An analysis of dynamic scheduling techniques for symbolic applications

A weighted-fair-queuing (WFQ)-based dynamic request scheduling approach in a multi-core system

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.

Existing real-time multicore schedulers use either global or partitioned scheduling technique to schedule real-time tasks. Partitioned scheduling is a static approach in which, a task is mapped to a per-processor ready queue prior to scheduling it and it cannot migrate. Partitioned scheduling makes ineffective use of the available processing power and incurs high overhead when real-time tasks are dynamic in nature. Global scheduling is a dynamic scheduling approach, where the processors share a single ready-queue to execute the highest priority tasks. Global scheduling allows task migration which results in high scheduling overhead. In this paper, we present a dynamic partitioning based scheduling of real-time tasks, called DP scheduling. In DP scheduling, jobs of tasks are assigned to cores when they are released and remain in the same core till they finish execution. The partitioning in DP scheduling is done based on the slack time and priority of jobs. If a job cannot be allocated to any core, then it is split, and executed on more than one core. DP scheduling technique attempts to retain good features of both global and partitioned scheduling without compromising on resource utilization, and at the same time, also tries to minimize the scheduling overhead. We have tested DP scheduling technique with EDF scheduling policy at each core, and we term this scheduling algorithm as DP-EDF. The performance of DP-EDF scheduling algorithm has been evaluated using simulation study and its implementation in LITMUS-RT on a 64-bit intel processor with eight logical cores. Both simulation and experimental results show that DP-EDF scheduling algorithm has better performance in terms of resource utilization, and comparable or better performance in terms of scheduling overhead in comparison to contemporary scheduling algorithms.

Current and future parallel programming models need to be portable and efficient when moving to heterogeneous multi-core systems. OmpSs is a task-based programming model with dependency tracking and dynamic scheduling. This paper describes the OmpSs approach on scheduling dependent tasks onto the asymmetric cores of a heterogeneous system. The proposed scheduling policy improves performance by prioritizing the newly-created tasks at runtime, detecting the longest path of the dynamic task dependency graph, and assigning critical tasks to fast cores. While previous works use profiling information and are static, this dynamic scheduling approach uses information that is discoverable at runtime which makes it implementable and functional without the need of an oracle or profiling. The evaluation results show that our proposal outperforms a dynamic implementation of Heterogeneous Earliest Finish Time by up to 1.15x, and the default breadth-first OmpSs scheduler by up to 1.3x in an 8-core heterogeneous platform and up to 2.7x in a simulated 128-core chip.

In an age when enterprises are increasingly dependent on their suppliers and customers, individual companies should broaden their scope in order to include other organisations participating in the same value chain. Consequently, different parties of the chain ought to exchange information to make sound decisions in order to cut down global costs or improve customer service. This paper proposes a multi–agent approach for dynamic production and distribution scheduling in a simple supply chain. The approach is based on the continuous supervision of the active schedules and routes, in order to detect possible exceptions and apply corrective actions in a real–time and coordinated manner with other parties. Finally, the paper focuses on the design phase of the distribution stage, where the problem is mathematically formulated as a dynamic vehicle routing problem with time windows and backhauls (DVRP–TWB), and three combinatorial optimisation meta–heuristics are proposed as solving techniques.

This paper presents new algorithms for keeping the supply and demand balanced. Here, a power management scheme is proposed which employs a static and dynamic task scheduling approach based on aging priority factor. To implement the proposed task scheduling algorithm, loads are categorized into uninterruptable and interruptible (or deferrable) loads and then an adaptive priority, based on an aging concept, is assigned to improve the effectiveness of peak shaving while considering the consumer's comfort (by responding to the loads in the desirable interval). The scheduler uses the proposed algorithm to choose between different loads with different priorities at each time slot and the priority will be updated for the next time interval. This paper also discusses a new strategy for taking the peak hours into consideration for adjusting priority factors and helping the power system to have an efficient load dispatch. This goal is achieved by using an artificial neural network to forecast the next day load profile and make the scheduler aware of the peak hours. The system is simulated using MATLAB software and the results show that the Peak to Average Ratio (PAR) is significantly improved by applying the proposed algorithm to real data.

Body Area Networks (BANs), targeting everywhere anytime health monitoring, operate in license-exempt frequency bands. They require a cross layer design that jointly considers the requirements of (1) access delay to the medium and (2) packet delivery ratio (PDR). In this paper, we experimentally evaluate the PDR of a BAN under static and dynamic scheduling schemes. Investigations show that static multi-hop routing enhances PDR with the help of packet relaying, but at the cost of using more transmission slots in the shared medium, which may compromise access delay. To reduce this cost, we propose a dynamic scheduling approach that adapts to BAN topology changes resulting from body movements. Our extensive experimental results indicate that for BANs colloccated with other wireless systems, our dynamic multi-hop scheduling is mandatory for satisfaction of the PDR constraint.

Current microprocessors aggressively exploit instruction-level parallelism (ILP) through techniques such as multiple issue, dynamic scheduling, and non-blocking reads. Recent work has shown that memory latency remains a significant performance bottleneck for shared-memory multiprocessor systems built of such processors.This paper provides the first study of the effectiveness of software-controlled non-binding prefetching in shared memory multiprocessors built of state-of-the-art ILP-based processors. We find that software prefetching results in significant reductions in execution time (12% to 31%) for three out of five applications on an ILP system. However, compared to previous-generation system, software prefetching is significantly less effective in reducing the memory stall component of execution time on an ILP system. Consequently, even after adding software prefetching, memory stall time accounts for over 30% of the total execution time in four out of five applications on our ILP system.This paper also investigates the interaction of software prefetching with memory consistency models on ILP-based multiprocessors. In particular, we seek to determine whether software prefetching can equalize the performance of sequential consistency (SC) and release consistency (RC). We find that even with software prefetching, for three out of five applications, RC provides a significant reduction in execution time (15% to 40%) compared to SC.

EPIC architectures rely heavily on state-of-the-art compiler technology to deliver optimal performance while keeping hardware design simple. It is generally believed that an optimizing compiler has an enormous scheduling window to exploit instruction-level parallelism (ILP) since the compiler orchestrates the entire program. Many state-of-the-art compilers typically confine optimizations to loop boundaries (e.g. software pipelining, trace scheduling, and loop unrolling) and function boundaries (e.g. loop peeling, loop exchanges, invariant hoisting, and global optimizations). Although techniques such as function inlining and interprocedural optimizations can alleviate these constraints to a limited extent, loop and function boundaries are often the real scopes of the compiler scheduler. Several previous ILP studies have explored the limits of parallelism on dynamic superscalar machines; however, those results are not applicable to EPIC architectures since they rely on dynamic scheduling, not static code scheduling by the compiler, to reorder instructions. In this paper, we evaluate the limits in ILP obtained through compiler scheduling alone. We quantify these limits as more restrictive scheduling constraints are imposed-starting from inter-procedural code scheduling, to intra-procedural and finally to loop-confined code scheduling.

Dynamic gain scheduled process control

Heuristic resolution for multiple inheritance in object-oriented expert system building tools

This paper deals with Intellectual Property (IP) core design for dynamic task scheduling to support Out-of-Order (OoO) execution in Multiprocessor System-on-Chip (MPSoC) environment. MPSoC is one of the most promising future processor architecture. But such systems have to face challenges in the context of OoO execution during dynamic scheduling due to data dependencies like Read-after-Write (RAW), Write-after-Write (WAW) and Write-after-Read (WAR). Due to these dependencies stalling problem occur during OoO execution. In order to solve this stalling problem and achieve task level parallelism (TLP), Scoreboarding algorithm with register renaming technique is designed. As these dependencies impose challenging constraints on the direct use of techniques like OoO execution, register renaming and dynamic scheduling, a synthesizable IP core is designed. The simulation results show that the design can analyze all the task dependencies during run-time and resolves them at TLP. This algorithm is able to resolve 100% RAW, WAW and WAR hazards which are not solvable at instruction level parallelism (ILP).

Dynamic job shop scheduling is an important but difficult problem in manufacturing systems which becomes complex particularly in uncertain environments with varying shop scenarios. Genetic programming based hyper-heuristics (GPHH) have been a successful approach for dynamic job shop scheduling (DJSS) problems by enabling the automated design of dispatching rules for DJSS problems. GPHH is a computationally intensive and time consuming approach. Furthermore, when complex shop scenarios are considered, it requires a large number of training instances. When faced with multiple shop scenarios and a large number of problem instances, identifying good training instances to evolve dispatching rules which perform well over diverse scenarios is of vital importance though challenging. Essentially this requires the tackling of exploration versus exploitation trade-off. To address this challenge, we propose a new framework for GPHH which incorporates active sampling of good training instances during evolutionary process. We propose a sampling algorithm based on the ε-greedy method to evolve a set of dispatching rules. Through our experiments, we demonstrate the ability of our framework to efficiently identify useful training instances toward evolving dispatching rules which outperform the existing training methods.

An effective query processing plays an important role in the uncertain data streams. Specifically, multiple top-k queries processing on uncertain data streams obtained from large applications of several fields such as sensor network monitoring and internet traffic control requires periodic execution of queries and sharing results among them. The system that monitors uncertain events in such data streams manipulates the top-k queries. Here the problem is that systems were not designed to allow query results sharing which in turn leads to high computation cost and inaccurate response from the system. To overcome these issues (Queries results sharing), the system using a sampling algorithm for sample the top possible worlds from well-known possible worlds based on their high probability. System uses an optimal dynamic programming approach that split the multiple queries into number of groups. Then the query groups are scheduled and planned for sharing results to yield minimum computation cost. A faster greedy algorithm is used to reduce the time and storage space of the top-k queries based on the greedy rule. Thus the proposed approach allows sharing computation among multiple top-k queries and generates best plan of query execution.