Showing papers on "Fair-share scheduling published in 2020"

A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment

Abstract: In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.

DSARP: Dependable Scheduling with Active Replica Placement for Workflow Applications in Cloud Computing

Abstract: As an efficient development for industrial and scientific applications, workflow technologies have received substantial attention in recent decades. To address the issue of workflow scheduling in a state-of-the-art cloud environment, based on analysis of a decentralized architecture for workflow scheduling, a dependable scheduling strategy with active replica placement (DS-ARP) is proposed in this paper. In this proposal, by analyzing control/data dependencies in a workflow, a game-theory-based active replica placement model is first developed to achieve reasonable replica placement; then, a dependable scheduling algorithm is proposed to enhance the system reliability and security. With five well-known workflow applications, CloudSim-based simulations are performed, and the analytical results are shown to demonstrate the performance of DS-ARP on an average number of initiated replicas, costs resulting from canceled replicas, makespans, deadline violation rates and resource utilization rates.

Two-Phase Scheduling for Efficient Vehicle Sharing

Abstract: bfCooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

A Particle Swarm Optimization -Based Heuristic for Scheduling in FMS Review

Abstract: This paper focus on the problem of simultaneous scheduling of machine and automated guided vehicle (AGV) in a flexible manufacturing system (FMS) so as to minimize the makespan The FMS scheduling problem has been tackled by various traditional optimization techniques. While these methods can give an optimal solution to small-scale problems, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple bjectives,i.e.,minimising the idle time of the machine andminimising the total penalty cost for not meeting the deadline concurrently. Two optimization algorithms ( genetic algorithm and particle swarm algorithm) are compared and conclusions are presented

Memory-Aware Fair-Share Scheduling for Improved Performance Isolation in the Linux Kernel

Abstract: Performance interference between QoS and best-effort applications is getting more aggravated as data-intensive applications are rapidly and widely spreading in recently emerging computing systems. While the completely fair scheduler (CFS) of the Linux kernel has been extensively used to support performance isolation in a multitasking environment, it falls short of addressing memory-related interference due to memory access contention and insufficient cache coverage. Though quite a few memory-aware performance isolation mechanisms have been proposed in the literature, many of them rely on hardware-based solutions, inflexible resource management or ineffective execution throttling, which makes it difficult for them to be used in widely deployed operating systems like Linux running on a COTS SoC platform. We propose a memory-aware fair-share scheduling algorithm that can make QoS applications less susceptible to memory-related interference from other co-running applications. Our algorithm carefully separates the genuine memory-related stall from a running task’s CPU cycles and compensates the task for the memory-related interference so that the task gets the desired share of CPU before it is too late. The proposed approach is adaptive, effective and efficient in the sense that it does not rely on any static allocation or partitioning of memory hardware resources and improves the performance of QoS applications with only a negligible runtime overhead. Moreover, it is a software-only solution that can be easily integrated into the kernel scheduler with only minimal modification to the kernel. We implement our algorithm into the CFS of Linux and name the end result mCFS. We show the utility and effectiveness of the approach via extensive experiments.

CCHybrid: CPU co-scheduling in virtualization environment

Abstract: Summary Virtualization is very important to build the emerging cloud infrastructure, and a VM (virtual machine) with many kinds of workloads can run on physical machines in cloud environment. The VMM (virtual machine manager) scheduling algorithm asynchronously schedules each VCPU (virtual CPU) of a VM and ensures the CPU time usage of each VM. This proportional share method is widely used, because it simplifies the implementation of VMM CPU scheduling algorithm and can provide near-perfect performance for most ordinary workloads. However, when a VM runs with parallel workloads, the above method causes performance degradation because of the negative impact of virtualized systems. Therefore, in this paper, we propose an optimized scheduling system, called CCHybrid, for parallel program in the Xen. It uses weight-based proportion share strategy to ensure the fairness. In order to resolve the impact of virtualization on synchronization, it uses a novel co-scheduling strategy, which dynamically adjusts the size of co-scheduling to remit CPU fragmentation and maintains the original asynchronous scheduling policy for non-parallel applications. In this way, CCHybrid provides CPU resource allocation services for Xen and can decrease the negative impact of virtualized systems, while ensuring the fairness of VMs and the performance of non-parallel workload. Experimental results show that in the case of multiple VMs, CCHybrid improves the performance of parallel workload from 15% to 50%, and the impact on non-parallel workload is less than 5%, in comparison with the credit scheduling algorithm of Xen.

Deadline Flow Scheduling in Datacenters with Time-Varying Bandwidth Allocations

Abstract: Many production datacenters nowadays service multiple applications with dynamically allocated network bandwidth, and some applications are time-critical so their data transfers or flows are constrained by deadlines. To meet deadlines, several flow scheduling schemes for datacenter networks are proposed, but most of them are unaware of bandwidth variations, leading to suboptimal throughputs. In this paper, we present a flow scheduling scheme, called UBAS, to improve deadline-meeting throughputs for time-critical applications with uncertain time-varying bandwidth allocations. First, we model an optimization problem of scheduling deadline-constrained flows under uncertain time-varying bandwidth allocations to maximize the expected deadline-meeting throughput. The problem is NP-hard. Then, we propose an approximation algorithm under a mild condition for the problem in a special case, where bandwidth allocations are certain, as well as a conditional approximation algorithm for the problem in general. To adapt to practice, scalable and online variants of the algorithm are also presented. In evaluation, we conduct simulations based on a real traffic trace in a production datacenter. The results demonstrate that, with severe practical settings, UBAS still achieves nearly optimal deadline-meeting throughputs. Moreover, the throughput improvements of UBAS against existing bandwidth-agnostic schemes are more substantial when the variance of bandwidth allocations over time increases.

Efficient Schedulability Test for Dynamic-Priority Scheduling of Mixed-Criticality Real-Time Systems

Abstract: Systems in many safety-critical application domains are subject to certification requirements. In such a system, there are typically different applications providing functionalities that have varying degrees of criticality. Consequently, the certification requirements for functionalities at these different criticality levels are also varying, with very high levels of assurance required for a highly critical functionality, whereas relatively low levels of assurance required for a less critical functionality. Considering the timing assurance given to various applications in the form of guaranteed budgets within deadlines, a theory of real-time scheduling for such multi-criticality systems has been under development in the recent past. In particular, an algorithm called Earliest Deadline First with Virtual Deadlines (EDF-VD) has shown a lot of promise for systems with two criticality levels, especially in terms of practical performance demonstrated through experiment results. In this paper we design a new schedulability test for EDF-VD that extend these performance benefits to multi-criticality systems. We propose a new test based on demand bound functions and also present a novel virtual deadline assignment strategy. Through extensive experiments we show that the proposed technique significantly outperforms existing strategies for a variety of generic real-time systems.

The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it

Abstract: In the paper, the new approach to the scheduling problem are described. The approach deals with the problem of planning the cyclic production and proposes to consider such scheduling problem as the cyclic job-shop problem of the order k, where k is the number of reiterations. It was found out that planning of only one iteration of the loop is less effective than planning of the entire cycle. To the experimental research, a number of test instances of the job-shop scheduling problem by Operation Research Library were used. The Simulated Annealing was applied to solve the instances. The experiments proved that the approach proposed allows increasing the efficiency of cyclic scheduling significantly.

A New Scheduling Method for TTCAN-Based Turbofan Distributed Control System

Abstract: Abstract A hybrid-scheduling method, consisting of packing algorithm, genetic algorithm and priority promotion algorithm, was proposed for time-triggered CAN in this paper. We divided the basic cycles (BC) into synchronous phase for transmitting time-triggered messages and asynchronous phase for transmitting event-triggered messages. At the each end of BC, fault-tolerant windows were designed to improve the fault-tolerance. The First Fit Increasing and the genetic algorithm scheduled and optimized the transmission of time-triggered messages in the synchronous phase, and the priority promotion algorithm optimized the transmission of event-triggered messages in the synchronous phase. For the communication application of a TTCAN-based turbofan distributed control system, the new scheduling method improved the bandwidth utilization and arbitrary windows utilization to 99% and 9.3%.