Heterogeneous Computing Environments Research Articles

Efficient Algorithm Set Forming for the Computing Resources Distribution in Heterogeneous Dynamic Computational Environments Based on the Ontology Usage

Purpose of research. The purpose of this research is to develop an ontology structure as the basis of a database/knowledge base for selecting effective metaheuristic algorithms for solving the problem of load distribution in heterogeneous distributed dynamic computing environments, taking into account the overhead of data transmission over the network.Methods. The main scientific methods used in this study are domain analysis, methods for constructing subject ontologies, numerical optimization methods and computer modeling.Since the literature does not present resource allocation planning models that would take into account geographic distribution, the presence of intermediate data transmission routes, the dynamics of topologies and load, as well as system heterogeneity in terms of criteria for assessing the quality of load distribution, this article proposes a new model that takes into account these features. The complexity of solving a planning problem becomes one of the variable parameters, which has a significant impact on the planning result: with a decrease in the complexity of calculations, the result deteriorates accordingly. Therefore, a greedy strategy is proposed as a solution method: from the optimization methods to be considered, select the least labor-intensive one that would allow obtaining the best result in the allotted time. Test runs of simulated annealing algorithms demonstrate different effectiveness under different initial conditions of the problem; therefore, it is advisable for selected classes of problems to choose algorithms that are effective in terms of solution quality and labor intensity.Results. The result of the study is the structure of the ontology of effective algorithms. Also, the results are instances of simulated annealing algorithms and tasks included in the ontology, related by the “efficiency” relation.Conclusion. This article proposes the structure of an ontology of effective optimization algorithms and an approach to solving the problem of distributing the computational load, taking into account the complexity of the distribution procedure through the “greedy” selection of the most effective optimization algorithms.

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Contemporary HPC hardware typically provides several levels of parallelism, e.g. multiple nodes, each having multiple cores (possibly with vectorization) and accelerators. Efficiently programming such systems usually requires skills in combining several low-level frameworks such as MPI, OpenMP, and CUDA. This overburdens programmers without substantial parallel programming skills. One way to overcome this problem and to abstract from details of parallel programming is to use algorithmic skeletons. In the present paper, we evaluate the multi-node, multi-CPU and multi-GPU implementation of the most essential skeletons Map, Reduce, and Zip. Our main contribution is a discussion of the efficiency of using multiple parallelization levels and the consideration of which fine-tune settings should be offered to the user.

Epigenomics Scientific Big Data Workflow Scheduling for Cancer Diagnosis in Health Care Using Heterogeneous Computing Environment

HIGHLIGHTS Analysis of different scheduling techniques for Epigenomics computation. An efficient algorithm that minimize computation time of Epigenomics Application. Performance analysis in High Performance Computing (HPC) environment. Shows better performance over state-of-art algorithms.

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

Stencil Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

The development of parallel applications is a difficult and error-prone task, especially for inexperienced programmers. Stencil operations are exceptionally complex for parallelization as synchronization and communication between the individual processes and threads are necessary. It gets even more difficult to efficiently distribute the computations and efficiently implement communication when heterogeneous computing environments are used. For using multiple nodes, each having multiple cores and accelerators such as GPUs, skills in combining frameworks such as MPI, OpenMP, and CUDA are required. The complexity of parallelizing the stencil operation increases the need for abstracting from the platform-specific details and simplify parallel programming. One way to abstract from details of parallel programming is to use algorithmic skeletons. This work introduces an implementation of the MapStencil skeleton that is able to generate parallel code for distributed memory environments, using multiple nodes with multicore CPUs and GPUs. Examples of practical applications of the MapStencil skeleton are the Jacobi Solver or the Canny Edge Detector. The main contribution of this paper is a discussion of the difficulties when implementing a universal Skeleton for MapStencil for heterogeneous computing environments and an outline of the identified best practices for communication intense skeletons.

Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment

Objectives: The objective of this model is to design a technique to schedule the workload for the computation of web services in the heterogeneous cloud environment. As the Web Service Composition (WSC) for the execution of scientific workload in a heterogeneous cloud computing environment is a challenging task. Modern workload requires dynamic resource provisioning technique as there exist parallelization among sub-tasks and different task demands different Quality of Service (QoS) requirement. Methods: This study presents an Evolutionary Computing based Web Service Composition (EC-WSC) technique to execute a large-scale scientific workload in a heterogeneous cloud environment. A multi-objective metric for improving energy efficiency and resource utilization is modelled. Then, an improved searching mechanism for the dragonfly evolutionary computing algorithm is modelled. Findings: Experiment outcomes show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis when compared with existing resource provisioning models of workload service composition such as Deadline and Budget-Aware Workflow Scheduling (DBAWS)(1), Evolutionary Computing Multi-objective optimization for Hybrid Clouds (EC-MOH)(2), Web Service Composition (WSC)(3), and Evolutionary Multi-Objective Optimization for clouds (EMOC)(4) in terms of heterogeneous computing, workload size, multi-objective optimization, QoS metric, and optimization strategy. Our model EC-WSC has proved to be more efficient in terms of energy efficiency by a reduction of 52.13% and also reduction in execution time by 71% when compared with the WSC(3) existing Web Service Composition model. Novelty: Existing resource provisioning predominantly focused on reducing computation cost and time; however, induces task execution latency and energy overhead. However, EC-WSC is modelled to utilize resources more efficiently and meet task QoS requirements by assuring energy minimization constraints. Keywords: Cloud Computing; Heterogeneous Computing Environment; Multiobjective optimization problem; Resource Provisioning; Workload Scheduling

Design considerations for workflow management systems use in production genomics research and the clinic

The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments.

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment

Heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. Recently, number of multi-objective based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these model induce significant overhead when demand and number of processing core increases. For addressing research problem this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Thus, this paper present Energy Aware Resource Utilization (EARU) model by minimizing energy dissipation and utilizing cache resource more efficiently. The EARU model achieves much lesser execution time, energy consumption, and power consumption when compared with existing multi-objective based and DVFS-based workload scheduling algorithm.

Web Service Composition for Executing Workflows: Open Issues, Challenges, Survey and Future Directions

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more data intensive business and scientific web service applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient web service compositing for executing workflow are in high demand. This paper conduct extensive survey of various existing web service composition technique for executing workflow. From survey it is identified very limited work is done for minimizing workflow execution processing time with minimal energy dissipation considering task deadline requirement under heterogeneous cloud computational environment. This paper identifies open issues and future challenges and presented possible solution in building efficient web service composition for executing workflows under heterogeneous cloud computational environment

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

Statistical Model for Cybercrime Detection in Joined Heterogeneous Cloud Computing Environments

A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments

In this paper, we focus on the problem of optimizing deadline violations for executing tasks in various heterogeneous computational environments. To address the problem, we formulated it as a binary nonlinear programming (BNP) model, which maximize the number of completed tasks and optimize the resource utilization of servers. To solve the BNP model in a polynomial complexity, we propose a heuristic task scheduling method, which iteratively schedules a task to the first core such that the accumulated slack time of all scheduled tasks is minimum, until the core cannot finish any task, and executes tasks with the earliest deadline first in each core to execute as many task as possible in a core. Experiment results based on a real world trace show that our method has upto 100% less task violations, and has the best performance in resource efficiency optimization in overall, compared with eight classical and state-of-the-art heuristic methods.

Multi-Objective Task Scheduling Using Hybrid Whale Genetic Optimization Algorithm in Heterogeneous Computing Environment

The system of cloud computing comprises of several servers that are inter-connected in a datacenter, provisioned dynamically to cater on-demand services through the front-end interface for the clients. Improvement in virtualization technology has made cloud computing a viable option for various application services development. Cloud datacenters process the tasks on the basis of pay as you use manner. Task scheduling is one of the important research challenges in cloud computing. The formulation of task scheduling probes has been depicted to be NP-hard hence identifying the solution for a bigger problem is intractable. The dissimilar feature of cloud resources makes task scheduling non-trivial. NP-hard problem arises due to the dynamic behavior of the dissimilar resources identified in the cloud computing environment. Task scheduling can be optimized using a meta-heuristic algorithm. In this paper, we have combined two meta-heuristic techniques, namely Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) to devise a new hybridized algorithm called as Whale Genetic Optimization Algorithm. Our aim is to minimize the makespan and cost while scheduling the tasks. The simulation is done by using Cloudsim toolkit. The results obtained shows significant reduction in the execution time that was measured in terms of enactment amelioration rate. These results were compared with the classical WOA and standard GA. The results of the proposed technique provide higher quality solution for task scheduling.

Elastic Regression-Tree Learning in a Heterogeneous Computing Environment

Embedded intelligence demands sensor functions with minimal operator interference. While computing resources in close proximity to a device are sometimes limited, more capable computing units suffer from latency in long-range data transmission. Here, we demonstrate a workflow on optimizing sensor performance for individual samples, in which we utilize an elastic regression-tree method to distribute computing tasks between an embedded chipset and a full-fledged workstation. A reconciliation of computing resources of different nature enables an efficient data acquisition in a completely automated manner, crucial to the deployment of edge intelligence in a broad context.

A Novel Task-Duplication Based Clustering Algorithm for Heterogeneous Computing Environments

As a crucial task in heterogeneous distributed systems, DAG-scheduling models a scheduling application with a set of distributed tasks by a Direct Acyclic Graph (DAG). The goal is to assign tasks to different processors so that the whole application can finish as soon as possible. Task Duplication-Based (TDB) scheme is an important technique addressing this problem. The main idea is to duplicate tasks on multiple machines so that the results of the duplicated tasks are available on multiple machines to trade computation time for communication time. Existing TDB algorithms enumerate and test all possible duplication candidates, and only keep the candidates that can improve the overall scheduling. We observe that while a duplication candidate is ineffective at the moment, after other duplications have been applied, this ineffective duplication candidate can become effective, which in turn can cause other ineffective duplications to become effective. We call this phenomenon the chain reaction of task duplication. We propose a novel Task Duplication based Clustering Algorithm (TDCA) to improve the schedule performance by utilizing duplication task more thoroughly. TDCA improves parameter calculation, task duplication, and task merging. The analysis and experiments are based on randomly generated graphs with various characteristics, including DAG depth and width, communication-computing cost ration, and variant computation power of processors. Our results demonstrate that the TDCA algorithm is very competitive. It improves the schedule makespan of task duplication-based algorithms for heterogeneous systems for various communication-computing cost ratios.

Improving task scheduling with parallelism awareness in heterogeneous computational environments

Task scheduling is a key function for executing tasks in heterogeneous computational environments, efficiently. While the available computing resources are not fully used when applying existing scheduling methods as they consider that a task is executed on one single core or on a server without parallel tasks by assuming that the task exhausts the server. Therefore, in this paper, we focus on the problem of executing tasks with deadline constraints with parallelism awareness where the parallel degree of each task can be tuned between one and its maximum according to the available cores of the server it assigned to during its execution. We first model the problem as an optimization problem maximizing the overall utilization of servers, and propose a set of scheduling methods with parallelism awareness (SPA), each of which iteratively allocates as much resources and as soon as possible to the assigned task with the earliest deadline on a server, based on existing scheduling algorithms, and present two SPA instances to illustrate the implement of SPA. Experiment results show a great performance improvement in various aspects, e.g., resource utilization, task violations, finish time, and energy efficiency, when executing tasks heterogeneous computational systems using SPA.

Novel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in Heterogeneous Computing Environments

Cloud computing has become increasingly popular model for delivering applications hosted in large data centers as subscription oriented services. Hadoop is a popular system supporting the MapReduce function, which plays a crucial role in cloud computing. The resources required for executing jobs in a large data center vary according to the job type. In Hadoop, jobs are scheduled by default on a first-come-first-served basis, which may unbalance resource utilization. This paper proposes a job scheduler called the job allocation scheduler (JAS), designed to balance resource utilization. For various job workloads, the JAS categorizes jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue . However, the JAS exhibited a locality problem, which was addressed by developing a modified JAS called the job allocation scheduler with locality (JASL). The JASL improved the use of nodes and the performance of Hadoop in heterogeneous computing environments. Finally, two parameters were added to the JASL to detect inaccurate slot settings and create a dynamic job allocation scheduler with locality (DJASL). The DJASL exhibited superior performance than did the JAS, and data locality similar to that of the JASL.

Building situational awareness for network threats in fog/edge computing: Emerging paradigms beyond the security perimeter model

The growing interest in fog and edge computing is gradually but inexorably outlining new architectural and usage models, distinguished by geographical dispersion and device heterogeneity. Unfortunately, the evolution of cyber-security paradigms has not gone with the same pace, leading to a substantial difficulty in protecting the new forms of distributed and heterogeneous systems against cyber-threats.In this paper, we focus on situational awareness for network threats. We briefly review the main limitations of current cyber-security paradigms with respect to emerging fog/edge architectures, and we discuss how current challenges and emerging trends are pushing from vertical security frameworks to horizontal and distributed architectures. In this respect, we outline the main elements and relevant technologies for a multi-layer framework that create the necessary knowledge and awareness in relation to network threats over large and heterogeneous computing and networking environments.

Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware

Heterogeneous computer environments are becoming commonplace so it is increasingly important to understand how and where we could execute a given algorithm the most efficiently. In this paper we propose a methodology that uses both static source code metrics, and dynamic execution time, power, and energy measurements to build gain ratio prediction models. These models are trained on special benchmarks that have both sequential and parallel implementations and can be executed on various computing elements, e.g., on CPUs, GPUs, or FPGAs. After they are built, however, they can be applied to a new system using only the system’s static source code metrics which are much more easily computable than any dynamic measurement. We found that while estimating a continuous gain ratio is a much harder problem, we could predict the gain category (e.g., “slight improvement” or “large deterioration”) of porting to a specific configuration significantly more accurately than a random choice, using static information alone. We also conclude based on our benchmarks that parallelized implementations are less maintainable, thereby supporting the need for automatic transformations.

Grid Computing: An Insight into Heterogeneous Computing Environment

Grid Computing: An Insight into Heterogeneous Computing Environment