A Survey on Scheduling Algorithms for Parallel and Distributed Systems

Tyagi, Rinki; Gupta, Santosh Kumar

doi:10.1007/978-981-10-7656-5_7

Cited by 27 publications

(13 citation statements)

References 15 publications

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“…RM requires the least computation because all VM's are checked in the run queue. RM is less complex than EDF in some specific scenarios because the popularity of memory-nodes has to be taken into account in EDF [33] [34] [35].…”

Section: Scheduling Overheadmentioning

confidence: 99%

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

et al. 2021

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Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardwarebased scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen's Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory's energy consumption based on the implementation.

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Section: Scheduling Overheadmentioning

confidence: 99%

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

et al. 2021

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“…Scheduling of batch and on-demand jobs for concurrent execution where resources sharing is limited to each type of job has been widely studied [12]- [19] in the past. However, not much work has been done for collocating both batch and on-demand jobs on the same set of resources [20].…”

Section: Related Workmentioning

confidence: 99%

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

Maurya

Nicolae

Guliani

et al. 2020

2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments, acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing infrastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

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“…Scheduling is a process of allocating a variety of tasks on processors aiming at various objectives including increase the speed of execution, decrease the make-span, and reduce the communication delay and cost. The main goal of scheduling in cloud computing is the efficiency that optimizes the overall system performance and minimizes the overhead problems [10]. According to the work presented in [11], a scheduling process is to guide the processing or the execution of interdependent tasks (workflows) on distributed resources.…”

Section: Task Schedulingmentioning

confidence: 99%

Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

Saif¹

2019

IJATCSE

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Cloud computing is a ubiquitous platform that offers a wide range of online services to clients including but not limited to information and software over the Internet. It is an essential role of cloud computing to enable sharing of resources on-demand over the network including servers, applications, storage, services, and database to the end-users who are remotely connected to the network. Task scheduling is one of the significant function in the cloud computing environment which plays a vital role to sustain the performance of the system and improve its efficiency. Task scheduling is considered as an NP-complete problem in many contexts, however, the heterogeneity of resources in the cloud environment negatively influence on the job scheduling process. Furthermore, on the other side, the heuristic algorithms have satisfying performance but unable to achieve the desired level of the efficiency for optimizing the scheduling in a cloud environment. Thus, this paper aims at evaluating the effectiveness of the hybrid meta-heuristic that incorporate genetic algorithm along with DE algorithm (GA-DE) in terms of make-span. In addition, the paper also intends to enhance the performance of the task scheduling in the heterogeneous cloud environment exploiting the scientific workflows (Cybershake, Montage, and Epigenomics). The proposed algorithm (GA-DE) has been compared against three heuristic algorithms, namely: HEFT-Upward Rank, HEFT -Downward Rank, and HEFT -Level Rank. The simulation results prove that the proposed algorithm (GA-DE) outperforms the other existing algorithms in all cases in terms of make-span.

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A Survey on Scheduling Algorithms for Parallel and Distributed Systems

Cited by 27 publications

References 15 publications

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

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