Heuristic Scheduling Algorithm Research Articles

Cost-aware DAG scheduling algorithms for minimizing execution cost on cloud resources

Directed acyclic graph (DAG) scheduling is a well-known problem, because a DAG can be used to describe a wide range of complex applications, including scientific applications and parallel computing jobs. Most DAG scheduling algorithms were proposed to minimize the job makespan (i.e., execution time) on a multiprocessor computer or cluster. However, as the cost-driven public cloud services have become an attractive and popular platform for providing computing resources, cost minimization emerges as a new critical issue. Therefore, the objective of this work is to propose and solve the cost optimization problem for scheduling DAGs on an IaaS cloud platform where task scheduling must cope with resource provisioning to achieve the optimal solution. In this paper, we proposed both optimal and heuristic scheduling algorithms, and we evaluated them across a variety of DAGs using the price model from EC2. Comparing to other cost-oblivious DAG schedules that aim to minimize makespan or resource usage, the results show that our cost-aware heuristic algorithm can reduce cost by 20---50 % and achieve a cost within x1.16 of the optimal one.

Heuristic Algorithm for Proportional Fair Scheduling in D2D-Cellular Systems

The effective reuse of the radio resources in D2D communication underlaying cellular system is pivotal for enhancing the total system capacity. Developing practical and effective scheduling schemes is critical to the success of D2D-based cellular systems. An optimal proportionally fair (PF) scheduling scheme that maximizes the logarithmic sum of the user data rates is proposed in this paper. Considering the fact that the computational complexity of the optimal PF scheduling is significantly high, low-complexity heuristic algorithm has also been proposed. It has been shown that the heuristic algorithm achieves high logarithmic sum of the average user data rates while maintaining low-computational complexity. Simulation results are used to verify the performance of the proposed algorithm, and to compare it with the optimal PF scheduling algorithms.

Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint: Heuristic approaches

Job selection and scheduling are among the most important decisions for production planning in today's manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs' processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time

QoS based Task Rescheduling in Computational Grid Environment

Grid computing is an emerging field for the next generation of parallel distributed computing platform for solving large scale computational and data intensive problems. In general, the grid users need not be aware of the computational resources that are used for executing their applications and storing their data. In grid environment, all the tasks are not possible to run in all the resources. A challenging task in grid computing is to ensure the guaranteed Quality of Service (QoS) in grid scheduling. Thus the implementations of QoS based algorithms are necessary. The main objective of grid scheduling is to find a feasible schedule that minimizes the makespan. In recent times, QoS based heuristic grid scheduling algorithms have become a vibrant research topic. So, in this paper, we proposed a QoS based Task Rescheduling algorithm (QTR), in which the scheduled but not executed tasks are collected from the heuristic algorithms and rescheduled them using Minimum Completion Time (MCT) algorithm. The simulation model with twelve benchmark instances has been used for testing the performance of the proposed algorithm. The results of the computations show that the QTR algorithm can produce solutions with reduced makespan compared to the existing approaches.

Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.

Exploiting Replication for Energy-Aware Scheduling in Disk Storage Systems

This paper deals with the problem of scheduling requests on disks for minimizing energy consumption. We first analyze several versions of the energy-aware disk scheduling problem based on assumptions on the arrival pattern of the requests. We show that the corresponding optimization problems are NP-complete. Then both optimal and heuristic scheduling algorithms are proposed to maximize the energy saving of a storage system. We evaluate our approach using multiple realistic I/O traces, disk simulator and energy model. The results show that we significantly reduce energy consumption up to 55 percent and achieve fewer disk spin-up/down operations and shorter request response time as compared to other approaches. Since our approach attempts to dynamically assign each request to an energy optimized location, it can also benefit from other traditional static or semi-static solutions that rely on data placement or migration. Finally, we show that a write offloading technique can also be adapted into our solution to minimize the impact from write re quests in terms of energy consumption and request response time.

Scheduling in Successive Interference Cancellation Based Wireless Ad Hoc Networks

Successive Interference Cancellation (SIC) allows multiple transmissions in the same neighborhood by enabling both concurrent reception and interference rejection via decoding and subtracting the signals successively from the composite received signal. In this letter, we study the scheduling problem for minimizing the schedule length required to satisfy the traffic demands of the links in SIC based wireless ad hoc networks. Upon proving the NP-hardness of the problem, we propose a novel efficient heuristic scheduling algorithm based on the greedy assignment of the links to each time slot by using a novel metric called Interference Effect (IE). The IE of a feasible link is defined as the total Signal-to-Interference-plus-Noise Ratio (SINR) drop of the links in the scheduled set with the addition of that link. We demonstrate via extensive simulations that the proposed algorithm performs better than the previous algorithms, with lower computational complexity.

Heuristic Grid Resource Scheduling Algorithm based on Group of Task and Secondary Distribution

Grid resource scheduling policies directly affect the performance of the grid, so the grid resource scheduling algorithm for grid research has become a hot spot. In this paper, based on grid heterogeneity to resource scheduling of load balancing, effective resource utilization and minimize task sets the span of time (Makespan) as the goal, propose a heuristic grid resource scheduling algorithm based on Group of Task and Secondary Distribution, the basic idea is dividing the set of tasks into two groups by using the standard deviation, the one is long tasks group, another is short tasks group .The longest task in long tasks group will be allocated to the worst performance to execute, the rest can be done in the same manner. Tasks in short tasks group will be executed by the Min-Min algorithm , and then through secondary dispatch ideas to make the grid system to quickly reach each resource load balancing , improving resource utilization efficiency and minimizing the task set time span. Finally, the simulation model benchmark Braun et al. demonstrates the effectiveness of the algorithm.

Low-time complexity budget–deadline constrained workflow scheduling on heterogeneous resources

The execution of scientific applications, under the utility computing model, is constrained to Quality of Service (QoS) parameters. Commonly, applications have time and cost constraints such that all tasks of an application need to be finished within a user-specified Deadline and Budget. Several algorithms have been proposed for multiple QoS workflow scheduling, but most of them use search-based strategies that generally have a high time complexity, making them less useful in realistic scenarios. In this paper, we present a heuristic scheduling algorithm with quadratic time complexity that considers two important constraints for QoS-based workflow scheduling, time and cost, named Deadline–Budget Constrained Scheduling (DBCS). From the deadline and budget defined by the user, the DBCS algorithm finds a feasible solution that accomplishes both constraints with a success rate similar to other state-of-the-art search-based algorithms in terms of the successful rate of feasible solutions, consuming in the worst case only approximately 4% of the time. The DBCS algorithm has a low-time complexity of O(n2.p) for n tasks and p processors.

Energy and Delay Constrained Maximum Adaptive Schedule for Wireless Networked Control Systems

Communication system design for wireless networked control systems (WNCSs) is very challenging since the strict timing and reliability requirements of control systems should be met by the wireless communication systems that introduce non-zero packet error probability and non-zero delay at all times. Particularly, the scheduling algorithms for WNCSs should be designed to provide maximum level of adaptivity accommodating packet losses and changes in network topology while exploiting periodic nature of the sensor node transmissions. Creating such a schedule has been previously studied for an Ultra Wide Band (UWB) based WNCS. In this paper, we extend the joint optimization problem of power control, rate adaptation and scheduling with the objective of providing maximum adaptivity for general WNCSs employing continuous rate transmission model in which Shannon's channel capacity formulation is used for the achievable transmission rate. Upon proving the NP-hardness of the problem, we provide a framework for the design of a heuristic algorithm for scheduling and propose an optimal polynomial time algorithm for the power control and rate adaptation problem following the derivation of the optimality conditions. We demonstrate via extensive simulations that the proposed algorithms outperform the existing algorithms with performance close to optimal solution and average runtime admissible for practical WNCSs.

An Optimal Scheduling Algorithm for Maximizing Throughput in WiMAX Mesh Networks

The WiMAX mesh network (WMN) architecture is defined in the IEEE 802.16 standard for increasing network coverage and improving communication performance. In the past few years, several greedy or heuristic algorithms have been proposed to cope with the scheduling problem in WMNs. However, their performance highly depends on the network topology and bandwidth requests, and they do not achieve optimal performance in all cases. This paper proposes an optimal scheduling algorithm called the scheduling algorithm with dynamic programming approach (SADP), which exploits the opportunities of spatial reuse and maximizes the network throughput based on the network topology and the uplink bandwidth requests of each subscriber station. In addition, a heuristic scheduling algorithm (HSA) is proposed to reduce the computing complexity. The performance results were approximate to the optimal results. The simulation study reveals that the proposed SADP provides the WMN with maximal throughput and shortest transmission time, and the proposed HSA likely achieves the optimal results.

An Ant Colony-based Heuristic Algorithm for Joint Scheduling of Post-earthquake Road Repair and Relief Distribution

Emergency road repair and distribution of relief goods are crucial for post-earthquake response. However, interrelationships between those two tasks are not adequately considered in their work schedules, especially in cases with very limited repair resources, leading to unnecessary delay and expenditure. A time-space network model is constructed to better describe the constraints arising from the interrelationships in joint scheduling of road repair and relief distribution works. An ant colony-based heuristic algorithm is developed to solve the NP-hard model efficiently for practical use, followed by a case study of Wenchuan earthquake to validate the planning tool and to demonstrate its feasibility for resolving real world problem.

Energy efficient virtual network embedding for green data centers using data center topology and future migration

With the rapid proliferation of data centers, their energy consumption and greenhouse gas emissions have significantly increased. Some efforts have been made to control and lower energy consumption of data centers such as, proportional energy consuming hardware, dynamic provisioning, and virtualization machine techniques. However, it is still common that many servers and network resources are often underutilized, and idle servers spend a large portion of their peak power consumption.We first build a novel model of virtual network embedding in order to minimize energy usage in data centers for both computing and network resources by taking practical factors into consideration. Due to the NP-hardness of the proposed model, we develop a heuristic algorithm for virtual network scheduling and mapping. In doing so, we specifically take the expected energy consumption at different times, virtual network operation and future migration costs, and a data center architecture into consideration. Our extensive evaluation results show that our algorithm could reduce energy consumption up to 40% and take up to a 57% higher number of virtual network requests over other existing virtual mapping schemes.

Heuristic Algorithm for Efficient Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server’s point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. The most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Cost Data Retrieval Problem (MCDR) and Large Number Data Retrieval (LNDR) Problem. We also develop a heuristic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time.

A hybrid heuristic workflow scheduling algorithm for cloud computing environments

Cloud computing is a relatively new concept in the distributed systems and is widely accepted as a new solution for high performance and distributed computing. Its dynamisms in providing virtual resources for organisations and laboratories and its pay-per-use policy make it very popular. A workflow models a process consisting of a series of steps that shape an application. Workflow scheduling is the method for assigning each workflow task to a processing resource in a way that specific workflow rules are satisfied. Some scheduling algorithms for workflows may assume some quality of service parameter such as cost and deadline. Some efforts have been done on workflow scheduling on cloud computing environments with different service level agreements. But most of them suffer from low speed. Here, we introduce a new hybrid heuristic algorithm based on particle swarm optimisation (PSO) and gravitation search algorithms. The proposed algorithm, in addition to processing cost and transfer cost, takes deadline limitations into account. The proposed workflow scheduling approach can be used by both end-users and utility providers. The CloudSim toolkit is used as a cloud environment simulator and the Amazon EC2 pricing is the reference pricing used. Our experimental result shows about 70% cost reduction, in comparison to non-heuristic implementations, 30% cost reduction in comparison to PSO, 30% cost reduction in comparison to gravitational search algorithm and 50% cost reduction in comparison to hybrid genetic-gravitational algorithm.

Heuristic Algorithms for One-Slot Link Scheduling in Wireless Sensor Networks under SINR

One-slot link scheduling is important for enhancing the throughput capacity of wireless sensor networks. It includes two aspects: maximum links scheduling (MLS) and maximum weighted links scheduling (MWLS). In this paper we propose two heuristic algorithms for the two NP-hard problems with obvious power assignments under the SINR (signal-to-interference-plus-noise-ratio) model. For MLS, we propose an algorithm MTMA (maximum tolerance and minimum affectance), which improves the currently best approximation algorithm by 28%–62% on average. For MWLS, we give an effective heuristic algorithm MWMA (maximum weighted and minimum affectance), which performs better on improving the throughput and reducing the running time. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations.

An incentive-based heuristic job scheduling algorithm for utility grids

Abstract Job scheduling in utility grids should take into account the incentives for both grid users and resource providers. However, most of existing studies on job scheduling in utility grids only address the incentive for one party, i.e., either the users or the resource providers. Very few studies on job scheduling in utility grids consider incentives for both parties, in which the cost, one of the most attractive incentives for users, is not addressed. In this paper, we study the job scheduling in utility grid by optimizing the incentives for both parties. We propose a multi-objective optimization approach, i.e., maximizing the successful execution rate of jobs and minimizing the combined cost (incentives for grid users), and minimizing the fairness deviation of profits (incentive for resource providers). The proposed multi-objective optimization approach could offer sufficient incentives for the two parties to stay and play in the utility grid. A heuristic scheduling algorithm called Cost-Greedy Price-Adjusting (CGPA) algorithm is developed to optimize the incentives for both parties. Simulation results show that the CGPA algorithm is effective and could lead to higher successful execution rate, lower combined cost and lower fairness deviation compared with some popular algorithms in most cases.

Scheduling for virtual MIMO in single carrier FDMA (SC-FDMA) system

In this paper, we consider a joint frequency-domain scheduling and user-pairing problem for virtual MIMO in the single carrier frequency division multiple access (SC-FDMA) system, e.g., the uplink transmission for third generation partnership project-long term evolution (3GPP-LTE) standard. Due to the sub-carrier adjacency constraint inherent to SC-FDMA, its complexity becomes unmanageable. We propose a greedy heuristic algorithm for PF scheduling so as to deal with the complexity issue in this joint problem. It has been shown that its performance can reach up to 90% of its upper bound.

Efficient Heuristic Algorithm for Scheduling Two-Stage Hybrid Flowshop with Sequence-Dependent Setup Times

We propose an efficient heuristic algorithm for two-stage hybrid flowshop scheduling with sequence-dependent setup times. In the past, metaheuristic approaches, which usually need long time, have been mostly used for the problem. In this study, due to practical reasons of the application that we consider, we need to obtain the solution of the problem within a reasonably short computational time, even for large-sized problems. In this study, we devise the proposed algorithm as a hybrid of two methods, that is, the beam search and NEH method, and we compare the performances with other existing local search methods. The results of the computational experiments show that the proposed algorithm solves the problems in a relatively shorter computation time, while the scheduling performances are superior to the existing methods.

A heuristic algorithm for scheduling in a flow shop environment to minimize makespa

A B S T R A C T Scheduling 'n' jobs on 'm' machines in a flow shop is NP- hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of machines at each stage to generate different combination of 'm-1' sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993) (Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.). The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer's heuristic, Campbell's CDS heuristic, and Dannenbring's rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.