Dynamic Resource Allocation And Job Scheduling
To Enhance The Performance Of HPC With SDN -
A Review
Sabbi Vamshi Krishna, Dr. Azad Shrivastava, Dr. Sunil J. Wagh, Dr. T.V. Prasad
Abstract: Conventional job management systems (JMS) consider physical resources alone as computational resources. Computational resources are relegated an assignment as calculation of nodes in HPC (High Performance Computing) cluster systems and the job procedures are executed legitimately on the allotted computing nodes. It is seen that the decrease in network latency and better bandwidth usage in SDN is accomplished just when it has dynamic allocation of resources and job scheduling standards. In this way in this survey it is outlined about different successful dynamic resource allocation schemes alongside better job scheduling techniques. On a similar time the slacking in every strategy are talked about and from that the answer for improving the resource allocation in HPC discovered. In this way this review may give an approach to define better and proficient resource allocation and job scheduling schemes to improve the performance of HPC with SDN.
Keywords: Job Management System (JMS), Software Defined Networking (SDN), High Performance Computing (HPC), Dynamic resource allocation
——————————◆——————————
1. INTRODUCTION
Mathematical models and numerical simulations plays an significant role in today's science and engineering, It is too expensive or dangerous to enabling the study of complex phenomena by direct experimentation. However, these techniques require such an enormous computational capability that significantly exceeds what is provided by ordinary computers. In this regard, High-Performance Computing (HPC) or supercomputing is the broad domain in which novel computing architectures and techniques are investigated to provide high-end computing power. Weather prediction, energy research, design of vehicles and aircraft, finding oil, computational chemistry, computational medicine, understanding the evolution of the universe, and online fraud detection are a few representative examples of domains for the application of HPC. The efficient utilization of the abundant hardware resources in HPC systems, becomes particularly important when consider the complexity and the large scale at which HPC systems are deployed. The introduction of SDN into the high-performance computing (HPC) world allows scientists to leverage a high-speed and simplified model for access to the vast computing power of supercomputers [1]. The growth of software defined networking (SDN) as a persistent network enables advancement. SDN is a network architecture that disengage the control plane from the
forwarding plane [2]. From the perspective of SDN, the forwarding plane comprises of high-performance item switches, while the control plane is reversed on an open and centralized control stage for deployment and coexistence. Multiple control protocols for controlling packets through the standard forwarding theoretical interface. (e.g. the Open Flow protocol) [3]. The SDN application can incite control directions to control packets as firewalls and optimize the sending way as per network traffic distribution [4]. In the present network, network capacity basically alludes to what extent a network conveys data packets. Such network limit is communicated by a forwarding that is controlled by interface bandwidth [5]. More bandwidth means the rate of transmission is high and thus the transmission delay gets reduced [6]. Therefore, bandwidth becomes the most important network resource, because the limit of link bandwidth is due to the participation and competition of many applications [7]. Supercomputing resources are usually managed by a batch processing system, which creates effective mapping of jobs on the resources (i.e., compute nodes). Parallel jobs are categorized into 4 types namely - rigid, moldable, malleable and evolving. While rigid jobs set resource requirements over the entire life cycle, moldable jobs allow batch systems to depart from the number of requested resources before the job begins. Traditional batch systems only support rigid & moldable jobs, they perform static resource management [8]. However, this is not enough as supercomputing enters a new era. To attain energy efficiency and fault tolerance in computing, Programming models are becoming more adaptive in nature to support malleability. Therefore, scheduling evolving and malleable jobs (ie, dynamic resource management) would be unavoidable, specifically on future large-scale systems. This dissertation suggests novel dynamic resource management and scheduling strategies for cluster frameworks, which make numerous commitments in the zones of dynamic resource distribution mechanism, efficient adaptive task scheduling, and regime. In this, we only focus on dynamic allocation of resources and scheduling of jobs to enhance HPC performance with SDN.
2. RELATED RESEARCHES
________________________________• Sabbi Vamshi Krishna : Electronics and Communication Department, Godavari Institute of Engineering and Technology, Rajahmundry. [email protected].
• Dr. Azad Shrivastava : HPC Division, Aura Emanating Technology Pvt. Ltd. New Delhi. [email protected].
• Dr. Sunil J. Wagh : Department of Electronics and Communication, Mahatma Gandhi Mission’s College of Engineering and Technology, Noida. [email protected].
Jason Liu et al. [9] proposed a simulator for scheduling parallel workloads and model application placements on large-scale high-performance computing platforms. Which is created dependent on the Performance Prediction Toolkit (PPT) and is written in Python, a parallel discrete-event simulator for quick assessment and performance prediction of huge scale logical applications on supercomputers. The experiments show that the application performance can vary significantly for various job scheduling and task mapping algorithms, as well as the runtime conditions of the targeted platform. The proposed simulator is unique in that it incorporates full-scale models for parallel applications and parallel architectures. In future they planned to extend the job scheduler simulator to incorporate the Machine learning techniques for large-scale performance evaluation studies. Sindhu et al. [10] Proposes a Dynamic List scheduling Algorithm (DLSCP) which is a modified version of the List scheduling Algorithm that schedules HPC applications on cloud resources to minimize make span. Which follows the concept of Critical Path and is dynamic because the priority of tasks to be scheduled at each step is re-calculated based on the already scheduled tasks and represented as a DAG onto a distributed cloud infrastructure. In future they like to experiment the algorithm with real workload traces and also consider the optimization of energy consumption. Vidya Chitre et al. [11] discussed about different approaches for improving HPC task scheduling performance on cloud using software-defined networking. Moreover, a task scheduling architecture is proposed to manage task scheduling of HPC applications, which can be later on used as a service (HPCaaS) to improve the profit of service providers. Bandwidth and capacity of virtual machines are the main parameters that are considered. SDN-enabled task scheduling algorithm can consider VM cost and enable the scheduler to decide target VMs so that optimal performance can be achieved. Meng et al. [12] Proposed a Consolidated job allocation approach to optimize both cooling power and communication latency in HPC data centers. First they use the MPIT algorithm to detect the most cooling-efficient nodes for job allocation. At that point the adjusted MC1X1 algorithm is applied to distribute work to cooling-efficient nodes by keeping the average distance at the very least and apply the data center cooling energy model and joint enhancement algorithm to the SST, which is a key for enormous data centers test system. By assessing joint approach under real-world workloads, this policy reduces cooling power by 42.21% compared to performance-aware policies. Xue et al. [13] expressed about the G-ACO scheme for LB of SDN, which combines GA's selection, crossover, and mutation operation with the ACO algorithm to boost the speed of path search and the ability of optimal path search. An experiment with two network topologies was carried out, by applying the suggested system in the LB module of the Open Daylight controller. Thus improves the time taken for transmission and reduces the rate of packet loss. In the future, analytical models will be developed by which the value of the factors can be properly determined.
3. PERFORMANCE ENHANCEMENT OF HPC
WITH SDN
Controlling data traffic is the major problem in networking. SDN gives a proficient partition of two basic functionalities of such systems administration workplaces, in particular, data forwarding and decision making. In SDN, data plane is the data forwarding part and the other part chooses how every data should be sent to the system and passes on the decision of
fitting to systems administration workplaces, is called control plane. The control plane is generally executed as a software program and that is the reason it is called software-defined, which implements the ability to direct and automate data traffic, thereby it becomes very easy to improving the quality of services (QoS). By implementing software-defined networking, enterprise businesses can easily adapt existing network devices thus remodeling existing hardware to follow SDN controller instructions, and more cost-effective hardware with greater effect. Some dynamic scheduling mechanisms have been used in this framework to achieve high performance computing. Dynamic scheduling algorithms focus on incorporating dynamicvariation in number of users and types of jobs and resources requested by them. This framework briefly describes the efficient HPC with SDN .The effective framework is implemented mainly based on two process,
● Resource allocation
● Job Scheduling
3.1 Review on Dynamic Resource Allocation to enhance HPC with SDN
scheduled job. The migration cost of BgMBF is also considered and the refreshed version of BgMBFSDF is proposed to decrease the measure of migrations when the execution time of jobs is seen. Guo et al. [18] present HyperFlow, a distributed event-based control plane for OpenFlow. HyperFlow is logically united anyway physically distributed: it gives adaptability while keeping the upsides of network control centralization. By idly synchronizing network-wide points of view on OpenFlow controllers, HyperFlow limits fundamental initiative to solitary controllers, therefore restricting the control plane response time to data plane requests. HyperFlow is adaptable to network allotting and section dissatisfactions. It moreover engages interconnecting self-sufficiently supervised OpenFlow networks, a crucial component missing in current OpenFlow courses of action. HyperFlow is executed as an application for NOX, it requires immaterial changes to NOX, and licenses reuse of existing NOX applications with minor adjustments. The central evaluation shows that, tolerating sufficient control data transmission, to bound the window of anomaly among controllers by a factor of the delay between the most remote controllers, the network changes must occur at a rate lower than 1000 events for consistently over the network. Vamshi Krishna et al. [19] considering a self-healing Software Defined Networking (SDN)-aware SH work, which formally analyse the trade-offs between timeliness and volume of the load information being revealed which enables the efficiency and granularity of the control input necessary to achieve fast reconfiguration which in turn enabling the throughput of the HPC network to be improved. The structure incorporates a SDN quickened HPC network for resource allocation utilizing FA-CRA (Fairness Aware Cooperative Resource Allocation) and CG-CRA (Coalition Game Based Resource Allocation) is an outright job scheduling done by joining algorithms and priority based scheduler DLS (Dynamic Level Scheduling) and time based scheduler ETF (Earlier Time First). Along these the throughput of the framework is improved. Fu et al. [20] proposes DRS (Dynamic resource scheduler) to make efficient resource allocation in cloud-based DSMS. DRS overcomes several fundamental challenges, including the estimation of the required resources necessary for satisfying real-time requirements, effective and efficient resource provisioning and scheduling, and the efficient implementation of such a scheduler in a cloud-based DSMS. The performance model of DRS is based on rigorous queuing theory, and it demonstrates robust performance even when the underlying conditions of the theory are not fully satisfied. In addition, integrated DRS into a popular system Storm, and evaluated it by conducting extensive experiments based on real applications and datasets. Kim et.al [21] proposed a dynamic resource scheduling algorithm. LTE radio resources could be allocated by MCPTT user service priority that changes the user priority dynamically by directly requesting the user or by changing the user authority on the command center. The MCPTT highest priority user shows that the download speed is improved in Radio resource scheduling. Wan et.al [22] Focusing on dynamic resource management based on IIoT, presents an effective data connection model dependent on the incorporation of OPC UA, SDIN, and D2D communication technology, and establishes the framework for resource interconnection. Ontology modeling is introduced to virtualize the manufacturing resources. With the combination of the ontology knowledge base and the local database, Jena manages to derive the equipment operation mode in real time. Also, multi-agent technology is introduced to achieve dynamic
communication, dynamic virtual resource allocation and substance through a software-defined networking (SDN) controller with an overall viewpoint on the system encourages caching. In our proposed framework, substrate physical resources can be virtualized and shared between multiple mobile virtual network operators (MVNOs). In the meantime, through incorporating D2D communications into information-centric wireless networks, content caching is enabled recognizable in air just as in mobile devices. Moganarangan et.al [30] depicted a novel resource management system (RMS) equipped for dealing with different resources of HPC cluster systems, and considering and building up the SDN upgraded job management system (JMS) structure, Which can deal with an interconnect as system resources by coordinating the software defined networking (SDN) idea into a conventional JMS. Be that as it may, the current SDN-upgraded JMS Framework can't assign virtualized computational resources to a job in light of the fact that the computational resource management is performed by the system of a customary JMS. This paper, proposed a system to manage virtualized computational resources on the SDN-improved JMS Framework. This system enables to send virtual machines (VMs) referenced by the customer to the processing hubs assigned to a job and execute job's methods in the VMs. Song et.al [31] analyzed with the ascent of ongoing correspondence (ie, high data transmission video, video calls, and so forward.) and IoT, it transforms into a test to manage the system and handle traffic. SDN is a development that gives versatility and adaptability to the system by decoupling the control plane and data plane. It is in like manner difficult to distribute resources reliant on assignments and customer necessities. SDN gives an overall point of view on worldwide controller arranges that makes resource allocation progressively trustworthy. Gallardo et.al [32] talked about Dynamic inter-cell wireless resource allocation plot that broadens and gives a progressively summed up design to heterogeneous radio access networks, especially as far as resource allocation dependent on QoE prerequisites for help. SDN Analytical and exploratory outcomes exhibit that the proposed plan has improved over 10% of client QoE fulfillment because of high productivity and extra adaptability of advancement gave by a radio resource allocation conspire between SDNs in the HetNets. Zheng et.al [33] displayed a coherent architecture for networks, Provided by Slicing-based 5G system, and familiarizes a plan to oversee mobility between individual access networks, similarly as a combined power and sub-direct distribution plot in spectrum-sharing-two-tier systems based on network slicing. Where both the co-tier interference and cross-tier interference are considered. Proliferation results show that the proposed resource allotment plan can deftly convey network resources between different slices in a 5G system. Li et .al [34] characterized cellular network with wireless network virtualization. In the proposed type, a hypervisor empowers virtualization of physical M2M networks as per the various capacities and quality of service (QoS) prerequisites of machine-type communication devices (MTCDs), which are disconnected and sliced into multiple virtual M2M networks. Also, build up a decision-theoretic approach to enhance the random access procedure of M2M communication. Further built up a feedback and control loop, which modifies the quantity of resource blocks (RBs) utilized by the SDN controller in the arbitrary access phase in the virtual M2M network.
3.2 Review on Dynamic job Allocation to enhance HPC with SDN
SETSA has significant potentials to improve the performance of HPCaaS platforms by increasing the bandwidth capability and decreasing task turn time. In addition, SETSAW, (SETSA Window) is proposed as an improvement of the SETSA algorithm. Li et.al [41] proposed a new task scheduling algorithm based on Hadoop to optimize scheduling of resources problems under the Distributed cloud-computing platforms. The main idea of the algorithm is the full context for current network conditions and is considered as an important reference for system task scheduling, allowing for SDN architecture with bandwidth management capability. It need to allocate bandwidth according to a time slot strategy, then sooner or later according to the operation to decide whether the task is assigned to the local node or the non-local node of low load. Watashiba et.al [42] clarified about SDN-enhanced JMS that proficiently handle both network and CPU resources and subsequently quicken the execution time of client employments is presented as the structure square innovation for such HPC cloud. Our assessment suggests that SDN-increased JMS successfully utilize the fat-tree interconnect of cluster systems running behind the cloud to stifle the impact of correspondences made by different jobs. Liu et.al [43] propose another set of lattices called potential capacity (PC) and equilibrium capacity (EC), of resources that offer aspects of dynamic, elastic, and co-located virtual machines. Effectively meshes this set of metrics into traditional scheduling algorithms to evaluate execution in a generally utilized greedy scheduling algorithm and show that a similar algorithm utilizing traditional physical machine metrics, for example, the accessible CPU capacity in the new scheduler Comparisons improve job speed for various designs.
A. Completely Fair Scheduler (CFS)
Kobus et.al [44] discussed about the processor scheduler called CFS, it uses red black tree (RBT) scheduling algorithm. It calculates the time for a given task just before it is scheduled for execution and the number of tasks and its priorities are maintained in a queue to share the resource between queues. Different policies are used, namely SCHED_FIFO and SCHED_RR. SCHED_FIFO functions do not get finite time, but execute until completion. The SCHED_RR classes obtain fixed time quanta and are determined according to the round-robin algorithm. It handles CPU resource allocation for execution processes, and aims to maximize overall CPU utilization while maximizing interactive performance, but the problem with this is more switching and more waiting time.
B. Adaptive completely fair scheduling policy(adCFS):
Alzahrani et.al [45] Proposes a shared policy called the Adaptive Completely Fair Scheduling Policy (adCFS), which thinks about the future state of CPU utilization and the premise of their particular workload metrics (eg, CPU use, task request, # tasks). Be that as it may, shares CPU cycles between various containers later on. adCFS estimates the heaviness of workload attributes and redistributes the CPU dependent on this weight. The Markov chain model is utilized to anticipate the utilization of CPU state, and an ADCFS policy is acquainted with dynamically allocate containers to the fitting CPU parts. Exploratory outcomes show upgraded holder CPU response time for containers that run heavier and bigger tasks: they show 12% quicker response time than the default CFS (Completely Fair Scheduler). In this way adCFS improves CFS by thinking
about workload metrics, which when completely utilized, are vigorously shared to the CPU.
C. High Performance Computing Scheduler (HPC Sched)
Boneti et.al [46] presented a dynamic solution inside the Linux kernel as a new scheduler (HPCSched) for HPC applications. In order to balance the HPC application, the scheduler tracks the application behavior and detects when to increase or decrease the amount of processor’s internal resources assigned to a specific process. Provides a software-controlled prioritization mechanism that allows us to bias the processor resource allocation. HPC scheduler is based on three components namely Scheduling policy, Load Imbalance Detector and Heuristics, Mechanism. Where scheduling policy is based on Fist-In-First-Out and Round-Robin algorithms to balance the number of task at each domain. Load Imbalance Detector and heuristic functions learns from the execution history of a process to select the new hardware priority for the task. Mechanism is the only architecture-depended part, while the HPC scheduler can be used on any architecture and may, eventually, provide some performance improvement (because the HPC class has higher priority than the CFS class), balancing an MPI application assigning more or less hardware resources to a process can only be done if the underneath processor supports this feature.
D. Stochastic Fairness Queuing (SFQ)
Tucker et.al [47] states that a SFQ keeps up separate queues for each stream. Queues are served in a round-robin style so streams can make the most of their well-deserved share of bandwidth. Those that attempt to use more than the apportioned portion of system resources are rebuffed through longer delays and huge packet loss rate. Input packets are to be mapped to one specific FCFS queue contingent upon source, goal address pair.
E. Effective Random Early Detection and Fuzzy Logic
Khatari et.al [48] Blockage in switch bolster assembles the delay and packet loss. Active Queue Management (AQM) methods can separate stop up in early stage and control it by packet dropping. Effective Random Early Detection (ERED) strategy, among various other AQM procedures, gives an Ok performance in perceive and control blockage and spare packet loss. Nevertheless, the ERED dismiss the delay factor, which is sway the performance of the framework. Also, ERED has a real parameterization issue. A couple of parameters must be acquainted with perfect regards to gain great results. This paper proposed an extended ERED method that thinks about the delay in its system and combines the extended ERED methodology with a Fuzzy Inference Process that encourages the issue of parameter instatement. The results show that the parametric-based form of the proposed work gives a predominant performance results, as demonstrated by the performance measures, delay, dropping and packet loss. The loss has been overhauled by 10-100%. Delay has been redesigned by 30-60%. The performance of the fuzzy-based form of the proposed technique is better than anything the parametric-based form and ERED to the extent delay and packet loss.
F. Ethernet Congestion Control Protocol(ECCP)
versatile and cost-beneficial switch surface, and fulfills the extreme switch characteristics. Plus, present a numerical model of ECCP using Delay Differential Equations (DDEs), and research its trustworthiness using the phase plane methodology. By then Deduced the satisfactory conditions of the reliability of ECCP that could be used for parameter setting fittingly. It in like manner found that the sufficiency of ECCP is generally ensured by the sliding mode development, causing ECCP to keep cross traffic close to the best association atmost and queue length almost zero. Expansive entertainment circumstances are gone to favor the descriptive results of ECCP lead. This assessment shows that ECCP is practical in avoiding congestion and achieving least framework idleness. Likewise, to affirm the introduction of ECCP in certifiable frameworks, and a testbed execution is coordinated for ECCP using Linux machines and a 10-Gbps switch.
Advantages of ECCP are as follows:
• Using Ethernet commodity switches as a switch fabric for routers.
• ECCP controls transmission rate using estimated available bandwidth.
• Presenting a mathematical model of ECCP using Delay Differential Equations (DDEs).
• Deducing the stability conditions of ECCP using the phase plane method.
G. Adaptive Bit-Map-Assisted Medium Access Control Protocol (ABMA)
Tolani et.al [50] proposes an Adaptive Bit-Map-Assisted (ABMA) Medium Access Control (MAC) protocol. ABMA is a modified type of Energy-efficient Bit-Map-Assisted (E-BMA) MAC protocol by which it can efficiently utilize the data spaces with low overhead traffic. In ABMA MAC protocol, cluster head adaptively varies allotment of data openings to the sensor node (SN) which is based on the buffer status of the SN. What's more, ABMA uses useless control field for the reservation of data spaces and piggybacking. Recreation and mathematical models have been developed and proposed for ABMA MAC protocol. The performance of ABMA MAC protocol has been compared with Time Division Multiple Access (TDMA), Energy Efficient-TDMA (EA-TDMA), Bit-Map-Assisted (BMA) and Energy Efficient-BMA (E-BMA) MAC protocols. Both expository and reproduction results show the overall superiority of our proposed ABMA MAC protocol.
4. FUTURE WORK
Resource allocation with self-healing measures may give the independent highlights like self-discovery, self-repairing and self-configuring in the system foundation towards augmenting the unwavering quality, versatility, wellbeing and accessibility of the system. So it is utilized for resource allocation. Since for better performance and to overcome the drawback of fair scheduling to all users a hybridisation process is included. Similarly, the job scheduling refers to adjusting resources between different resource users based on certain criteria i.e. the combined scheduling algorithms are utilized were both priorities as well as time are to be considered as future work may provide better results. Also by the consideration of effective measures such as priority and time for scheduling, the throughput of the network is improved. HPC does not consider the process of software-controlled prioritization mechanisms as self-healing in the prevailing way of networks. Self-healing
mechanisms include features such as identification, self-repair, and self-configuration in the network infrastructure to maximize the reliability, flexibility, security, and availability of HPC networks. Thus in our review framework by considering the hybridization of HPC Sheduler and adCFS for resource allocation and Job scheduling in order to make it more effective, thereby enables the efficiency and granularity of the control input necessary to achieve fast reconfiguration which in turn enabling the performance of the HPC network to be improved with SDN. In this paper we conduct a literature review about dynamic job scheduling and resource management techniques for HPC. While there have been several advancements in static scheduling for HPC, dynamic scheduling has not seen a comparable progress from the time it was concieved. This is mainly because dynamic scheduling has never been an imperative requirement in the past. However, with the recent progresses that HPC has gone through, the motivation to investigate and propose practical methods for the same comes mainly from: (i) scientific applications exploring new domains with potentially varying resource requirements during the different phases of the application, (ii) the growing ease of realizing malleability in upcoming cluster systems, and (iii) the exploration of heterogenous architectures with various network-attached resources including accelerators and storage. Therefore, we consider the novel and practical methods for dynamic scheduling and resource management that can be integrated in SDN.
5. CONCLUSION
In this paper it is surveyed about the techniques and strategies for improving the performance of HPC based on resource allocation and job scheduling. Many existing works based on the efficient resource allocation with the enhanced performance and job scheduling with better performance are discussed along with that their drawbacks are also stated followingly. Similarly, as a conclusion from the review made the path way for future direction also discussed. Thus the performance of HPC with SDN can be improved with combined resource allocation and job scheduling strategies.
REFERENCES
[1] J.R. Lee, R.S. Canon, T. Declerck, B. Draney, D. Paul and D. Skinner, “Enhancing supercomputing with software defined networking,” In 2018 International Conference on Information Networking (ICOIN), 2018, pp. 571-576. [2] X. Zeng, D. Wang, S. Han, W. Yao, Z. Wang, and R. Chen,
“An Effective Load Balance Using Link Bandwidth for SDN-Based Data Centers,” In International Conference on Artificial Intelligence and Security, 2019, pp. 256-265. [3] M. Hayashida, S. Rivera, J. Griffioen, Z. Fei, and Y. Song,
“Debugging SDN in HPC Environments,” In Proceedings of the Practice and Experience on Advanced Research Computing, 2018, pp. 7.
[4] J. Bao, J. Wang, Q. Qi, and J. Liao, “ECTCP: An Explicit Centralized Congestion Avoidance for TCP in SDN-based Data Center,” In 2018 IEEE Symposium on Computers and Communications (ISCC), 2018, pp. 00347-00353.
[5] S. Aglianò, M. Ashjaei, M. Behnam, and L.L. Bello, “Resource management and control in virtualized SDN networks,” In 2018 Real-Time and Embedded Systems and Technologies (RTEST), 2018, pp. 47-53.
Broad Band networks,” In 2018 24th Asia-Pacific Conference on Communications (APCC), 2018, pp. 263-268.
[7] M. Amiri, H. Al Osman, and S. Shirmohammadi, “Game-Aware and SDN-Assisted Bandwidth Allocation for Data Center Networks,” In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018, pp. 86-91.
[8] S. Prabhakaran, “Dynamic resource management and job scheduling for high performance computing (Doctoral dissertation, Technische Universität Darmstadt),” 2016. [9] M.A. Obaida, and J. Liu, “Simulation of HPC job scheduling
and large-scale parallel workloads,” In 2017 Winter Simulation Conference (WSC), 2017, pp. 920-931.
[10] S. Sindhu, and S. Mukherjee, “A Dynamic List Scheduling Algorithm for Scheduling HPC Application in a Cloud Environment.”
[11] Vidya Chitre, Deven Shah, “Optimizing Task Scheduling for HPC using Software Defined Network.”
[12] J. Meng, S. McCauley, F. Kaplan, V.J. Leung, and A.K. Coskun, “Simulation and optimization of HPC job allocation for jointly reducing communication and cooling costs,” Sustainable Computing: Informatics and Systems, Vol. 6, 2015, pp. 48-57.
[13] H. Xue, K.T. Kim, and H.Y. Youn, “Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization,” Sensors, Vol. 19, No. 2, 2019, pp. 311.
[14] A. Singh, and M. Malhotra, “A comparative analysis of resource scheduling algorithms in cloud computing,” Am. J. Comp. Sci. Eng. Surv, Vol. 1, No. 1, 2013, pp. 1-19. [15] A. Gainaru, H. Sun, G. Aupy, Y. Huo, B.A. Landman, and P.
Raghavan, “On-the-fly scheduling versus reservation-based scheduling for unpredictable workflows,” The International Journal of High Performance Computing Applications, 2019, 1094342019841681.
[16] M. Hovestadt, O. Kao, A. Keller, and A. Streit, “Scheduling in HPC resource management systems: Queuing vs. planning,” In Workshop on Job Scheduling Strategies for Parallel Processing, 2003, pp. 1-20.
[17] Z. Li, B. Chen, X. Liu, D. Ning, Q. Wei, Y. Wang, and X. Qiu, “Bandwidth-Guaranteed Resource Allocation and Scheduling for Parallel Jobs in Cloud Data Center,” Symmetry, Vol. 10, No. 5, 2018, pp. 134.
[18] A. Tootoonchian, and Y. Ganjali, “Hyperflow: A distributed control plane for openflow,” In Proceedings of the 2010 internet network management conference on Research on enterprise networking, Vol. 3, 2010.
[19] S. Vamshi Krishna, A. Srivastava, S.J. Wagh, and S. Sabbi, “Mongrel of FA-CRA and CG-CRA: Self Healing Resource Allocation with Optimized Job-Scheduling in SDN-Accelerated HPC Infrastructure,” Journal of Intelligent & Fuzzy Systems, 2019, pp. 1-12.
[20] T.Z. Fu, J. Ding, R.T. Ma, M. Winslett, Y. Yang, and Z. Zhang, “DRS: dynamic resource scheduling for real-time analytics over fast streams,” In 2015 IEEE 35th International Conference on Distributed Computing Systems, 2015, pp. 411-420.
[21] H. Kim, J. Jo, C. Park, S. Ahn, H. Chin, P. Park, and Y. Kim, “Dynamic Resource Scheduling Algorithm for Public Safety Network,” In 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim), 2018, pp. 127-132.
[22] J. Wan, B. Chen, M. Imran, F. Tao, D. Li, C Liu, and S. Ahmad, “Toward dynamic resources management for IoT-based manufacturing,” IEEE Communications Magazine, Vol. 56, No. 2, 2018, pp. 52-59.
[23] X. Xu, S. Fu, Q. Cai, W. Tian, W. Liu, W. Dou, X. Sun, and A.X. Liu, “Dynamic resource allocation for load balancing in fog environment,” Wireless Communications and Mobile Computing, 2018.
[24] X. Guan, J. Yin, X. Wan, T. Wang, and G. Bai, “A stackelberg game model for dynamic resource scheduling in edge computing with cooperative cloudlets,” In 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2018, pp. 1-2.
[25] X. Wang, K. Wang, S. Wu, S. Di, H. Jin, K. Yang, and S. Ou, “Dynamic resource scheduling in mobile edge cloud with cloud radio access network,” IEEE Transactions on Parallel and Distributed Systems, Vol. 29, No. 11, 2018, pp. 2429-2445.
[26] G. Domeniconi, E.K. Lee, and A. Morari, “CuSH: Cognitive ScHeduler for Heterogeneous High Performance Computing System,” 2019.
[27] Z. Li, Z.L. Deng, and T.F. Zhang, “Research on optimal task scheduling algorithm based on sdn architecture,” International Journal of Grid and Distributed Computing, Vol. 9, No. 10, 2016, pp. 221-230.
[28] R. Baraglia, G. Capannini, P. Dazzi, and G. Pagano, “A multi-criteria job scheduling framework for large computing farms,” Journal of Computer and System Sciences, Vol. 79, No. 2, 2013, pp. 230-244.
[29] S. Zhang, Y. Wu, S. Zhou, and Z. Niu, “Traffic-aware network planning and green operation with BS sleeping and cell zooming,” IEICE Transactions on Communications, Vol. 97, No. 11, 2014, pp. 2337-2346.
[30] N. Moganarangan, R.G. Babukarthik, S. Bhuvaneswari, M.S. Basha, and P. Dhavachelvan, “A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach,” Journal of King Saud University-Computer and Information Sciences, Vol. 28, No. 1, 2016, pp. 55-67.
[31] J. Song, F. Yang, Y. Zhou, S. Wan, and H.R. Wu, “QoE evaluation of multimedia services based on audiovisual quality and user interest,” IEEE Transactions on Multimedia, Vol. 18, No. 3, 2016, pp. 444-457.
[32] L.F. Gallardo, G. Mittag, S. Möller, and J. Beerends, “Variable voice likability affecting subjective speech quality assessments,” In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1-6. [33] J. Zheng, C. Liu, and L. Chu, “BARA: A Battery Energy and
Data Rate Aware Resource Allocation Algorithm for QoE in D2D Communication Underlaying Cellular Networks,” In 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1-6.
[34] M. S. Corson, R. Laroia, and J. Li et al., “Toward proximity-aware internetworking,” Wirel. Commun. IEEE, Vol. 1, No. 6, 2010, pp. 26–33.
[35] S.N.M. Shah, M.N.B. Zakaria, A.K.B. Mahmood, A.J. Pal, and N. Haron, “Agent based priority heuristic for job scheduling on computational grids,” Procedia Computer Science, Vol. 9, 2012, pp. 479-488.
[37] D. Thilagavathi, and A.S. Thanamani, “Scheduling in high performance computing environment using firefly algorithm and intelligent water drop algorithm,” Int. J. Eng. Trends Technol, Vol. 14, No. 1, 2014, pp. 8-12.
[38] S.H.H. Madni, M.S.A. Latiff, M. Abdullahi, and M.J. Usman, “Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment,” PloS one, Vol. 12, No. 5, 2017, pp. 0176321.
[39] V. Lopez, A. Jokanovic, M. D’Amico, M. Garcia, R. Sirvent, and J. Corbalan, “Djsb: Dynamic job scheduling benchmark,” In Workshop on Job Scheduling Strategies for Parallel Processing, 2017, pp. 174-188.
[40] S. Jamalian, and H. Rajaei, “ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud,” In 2015 IEEE International Conference on Cloud Engineering, 2015, pp. 329-334.
[41] Z. Li, Z.L. Deng, and T.F. Zhang, “Research on optimal task scheduling algorithm based on sdn architecture,” International Journal of Grid and Distributed Computing, Vol. 9, No. 10, 2016, pp. 221-230.
[42] K.K. Gupta, B. Nath, R. Kotagiri, “Layered approach using conditional random fields for intrusion detection,” IEEE Trans. Dependable Sec. Comput, 2010.
[43] Y. Liu, N. Bobroff, L. Fong, S. Seelam, and J. Delgado, “New metrics for scheduling jobs on cluster of virtual machines,” In 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, 2011, pp. 1001-1008.
[44] J. Kobus, and R. Szklarski, “Completely Fair Scheduler and its tuning. draft on Internet,” 2009.
[45] E.J. Alzahrani, Z. Tari, Y.C. Lee, D. Alsadie, and A.Y. Zomaya, “adCFS: Adaptive completely fair scheduling policy for containerised workflows systems,” In 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 2017, pp. 1-8.
[46] C. Boneti, R. Gioiosa, F.J. Cazorla, and M. Valero, “A dynamic scheduler for balancing HPC applications,” In Proceedings of the 2008 ACM/IEEE conference on Supercomputing, 2008, pp. 41.
[47] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, J. Turner, “Openflow: enabling innovation in campus networks,” ACM SIGCOMM Comput. Commun. Rev, 2008.
[48] M. Khatari, and G. Samara, “Congestion control approach based on effective random early detection and fuzzy logic,” arXiv preprint arXiv:1712.04247, 2017.
[49] M. Bahnasy, H. Elbiaze, and B. Boughzala, “Zero-queue ethernet congestion control protocol based on available bandwidth estimation,” Journal of network and computer applications, Vol. 105, 2018, pp. 1-20.
