Gang Scheduling Research Articles

EASYR: E nergy-Efficient A daptive Sy stem R econfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors

The increasing computing demands of autonomous driving applications have driven the adoption of multicore processors in real-time systems, which in turn renders energy optimizations critical for reducing battery capacity and vehicle weight. A typical energy optimization method targeting traditional real-time systems finds a critical speed under a static deadline, resulting in conservative energy savings that are unable to exploit dynamic changes in the system and environment. We capture emerging dynamic deadlines arising from the vehicle’s change in velocity and driving context for an additional energy optimization opportunity. In this article, we extend the preliminary work for uniprocessors [ 66 ] to multicore processors, which introduces several challenges. We use the state-of-the-art real-time gang scheduling [ 5 ] to mitigate some of the challenges. However, it entails an NP-hard combinatorial problem in that tasks need to be grouped into gangs of tasks, gang formation, which could significantly affect the energy saving result. As such, we present EASYR, an adaptive system optimization and reconfiguration approach that generates gangs of tasks from a given directed acyclic graph for multicore processors and dynamically adapts the scheduling parameters and processor speeds to satisfy dynamic deadlines while consuming as little energy as possible. The timing constraints are also satisfied between system reconfigurations through our proposed safe mode change protocol. Our extensive experiments with randomly generated task graphs show that our gang formation heuristic performs 32% better than the state-of-the-art one. Using an autonomous driving task set from Bosch and real-world driving data, our experiments show that EASYR achieves energy reductions of up to 30.3% on average in typical driving scenarios compared with a conventional energy optimization method with the current state-of-the-art gang formation heuristic in real-time systems, demonstrating great potential for dynamic energy optimization gains by exploiting dynamic deadlines.

Tight necessary feasibility analysis for recurring real-time tasks on a multiprocessor

One of the important design issues for time-critical embedded systems is to derive necessary conditions that meet all job deadlines invoked by a set of recurring real-time tasks under a computing resource (called feasibility). To this end, existing studies focused on how to derive a tight lower-bound of execution requirement (i.e., demand) of a target set of real-time tasks. In this paper, we address the following question regarding the supply provided by a multiprocessor resource: is it possible for a real-time task set to always utilize all the provided supply? We develop a systematic approach that i) calculates the amount of supply proven unusable, ii) finds a partial schedule that yields a necessary condition to minimize the amount of unusable supply, and iii) uses the partial schedule to further reclaim unusable supply. While the systematic approach can be applied to most (if not all) recurring real-time task models, we show two examples how the approach can yield tight necessary feasibility conditions for the sequential task model and the gang scheduling model. We demonstrate the proposed approach finds a number of additional infeasible task sets which have not been proven infeasible by any existing studies for the task models.

Enhancement of Resource Scheduling on Gui Based Operating System

In computer architecture, multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism. Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. Second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2?1/m). Both algorithms are also evaluated through intensive numerical experiments. In our research we have enhanced capability of Gang Scheduling by integration of multi core processor & Cache & make simulation of performance in MATLAB.

Tardiness Bounds for Sporadic Gang Tasks Under Preemptive Global EDF Scheduling

Following the trend of increasing autonomy in cyber-physical systems, parallel embedded architectures have enabled devices to better handle the large streams of data and intensive computation required by such autonomous systems. However, while the explosion of highly-parallel platforms has seen a proportional growth in the number of applications/devices that utilize these platforms, the embedded systems community's understanding of how to build time-predictable, safety-critical systems with parallel platforms has not kept pace. As a well-motivated but challenging parallel scheduling model, gang scheduling requires all parallel threads of each parallel task to simultaneously execute in unison, which is in contrast to traditional, multi-threaded parallel scheduling, where a parallel task may spawn multiple threads, and each thread will be scheduled independently of other threads of the same task. While increasing research efforts on hard real-time (HRT) gang scheduling have recently been seen, the problem of gang scheduling in the context of soft real-time (SRT) systems, where provably bounded deadline tardiness can be tolerated, has hardly been studied yet. In this article, we derive and prove the first tardiness bounds for sporadic gang task systems under preemptive GEDF scheduling. A total utilization bound for SRT-schedulability is required for ensuring such tardiness bounds but it is shown to be tight with respect to the platform capacity and maximum parallelism-induced idleness. Furthermore, we also empirically evaluate the effects of different degrees of task parallelism upon the SRT-schedulability.

A Multithreading Based Enhanced Process Scheduling Technique for Heterogeneous Distributed Environment

Multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism. Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. Second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2?1/m). Both algorithms are also evaluated through intensive numerical experiments. In our research we have enhanced capability of Gang Scheduling by integration of multi core processor & Cache & make simulation of performance in MATLAB.

Enhancement of Security and Energy Efficiency in Cloud Computing

This paper focuses on the job scheduling in cloud environment. Here the task has been scheduled in cloud and fog. Cloud provides services to heavy application while fog provides service to lighter application. The job scheduler would be helpful to reduce burden of cloud and help in energy optimization. The jobs are scheduled according to their types and priority. Various job scheduling algorithm such as gang scheduling, FCFS and round robin mechanism have been discussed in this research for load balancing and improve the compilation time. The simulation has been made using Matlab on virtual machines.

GPUart - An application-based limited preemptive GPU real-time scheduler for embedded systems

Emerging technologies like autonomous driving entail computational intense software solutions. More and more companies accelerate their embedded applications by General Purpose Computing on the Graphics Processing Unit (GPGPU), in order to overcome those computational demands. Unfortunately, Graphics Processing Units (GPUs) severely lack real-time capability, for example controllable preemption support, which limits their applicability in the embedded domain. We therefore present GPUart, a framework for GPU real-time scheduling. GPUart focuses on embedded systems and requires neither hardware nor driver stack extensions. We propose a software-only approach for preemption, based on the fixed preemption point strategy. In contrast to prior work, GPUart enables preemption inside a thread block by adding fixed preemption points. We further propose a portable high-level resource management concept to enable gang scheduling on GPUs. GPUart can schedule GPU workload either under the Gang-Earliest Deadline First (EDF) or Gang-Fixed Task Priority (FTP) policy. A case-study on Nvidia Tegra X1, using real-world engine management applications from Audi AG and Continental Automotive GmbH, shows that only up to 0.28% additional global memory is required to enable interruptible thread blocks. GPUart reduces the worst observed response times by a factor of up to 221, leading to response times without deadline misses.

Bubble execution

Enabling interactive data exploration at cloud scale requires minimizing end-to-end query execution latency, while guaranteeing fault tolerance, and query execution under resource-constraints. Typically, such a query execution involves orchestrating the execution of hundreds or thousands of related tasks on cloud scale clusters. Without any resource constraints, all query tasks can be scheduled to execute simultaneously (gang scheduling) while connected tasks stream data between them. When the data size referenced by a query increases, gang scheduling may be resource-wasteful or un-satisfiable with a limited, per-query resource budget. This paper introduces B ubble E xecution , a new query processing framework for interactive workloads at cloud scale, that balances cost-based query optimization, fault tolerance, optimal resource management, and execution orchestration. Bubble execution involves dividing a query execution graph into a collection of query sub-graphs (bubbles), and scheduling them within a per-query resource budget. The query operators (tasks) inside a bubble stream data between them while fault tolerance is handled by persisting temporary results at bubble boundaries. Our implementation enhances our JetScope service, for interactive workloads, deployed in production clusters at Microsoft. Experiments with TPC-H queries show that bubble execution can reduce resource usage significantly in the presence of failures while maintaining performance competitive with gang execution.

Resource Allocation Approach using Gang Scheduling: Design and Analysis

Resource Allocation Approach using Gang Scheduling: Design and Analysis

Investigation into Gang scheduling by integration of Cache in Multi core processors

Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. second algorithm is an approximation algorithm based on a fixedpriority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2−1/m). In computer architecture, multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism.

Optimal Scheduling of Periodic Gang Tasks

The gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. We propose two DPFAIR (deadline partitioning) algorithms that schedule all jobs in every interval of time delimited by two subsequent deadlines. These algorithms define a static schedule pattern that is stretched at run-time in every interval of the DPFAIR schedule. The first algorithm is based on linear programming and is the first one to be proved optimal for the considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors and an efficient implementation is fully detailed. The second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2-1/m). Both algorithms are also evaluated through intensive numerical experiments.

AIR

Parallel discrete event simulation (PDES) harnesses parallel processing to improve the performance and capacity of simulation, supporting bigger and more detailed models simulated for more scenarios. The presence of interference from other users can lead to dramatic slowdown in the performance of the simulation. Interference is typically managed using operating system scheduling support (e.g., gang scheduling), a heavyweight approach with some drawbacks. We propose an application-level approach to interference resilience through alternative simulation scheduling and mapping algorithms. More precisely, the most resilient simulators allow dynamic mapping of simulation event execution to processing resources (a work pool model). However, this model has significant scheduling overhead and poor cache locality. Thus, we investigate using application-level interference mitigation where the application detects the presence of interference and reacts by changing the thread task allocation. Specifically, we propose a locality-aware adaptive dynamic mapping (LADM) algorithm that adjusts the number of active threads on the fly by detecting the presence of interference. LADM avoids having the application stall when threads are inactive due to context switching. We investigate different mechanisms for monitoring the level of interference and different approaches for remapping tasks. We show that LADM can substantially reduce the impact of interference while maintaining memory locality.

Performance Analysis of Gang Scheduling in a Grid

Gang scheduling combines time-sharing with space-sharing to ensure a short response time for interactive tasks and high overall system throughput. It has been widely studied in different areas including the Grid. Gang scheduling tries to assign the task belonging to one job to different Grid nodes. During the tasks assignment, there are three targets as follows: (1) to keep the Grid in higher resource utilization, (2) to keep the jobs in a low average waiting time and executing time, and, (3) to keep the system in fairness between jobs. In order to meet these targets, we propose a new model according to the waiting time of the jobs. Then we propose a new scheduling method ZERO---ONE scheduling with multiple targets (ZEROONEMT) to solve the Gang scheduling in the Grid. We have conducted extensive evaluations to compare our method with the existing methods based on a simulation environment and a real log from a Grid. In the experiments, in order to justify our method, different metrics, including adapted first come first served and largest job first served, are selected to test the performance of our methods. Experimental results illustrate that our proposed ZEROONEMT reduces the values in the average waiting time, the average response time, and the standard deviation of waiting time of all the jobs.

Improving CompactMatrix phase in gang scheduling by changing transference condition and utilizing exchange

Gang scheduling (GS) is an efficient scheduling approach for distributed systems. The CompactMatrix is the second phase in GS and plays a main role in accelerating the scheduling of the larger gangs. In this paper, a novel method is proposed to compact the scheduling matrix by using two new ideas. By using simulation, the impact of the proposed ideas was compared to the basic CompactMatrix algorithm, either individually or simultaneously. The results show that the average response time in the Improved CompactMatrix Algorithm (ICMA), compared with the basic CompactMatrix, is reduced about 2 to 20, 0 to 10, and ?5 to 5 percent by selecting the migration overhead values equal to 5, 10, and 15 percent of the time-slices lengths, respectively. As can be seen, ICMA will show better results in smaller migration overheads.

Evaluation of Response Time Using Gang Scheduling Algorithm for B2C Electronic Commerce Architecture Implemented in Cloud Computing Environment by Queuing Models

Cloud Computing refers to the concept of outsourcing the services, computational requirements and data storage to a centralized facility called Cloud. Cloud consists of an assemblage of virtualized resources, which include both computational and storage services that can be provisioned on demand, depending on the users' necessities. Gang Scheduling is one of the most efficient algorithms for scheduling parallel jobs and is already functional in the extents of parallel and distributed systems. This paper associates the enactment of gang job scheduling algorithm in B2C electronic commerce architecture to calculate the total response time of the B2C EC architecture in public cloud, using combination of different queuing models. Results reveal the performance of job scheduling algorithm using each of the queuing models and suggestions are accomplished consequently.

Scheduling of hard real-time multi-phase multi-thread (MPMT) periodic tasks

In this paper we study the scheduling of parallel and real-time recurrent tasks on multiprocessor platforms. Firstly, we propose a new parallel task model which allows recurrent tasks to be composed of several phases, each one composed of several threads. Each thread requires a single processor for execution and can be scheduled simultaneously. We then propose an algorithm to transpose popular Fork-Join task model to our MPMT task model. Secondly, we define several kinds of real-time schedulers that can be applied to our parallel task model. We distinguish between two scheduling classes: Hierarchical schedulers and Global Thread schedulers. We present and prove correct an exact schedulability test for each class. Lastly, we also evaluate the performance of our scheduling paradigm in comparison with Gang scheduling by means of simulations. In this work we extend the work of Lupu and Goossens in Scheduling of hard real-time multi-thread periodic tasks (Real-Time and Network Systems, 2011) which considers mono-phase multi-thread task model. We extend their previous results to a Multi-Phase Multi-Thread task model.

Supporting Overcommitted Virtual Machines through Hardware Spin Detection

Multiprocessor operating systems (OSs) pose several unique and conflicting challenges to System Virtual Machines (System VMs). For example, most existing system VMs resort to gang scheduling a guest OS's virtual processors (VCPUs) to avoid OS synchronization overhead. However, gang scheduling is infeasible for some application domains, and is inflexible in other domains. In an overcommitted environment, an individual guest OS has more VCPUs than available physical processors (PCPUs), precluding the use of gang scheduling. In such an environment, we demonstrate a more than two-fold increase in application runtime when transparently virtualizing a chip-multiprocessor's cores. To combat this problem, we propose a hardware technique to detect when a VCPU is wasting CPU cycles, and preempt that VCPU to run a different, more productive VCPU. Our technique can dramatically reduce cycles wasted on OS synchronization, without requiring any semantic information from the software. We then present a server consolidation case study to demonstrate the potential of more flexible scheduling policies enabled by our technique. We propose one such policy that logically partitions the CMP cores between guest VMs. This policy increases throughput by 10-25 percent for consolidated server workloads due to improved cache locality and core utilization.

Performance Optimization in Gang Scheduling In Cloud Computing

Performance Optimization in Gang Scheduling In Cloud Computing

Special Issue on ‘Performance Evaluation, Modeling and Optimization of Computer Systems’

This Special Issue of SIMULATION: Transactions of The Society for Modeling and Simulation International is dedicated to the performance evaluation, modeling and optimization of computer systems. The aim of this special issue is to publish novel research work that contributes to the development of methodologies, techniques and tools, as well as their application in state-of-the-art computer systems. Some of the papers included were submitted specially for this issue, while others are extended versions of a selection of the best papers accepted and presented at the 2009 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2009. These papers have undergone a new round of reviews to guarantee that all of the papers included in this Special Issue have been rigorously peer-reviewed. The five articles of this Special Issue cover a variety of topics, including: performance evaluation of scheduling algorithms in clusters and distributed systems; reconstruction of load properties from observable traffic; modeling of workflows in heterogeneous distributed systems; and simulation of the performance of Flash memories. This wide range of topics reflects the many facets of this field, which has significantly progressed in the last years. The first paper, ‘Gang Scheduling in a two-cluster system implementing migrations and periodic feedback’, is authored by Zafeirios C Papazachos and Helen D Karatza. They consider scheduling gangs in a homogeneous multicluster system, in the presence of critical sporadic jobs that require immediate execution upon their arrival. Several scheduling algorithms, responsible for allocating the available system resources to the existing jobs, are examined, and their performance is studied using a simulation model. This study includes the effect of migrating tasks from a local queue to the head of another queue, both locally and through the grid. In addition, the paper studies the performance of a grid dispatching algorithm which allocates gangs to the available clusters based on periodic feedback from the clusters. In the second paper, ‘Reconstructing arrival processes to discrete queueing systems by inverse load transformation’, by Stephan Heckmuller and Bernd E Wolfinger, the problem of recovering arrival processes to discrete G/D/1 queuing systems from observable traffic is studied. Independent normally distributed arrivals and autoregressive processes are considered, which are commonly used as a model for highly aggregated traffic such as that of computer networks. Closed-form expressions are obtained for the normal distribution, while for the case of autoregressive processes, a novel use of the Tobit regression model (commonly used in social and economic sciences) allows estimates of the process parameters to be obtained with high accuracy. The estimation procedures are generalized to distributions other than the normal distribution by using the Buckley–James estimator. Finally, the authors demonstrate through simulation that the proposed methods can be used in realistic scenarios such as polling mechanisms in wireless local area networks. In the third paper, ‘Modeling and simulation of distributed computing workflows in heterogeneous network environments’, Qishi Wu and Yi Gu propose a simulation system to study the execution dynamics of distributed computing workflows. The simulator, named SDEDS (Simulation of Dynamic Execution of Distributed Systems), is designed for the performance evaluation of large-scale workflow systems, and it is implemented using multi-threaded programming and an event-driven control structure. It simulates the dynamic execution process of distributed systems with data execution on computer nodes and data transfer along network links, and evaluates the network performance of workflow scheduling or mapping algorithms. Experimental observations collected in real networks and theoretical analysis are used to confirm the simulation-based performance measurements. The fourth paper, ‘An effective duplication-based taskscheduling algorithm for heterogeneous systems’, is authored by Mahsa Hosseinzadeh and Hadi Shahriar Shahhoseini. They present a novel task scheduling

Gang scheduling istn't worth it ... yet

The hardware trend toward higher core counts will likely result in a dynamic, bursty and interactive mix of parallel applications in personal and server computing. We investigate whether gang scheduling can provide performance benefits for applications in this scenario. We present a systematic study of the conditions under which gang scheduling might be better than classical general-purpose OS scheduling, and derive a set of necessary conditions on the workload. We find that these conditions are rarely met today, except in a small subset of workloads, for which we give an example. However, we propose that this subset is potentially important in the future, if (for example) parallel algorithms become increasingly used for real-time computer-human interaction.