Optimizing Integrated Application Performance with Cache-Aware Metascheduling

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.

A parallel rough set based dependency calculation method for efficient feature selection

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.

Fog Computing (FC) paradigm offers significant potential for hosting diverse delay-sensitive Internet of Things (IoT) applications. However, the limited resources of fog devices pose significant challenges for deploying multiple applications, particularly in heterogeneous and dynamic IoT scenarios, due to the absence of effective mechanisms for resource estimation and discovery. An efficient resource allocation strategy is crucial for meeting the Quality of Service (QoS) requirements of IoT applications while enhancing overall system performance. Identifying the optimal allocation strategy for IoT applications with multiple QoS parameters is a complex and computationally intensive challenge, classified as an NP-complete problem. This paper proposes a Multi-Objective Optimization Algorithm (MOOA) for optimal resource allocation using the Osprey Optimization Algorithm (OOA) to efficiently allocate available resources. The proposed algorithm was evaluated against existing approaches, including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), under varying task loads ranging from 100 to 500 tasks. The simulation results demonstrate significant performance improvements, including an average reduction in execution time by 12.45% compared to PSO and 22.97% compared to GA, response time by 32.57% compared to GA and 24.45% compared to PSO, and completion time by 44.39% compared to GA and 33.23% compared to PSO. These findings highlight the proposed algorithm’s ability to efficiently handle task allocation in dynamic FC environments and its potential to address complex QoS requirements in real-world IoT applications.

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system's memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference.

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which takes Hadoop cluster load into consideration. We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential execution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization. The results reported in this paper were obtained in a pilot project to assess the feasibility of migrating A/B testing from Teradata + SAS analytics infrastructure to Hadoop. This work was performed on eBay production Hadoop cluster.

We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capacitated vehicle routing problem (CVRP), we leverage heuristic solvers traditionally used in logistics and transportation planning to optimize task execution times. As exemplified using an 8-channel pipette with individually controllable tips, our approach demonstrates robust optimization performance across different labware formats (e.g., well-plates, vial holders), achieving up to a 37% reduction in execution time for randomly generated tasks compared to the baseline sorting method. We further apply the method to a real-world high-throughput materials discovery campaign and observe that 3 minutes of optimization time led to a reduction of 61 minutes in execution time compared to the best-performing sorting-based strategy. Our results highlight the potential for substantial improvements in throughput and efficiency in automated laboratories without any hardware modifications. This optimization strategy offers a practical and scalable solution to accelerate combinatorial experimentation in areas such as drug combination screening, reaction condition optimization, materials development, and formulation engineering.

Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. A number of design choices and the addition of preprocessing and postprocessing steps lead to versions of the algorithms which differ in solution qualities and execution times. In this paper the performances of these versions are critically evaluated and compared for test cases with different features. The performance criteria are solution quality, execution time, robustness, bias and parallelizability. Experimental results show that the physical algorithms produce better solutions than those of recursive bisection methods and that they have diverse properties. Hence, different algorithms would be suitable for different applications. For example, the annealing and genetic algorithms produce better solutions and do not show a bias towards particular problem structures, but they are slower than the neural network algorithms. Preprocessing graph contraction is one of the additional steps suggested for the physical methods. It produces a significant reduction in execution time, which is necessary for their applicability to large problems.

Experimental observations and advanced computer simulations in High Energy Physics (HEP) paved the way for the recent discoveries at the Large Hadron Collider (LHC) at CERN. Currently, Monte Carlo simulations account for a very significant amount of computational resources of the Worldwide LHC Computing Grid (WLCG). The current growth in available computing performance will not be enough to fulfill the expected demand for the forthcoming High Luminosity run (HL-LHC). More efficient simulation codes are therefore required. This study focuses on evaluating the impact of different build methods on the simulation execution time. The Geant4 toolkit, the standard simulation code for the LHC experiments, consists of a set of libraries which can be either dynamically or statically linked to the simulation executable. Dynamic libraries are currently the preferred build method. In this work, three versions of the GCC compiler, namely 4.8.5, 6.2.0 and 8.2.0 have been used. In addition, a comparison between four optimization levels (Os, O1, O2 and O3) has also been performed. Static builds for all the GCC versions considered, exhibit a reduction in execution times of about 10%. Switching to newer GCC version results in an average of 30% improvement in the execution time regardless of the build type. In particular, a static build with GCC 8.2.0 leads to an improvement of about 34% with respect to the default configuration (GCC 4.8.5, dynamic, O2). The different GCC optimization flags do not affect the execution times.

SummaryMobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.

As transistors keep shrinking and on-chip data caches keep growing, static power dissipation due to leakage of caches takes an increasing fraction of total power in processors. Several techniques have already been proposed to reduce leakage power by turning off unused cache lines. However, they all have to pay the price of performance degradation. This paper presents a cache architecture, the snug set-associative (SSA) cache, that does not only cut most of static power dissipation but also reduces execution times. The SSA cache reduces leakage power by implementing the minimum set-associative scheme, which only activates the minimal numbers of ways in each cache set, while the performance losses incurred by this scheme are compensated by the base-offset load/store queues. These two techniques are both developed based on the principle of locality and they work together nicely - experimental results show that the minimum set-associative scheme can cut static power consumption of the L1 data cache by 90% on average for SPECint2000 benchmarks, while the execution times are reduced by 3% when the default 8-entry load/store queue is modified to the base-offset design. Furthermore, the SSA cache can trim the leakage power of L2 data cache by 96% on average while still accomplishing a 3% reduction in execution times.

Boosting memory access locality of the Spectral Element Method with Hilbert space-filling curves

Multithreaded architectures are emerging as an important class of parallel machines. By allowing fast context switching between threads on the same processor, these systems hide communication and synchronization latencies and allow scalable parallelism for dynamic and irregular applications. Thread partitioning is the most important task in compiling high-level languages for multithreaded architectures. Non-preemptive multithreaded architectures, which can be built from off-the-shelf components, require that if a thread issues a potentially remote memory request, then any statement that is dependent upon this request must be in a separate thread.When performing thread partitioning on codes that use pointer-based recursive data structures, it is often difficult to extract accurate dependence information. As a result, threads of unnecessarily small granularity get generated, which, because of thread switching costs, leads to increased execution time. In this paper, we present three techniques that lead to improved extraction and representation of dependence information in the presence of structured control flow, references through fields of structures, and pointer-based data structures. The benefit of these techniques is the generation of coarser-grained threads and, therefore, decreased execution time. Our experiments were performed using the EARTH-C compiler and the EARTH multithreaded architecture model emulated on both a cluster of Pentium PCs and a distributed memory multiprocessor. On our set of 6 pointer-based programs, these techniques reduced the static number of threads by 38%. Reductions in execution times ranged from 16% to 45% on the four programs we measured runtime performance.

In this paper we consider the problem of searching for an arbitrarily smart and fast evader in a large environment with a team of unmanned aerial vehicles (UAVs) while providing guarantees of detection. Our emphasis is on the fast execution of efficient search strategies that minimize the number of UAVs and the search time. We present the first approach for computing fast guaranteed search strategies utilizing additional searchers to speed up the execution time and thereby enabling large scale UAV search. In order to scale to very large environments when using UAVs one would either have to overcome the energy limitations of UAVs or pay the cost of utilizing additional UAVs to speed up the search. Our approach is based on coordinating UAVs on sweep lines, covered by the UAV sensors, that move simultaneously through an environment. We present some simulation results that show a significant reduction in execution time when using multiple UAVs and a demonstration of a real system with three AR.Drone 2.0.

This paper presents a multicore implementation of FrodoKEM, a post-quantum cryptographic scheme, targeting near real-time Internet of Things (IoT) applications. By combining tiled matrix multiplication and data parallelism, we enable FrodoKEM to efficiently operate under the memory and performance constraints of multicore microcontrollers. Implemented on the ESP32-S3, the single-core version demonstrates feasibility through memory optimization, while the dual-core version achieves a 44.5% reduction in execution time, meeting the latency requirements of IoT systems.