Efficient Scheduling Algorithm Research Articles

Efficient Algorithm of Splitting and Merging Classes on Elective Course of College Education

It should take human intervention to adjust the number of each class after the students select elective courses on web by educational administration system in college, because the numbers are often less than the lower limit. This paper puts out an efficient scheduling algorithm to get the resolution of splitting and merging classes by linear time complexity computing on mathematical model. It introduces in detail the principle, the precondition, the procedure and the operation of splitting and merging classes, which illustrated with several charts, diagrams, pseudo-codes in key steps and some notes, and at last validated it by example. Then it could extend to similar issues, for example enrolling and distributing issue.

RETRACTED ARTICLE: A new whale optimizer for workflow scheduling in cloud computing environment

Cloud computing environments enable real time applications on virtualized resources that can be provisioned dynamically. It is one of the efficient platform service which permits to enable the various applications based on cloud infrastructure. Nowadays workflow systems become an easy and efficient task for the development of scientific applications. Efficient workflow scheduling algorithms are employed to improve the resource utilization by enhancing the cloud computing performance and to meet the users’ requirements. Many scheduling algorithms have been proposed but they are not optimal to incorporate benefits of cloud computing. In this paper a new framework are introduced as whale optimizer algorithm (WOA) which mimics the social behaviour of humpback whales and aims to maximize the work completion for meeting QoS constraints such as deadline and budget. This proposed method outperforms well when compared with other techniques and measured in terms of makespan, deadline and it is applicable for real time applications.

An Effective and Efficient Dynamic eMBMS Multicast Grouping Scheduling Algorithm in MBSFNs for Public Safety Scenarios.

The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, a more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and user deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

A multi-objective EBCO-TS algorithm for efficient task scheduling in mobile cloud computing

Cloud computing proposals a novel service to the users to permitting for consume the distant resources due to some faults faced in smart devices. This causes to the emergence of the newest computing model known as mobile cloud computing. It supports to the user that the mobile devices not require to execute the application in user device as a replacement of some jobs will be execute in distant cloud/cloudlet is named as offloading jobs which is the nearby to the device. At the similar time job scheduling is more critical commonly at both the mobile devices and the mobile cloud. To solving the task scheduling problem, we propose an EBCO-TS algorithm for priority based task scheduling with load balancing model. The proposed EBCO-TS consume the power only 85% compared to the existing methods and it shows better load balancing when the number of request increases in the cloudlet.

A multi-objective EBCO-TS algorithm for efficient task scheduling in mobile cloud computing

Cloud computing proposals a novel service to the users to permitting for consume the distant resources due to some faults faced in smart devices. This causes to the emergence of the newest computing model known as mobile cloud computing. It supports to the user that the mobile devices not require to execute the application in user device as a replacement of some jobs will be execute in distant cloud/cloudlet is named as offloading jobs which is the nearby to the device. At the similar time job scheduling is more critical commonly at both the mobile devices and the mobile cloud. To solving the task scheduling problem, we propose an EBCO-TS algorithm for priority based task scheduling with load balancing model. The proposed EBCO-TS consume the power only 85% compared to the existing methods and it shows better load balancing when the number of request increases in the cloudlet.

A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.

Power Allocation and Beam Scheduling for Multi-User Massive MIMO Secret Key Generation

Secret key generation in multi-user massive multiple-input multiple-output (MIMO) communication suffers from high computation complexity, pilot overhead, and inter-user interference caused by the large dimension of the massive MIMO channel. To address these problems, this paper proposes a novel key generation approach that exploits the beam domain channel for channel feature extraction. The proposed approach can effectively reduce the channel feature dimension and mitigate the inter-user interference, via the well-designed power allocation and beam scheduling algorithms. Specifically, the multi-user key generation model is built and the design problem of channel feature extraction matrices is formulated as a sum key rate maximization problem. To tackle this problem, we derive an upper bound of the secret key rate and prove that the upper bound maximization is a convex optimization. After solving the upper bound maximization, the base station (BS) obtains the sum beams for all the users. To allocate the selected beams to each user, an efficient beam scheduling algorithm is proposed, which converges to a locally optimal solution with low computational complexity. Numerical results illustrate that in the proposed scheme, the bit disagreement ratio (BDR) of legitimate users achieves 10 -2 , while that of the eavesdropper tends to 0.5, confirming the feasibility and security of the proposed scheme.

Fog-cloud task scheduling of energy consumption optimisation with deadline consideration

The emerging IoT introduces many new challenges that cannot be adequately addressed by the current 'cloud-only' architectures. The cooperation of the fog and cloud is considered to be a promising architecture, which efficiently handles IoT's data processing and communications requirements. However, how to schedule tasks to better adapt to IoT real-time needs and reduce the energy in the fog-cloud system is not well addressed. In this paper, we first model the energy consumption of the fog and cloud, respectively, and formulate a task scheduling problem into a constrained optimisation problem in fog-cloud computing system. Then, an efficient deadline-energy scheduling algorithm based on ant colony optimisation (DEACO) is put forward to tackle this problem, which achieves to reduce energy consumption on the condition of satisfying the task deadline. Finally, algorithms have been simulated on the extended CloudSim simulator. The experimental results have shown that our scheduling approach reduces energy more effective.

Effective Capacity Based Power Allocation for the Coexistence of an Integrated Radar and Communication System and a Commercial Communication System

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.

Cost-efficient dynamic scheduling of big data applications in apache spark on cloud

Job scheduling is one of the most crucial components in managing resources, and efficient execution of big data applications. Specifically, scheduling jobs in a cloud-deployed cluster are challenging as the cloud offers different types of Virtual Machines (VMs) and jobs can be heterogeneous. The default big data processing framework schedulers fail to reduce the cost of VM usages in the cloud environment while satisfying the performance constraints of each job. The existing works in cluster scheduling mainly focus on improving job performance and do not leverage from VM types on the cloud to reduce cost. In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the proposed algorithms can also prioritise jobs based on their given deadlines. Besides, the proposed scheduling algorithms are online and adaptive to cluster changes. We have also implemented the proposed algorithms on top of Apache Mesos. Furthermore, we have performed extensive experiments on real datasets and compared to the existing schedulers to showcase the superiority of our proposed algorithms. The results indicate that our algorithms can reduce resource usage cost up to 34% under different workloads and improve job performance.

A hybrid heuristic of Variable Neighbourhood Descent and Great Deluge algorithm for efficient task scheduling in Grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND.

Fast Coflow Scheduling via Traffic Compression and Stage Pipelining in Datacenter Networks

Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional scheduling techniques based on improving network resource utilization are subject to limited bandwidth in datacenter networks. To alleviate the shortage of bandwidth, some cluster frameworks employ techniques of traffic compression to reduce transmission consumption. However, they tackle scheduling in a coarse-grained manner at task level and do not perform well in terms of flow-level metrics due to high complexity. Fortunately, the abstraction of coflow pioneers a new perspective to facilitate scheduling efficiency. In this paper, we introduce a coflow compression mechanism to minimize the completion time in data-intensive applications. Due to the NP-hardness, we propose a heuristic algorithm called Fastest-Volume-Disposal-First (FVDF) to solve this problem. For online applicability, FVDF supports stage pipelining to accelerate scheduling and exploits recurrent neural networks (RNNs) to predict compression speed. Meanwhile, we build ${\sf Swallow}$ Swallow , an efficient scheduling system that implements our proposed algorithms. It minimizes coflow completion time (CCT) while guaranteeing resource conservation and starvation freedom. The results of both trace-driven simulations and real experiments show the superiority of our algorithm, over existing one. Specifically, ${\sf Swallow}$ Swallow speeds up CCT and job completion time (JCT) by up to $1.47 \times$ 1 . 47 × and $1.66 \times$ 1 . 66 × on average, respectively, over the SEBF in Varys, one of the most efficient coflow scheduling algorithms so far. Moreover, with coflow compression, ${\sf Swallow}$ Swallow reduces data traffic by up to 48.41 percent on average.

Efficient Task and Data Scheduling Policy for Vehicular Fog Computing Based on Link Weight

Vehicular network has several applications in the smart city and IoT. Recently with the advancement in the computing technology such as fog computing and its application in the vehicular network and its services, a new paradigm known as vehicular fog computing has evolved as a hot topic of investigation in the research community because of the next generation computing and communication requirements. Vehicular fog computing can be used to solve the issues of next generation computing and communication scenario. There are several issues in vehicular fog computing. Efficient task computing and data dissemination is an important issue. Several approaches are proposed by different authors to solve the issues, but none of them has addressed the service completion and failure rate which is very important in the vehicular scenario as the vehicles move very fast and its contact time with the RSU controller is limited. The task has to be completed by the vehicular server within that time period, otherwise computation will fail. Once the computation and communication fails, the RSU controller will reinitiate to form the vehicular fog resulting high overhead. In this paper we address this issue and proposed an efficient scheduling algorithm based on multiple parameters namely queue length, response time and link weight. We simulated the algorithm using java and compared with the existing algorithm showing better performance.

PE2 - A Service Oriented Meta Task Scheduling Framework in Cloud Environment

Cloud computing is an emerging computing environment which facilitates on demand services. As it contributes a large pool of computing resources, scheduling of tasks in an efficient manner is one of the main problems. Poor allocation of tasks affects the performance of the whole system. Hence it is very important to schedule the tasks for better utilization of resources by allocating them properly to particular resources in particular time. Efficient scheduling algorithms fulfill the user requirements and also satisfy the needs of the cloud service providers without affecting the performance of the environment. Execution Time based Sufferage Algorithm (ETSA), Cost and Completion Time based Sufferage Algorithm (CCTSA) and Modified Artificial Fish Swarm(MAFSA) Algorithm are efficient task scheduling approaches developed in cloud environment. These algorithms considered the parameters such as makespan, cost and resource utilization while scheduling the tasks and produced better performance. This paper presents a scheduling framework which converts the above said algorithms in to services and deployed in the cloud. Depends on the user’s requirements, the services will be delivered.

Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks

Data collection is a major operation in Wireless Sensor Networks (WSNs) and minimizing the delay in transmitting the collected data is critical for a lot of applications where specific actions depend on the required deadline, such as event-based mission-critical applications. Scheduling algorithms such as Time Division Multiple Access (TDMA) are extensively used for data delivery with the aim of minimizing the time duration for transporting data to the sink. To minimize the average latency and the average normalized latency in TDMA, we propose a new efficient scheduling algorithm (ETDMA-GA) based on Genetic Algorithm(GA). ETDMA-GA minimizes the latency of communication where two dimensional encoding representations are designed to allocate slots and minimizes the total network latency using a proposed fitness function. The simulation results show that the performance of the proposed algorithm outperforms the existing state-of-the-art approaches such as Rand-LO, Depth-LO, DepthRe-LO, IDegRe-LO, and IDeg-LO in terms of average latency, average normalized latency, and average schedule length.

Energy efficient scheduling in local area networks

In Wireless Local Area Networks (WLAN), portable devices such as notebooks, tabs, and smart phones are powered by batteries with limited energy. With the great increase of using such portable devices, energy efficiency becomes one of the most important issues in wireless networks that are based on IEEE 802.11b standard. Although, IEEE 802.11b standard enables devices with limited batteries’ capacity to send and receive more data, it is not suitable for high load networks. In high load networks, Access Point (AP) cannot immediately deliver buffered packets to portable devices which leads to batteries’ drain as devices have to stay in active state for long time. Towards solving this great issue, an energy efficient downlink scheduling algorithm for WLAN is presented in this paper. The proposed scheduler aims to reduce the probability of medium contention and waiting time during the reception of buffered data packets from the AP without affecting the quality of service performance. The presented scheduler proposes a time slotted scheme to enhance the Power Saving Mode of IEEE 802.11b standard. The proposed approach is validated analytically and experimentally. Qualnet network simulator is used for this purpose. Promising results are obtained compared to legacy IEEE 802.11 protocol, and the Shortest Job First scheduler protocol in terms of energy saving, and throughput.

An offloading scheme leveraging on neighboring node resources for edge computing over fiber-wireless (FiWi) access networks

The computation resources at a single node in Edge Computing (EC) are commonly limited, which cannot execute large scale computation tasks. To face the challenge, an Offloading scheme leveraging on NEighboring node Resources (ONER) for EC over Fiber-Wireless (FiWi) access networks is proposed in this paper. In the ONER scheme, the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly, so that it can support the offloading transmission with low delay and wide bandwidth. Based on the ONER scheme supported by FiWi networks, computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops (e.g., just one wireless hop), which achieves low delay. Additionally, an efficient Computation Resource Scheduling (CRS) algorithm based on the ONER scheme is also proposed to make offloading decision. The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm. Therefore, the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

An Energy Efficient Algorithm for Workflow Scheduling in IaaS Cloud

Energy efficient workflow scheduling is the demand of the present time’s computing platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is important to decrease makespan of a schedule as well, so that it may not extend beyond the deadline specified by the cloud user. In this paper, we propose a workflow scheduling algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm. The proposed scheme is shown to be energy efficient. Apart from this, it is also shown to minimize makespan. We refer the proposed approach as energy efficient workflow scheduling (EEWS) algorithm. The EEWS is introduced with a novel measure to determine the amount of energy which can be conserved by considering a DVS-enabled environment. Through simulations on a variety of scientific workflow applications, we demonstrate that the proposed scheme performs better than the existing algorithms such as HCRO and multiple priority queues genetic algorithm (MPQGA) in terms of various performance metrics including makespan and the amount of energy conserved. The significance of the proposed algorithm is also judged through the analysis of variance (ANOVA) test and its subsequent LSD analysis.

Delay Efficient Scheduling Algorithms for Data Aggregation in Multi-Channel Asynchronous Duty-Cycled WSNs

Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.