the idea of the local greedy algorithm to the efficient global distribution of the budget with minimum scheduling length as a goal whilst in GR we iteratively apply the DP algorithm to the distribution of exponentially reduced budget so that the solutions are gradually refined. Second, we consider the optimization problem of minimizing cost when the (time) deadline of the computation D is fixed. We convert this problem into the standard Multiple-Choice Knapsack Problem via a parallel transformation. Our empirical studies verify the proposed optimal algorithms and show the efficiencies of the greedy algorithms in cost-effectiveness to distribute the budget for performance optimizations of the MapReduce workflows.

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.

A Heterogeneous Built-In Self-Repair Approach Using System-Level Synthesis Flexibility

Summary and Conclusions -A novel methodology is proposed for designing fault-tolerant real-time multi-processor systems-on-a-chip to achieve optimal productivity. The methodology employs the heterogeneous built-in-self-repair (BISR) based on graceful degradation and yield enhancement techniques as an embedded optimization engine. The technique exploits the flexibility provided in task-level scheduling and algorithm selection steps. A hardware fault model is developed for modern super-scalar processors and multi-processors which enables an efficient treatment of the synthesis and compilation goals. For the first time, heterogeneous BISR is used at the task level. The key idea is to adapt scheduling and algorithm selection to the available nonfaulty resources. If there is a fault in memory, the algorithms that use less memory are selected and the scheduler exploits the other abundant resource, viz, the processors, more vigorously to compensate for the loss of part of memory. Similarly, a fault in a processor is backed up by memory. The synthesis approach minimizes the degradation in performance for single or multiple faults using simulated annealing-based algorithm selection, scheduling, and assignment algorithms. On the large set of examples this adaptive algorithm selection and scheduling technique has achieved important improvement of throughput compared to conventional nonadaptive schemes. The experimental results also indicate that important improvement in productivity can be achieved by using the extra throughput gained from the technique.

MATADOR: AN EXPLORATION ENVIRONMENT FOR SYSTEM-DESIGN

We present a modular platform simulation environment to estimate the energy consumption and performance of distributed systems in a Systems-on-Chip context. We use the simulation environment to support the development of our high-level design methodologies. More in particular, we steer and verify the development of a task-level data transfer and storage methodology, the development of a task-level scheduling methodology and the development of an instruction memory management methodology. All of these methodologies are focussed on reducing the overall energy consumption of the complex dynamic system on a heterogeneous platform architecture. Compared to research in the academic and industrial context, our contribution is to integrate in a scalable way existing energy and performance simulators of the components of a heterogeneous multiprocessor SoC. Also a novel instruction memory hierarchy is included. The simulation environment consists of multiple processing nodes connected to a distributed memory hierarchy. To reduce the energy consumption of the system, both the processing nodes as well as the memory architecture can be varied: the processing voltage of each node can be tuned and the memory hierarchy can be fully customized. The integration of dynamic real-time applications on this platform is simplified by the availability of a multi-processor RTOS. The use of the simulator to develop our high-level design methodologies is illustrated on real-life multimedia applications.