An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cluster Computing - Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable...     

A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.     

Energy and resource efficient workflow scheduling in a virtualized cloud environment

High energy consumption (EC) is one of the leading and interesting issue in the cloud environment. The optimization of EC is generally related to scheduling problem. Optimum scheduling strategy is used to select the resources or tasks in such a way that system performance is not violated while minimizing EC and maximizing resource utilization (RU). This paper presents a task scheduling model for scheduling the tasks on virtual machines (VMs). The objective of the proposed model is to minimize EC, maximize RU, and minimize workflow makespan while preserving the task’s deadline and dependency constraints. An energy and resource efficient workflow scheduling algorithm (ERES) is proposed to schedule the workflow tasks to the VMs and dynamically deploy/un-deploy the VMs based on the workflow task’s requirements. An energy model is presented to compute the EC of the servers. Double threshold policy is used to perceive the server’ status i.e. overloaded/underloaded or normal. To balance the workload on the overloaded/underloaded servers, live VM migration strategy is used. To check the effectiveness of the proposed algorithm, exhaustive simulation experiments are conducted. The proposed algorithm is compared with power efficient scheduling and VM consolidation (PESVMC) algorithm on the accounts of RU, energy efficiency and task makespan. Further, the results are also verified in the real cloud environment. The results demonstrate the effectiveness of the proposed ERES algorithm.

     

OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment

Cluster Computing - Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and...     

Resource scheduling methods in cloud and fog computing environments: a systematic literature review

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments.

     

Hierarchical genetic-based grid scheduling with energy optimization

An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.     

HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex graph considering their impact on others, based on graph topology. This technique is efficient to reduction of completion time of application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in application graph in contrast with other studied algorithms.     

Task scheduling for MapReduce in heterogeneous networks

In this paper, the task scheduling in MapReduce is considered for geo-distributed data centers on heterogeneous networks. Adaptive heartbeats, job deadlines and data locality are concerned. Job deadlines are divided according to the maximum data volume of tasks. With the considered constraints, the task scheduling is formulated as an assignment problem in each heartbeat, in which adaptive heartbeats are calculated by the processing times of tasks, jobs are sequencing in terms of the divided deadlines and tasks are scheduled by the Hungarian algorithm. Taking into account both the data transfer and processing times, the most suitable data center for all mapped jobs are determined in the reduce phase. Experimental results show that the proposed algorithms outperform the current existing ones. The proposals with sorted task-sequences have better performance than those with random task-sequences.     

Auto-scaling techniques for IoT-based cloud applications: a review

Cluster Computing - Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for...     

P-Aware: a proportional multi-resource scheduling strategy in cloud data center

Concentrating on a single resource cannot efficiently cope with the overall high utilization of resources in cloud data centers. Nowadays multiple resource scheduling problem is more attractive to researchers. Some studies achieve progresses in multi-resource scenarios. However, these previous heuristics have obvious limitations in complex software defined cloud environment. Focusing on energy conservation and load balancing, we propose a preciousness model for multiple resource scheduling in this paper. We give the formulation of the problem and propose an innovative strategy (P-Aware). In P-Aware, a special algorithm PMDBP (Proportional Multi-dimensional Bin Packing) is applied in the multi-dimensional bin packing approach. In this algorithm, multiple resources are consumed in a proportional way. Structure and details of PMDBP are discussed in this paper. Extensive experiments demonstrate that our strategy outperforms others both in efficiency and load balancing. Now P-Aware has been implemented in the resource management system in our cooperative company to cut energy consumption and reduce resource contention.     

Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Cluster Computing - Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an...     

Cardiac rehabilitation: a comprehensive review

Cardiac rehabilitation (CR) is a commonly used treatment for men and women with cardiovascular disease. To date, no single study has conclusively demonstrated a comprehensive benefit of CR. Numerous individual studies, however, have demonstrated beneficial effects such as improved risk-factor profile, slower disease progression, decreased morbidity, and decreased mortality. This paper will review the evidence for the use of CR and discuss the implications and limitations of these studies. The safety, relevance to special populations, challenges, and future directions of CR will also be reviewed.     

Microbial xyloglucanases: a comprehensive review

Xyloglucan is one of the major hemicellulose components of the plant cell wall in majority of dicots and many monocots. Xyloglucanases break down xyloglucan and their role is implicated in the process of plant growth and pathogenesis. They are ubiquitously found in plants as well as in a variety of bacteria, fungi, actinomycetes, etc. In microbes, they are majorly extracellular and are active at a wide range of pH and temperature. They are a diverse group, with varying physicochemical and/or functional properties as well as substrate specificities and thereby the products they give rise to. Their biotechnological potential is immense, with applications in paper and pulp, textile, pharmaceutical and food industries as well as in production of biomaterials and biofuels. This review attempts to give an overview about microbial xyloglucanases and includes important sources, biochemical properties, industrial applications and a brief look at their catalytic mechanisms and GH families.     

Using priced timed automaton to analyse the energy consumption in cloud computing environment

According to the fact that cloud servers have different energy consumption on different running states, as well as the energy waste problem caused by the mismatching between cloud servers and cloud tasks, we carry out researches on the energy optimal method achieved by a priced timed automaton for the cloud computing center in this paper. The priced timed automaton is used to model the running behaviors of the cloud computing system. After introducing the matching matrix of cloud tasks and cloud resources as well as the power matrix of the running states of cloud servers, we design a generation algorithm for the cloud system automaton based on the generation rules and reduction rules given ahead. Then, we propose another algorithm to settle the minimum path energy consumption problem in the cloud system automaton, therefore obtaining an energy optimal solution and an energy optimal value for the cloud system. A case study and repeated experimental analyses manifest that our method is effective and feasible.     

Alvarez waves in pregnancy: a comprehensive review

Alvarez waves are local rhythmic contractions of the myometrium with high frequency and low intensity. They can be detected using internal or external tocography and electrohysterography. Some researchers correlate these small contractions with the initiation of labor, since they have been described as a pattern representing the uterine response to prostaglandin production. Other authors either do not validate a causality relation between Alvarez waves and labor or suggest that they have low predictive value for preterm labor. Alvarez waves’ research has become a multidisciplinary subject with inputs ranging from medical science, biomedical engineering, and related areas. A comprehensive review is herein conducted to summarize the state of the art regarding Alvarez waves and their role in the initiation of labor, namely in preterm birth. The results show that a large number of studies have analyzed and characterized Alvarez waves without necessarily digging into their relationship with labor. Publications were categorized in three groups: (A) reports about morphology and characterization of Alvarez waves; (B) publications reporting a positive causality relation between Alvarez waves and labor; and (C) publications reporting an absence of causality regarding the previous hypothesis. Studies in group B outnumbered those in group C. A critical analysis is presented.     

DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

Cluster Computing - Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that...     

Multicast scheduling algorithms in mobile networks

We consider the problem of multicasting data to mobile users in a cellular mobile network. In the absence of mobility, a single channel can be used to multicast to all mobile users within a cell. However, mobility combined with the effects of fading necessitates a more complex channel allocation policy. In this paper we develop theoretical bounds on the maximum obtainable efficiency and present algorithms that acheive this bound. Our results hold for the case when mobiles travel on a highway, as well as for the more general case where mobiles roam in a two-dimensional region. This revised version was published online in August 2006 with corrections to the Cover Date.     

CloudDMSS: robust Hadoop-based multimedia streaming service architecture for a cloud computing environment

The delivery of scalable, rich multimedia applications and services on the Internet requires sophisticated technologies for transcoding, distributing, and streaming content. Cloud computing provides an infrastructure for such technologies, but specific challenges still remain in the areas of task management, load balancing, and fault tolerance. To address these issues, we propose a cloud-based distributed multimedia streaming service (CloudDMSS), which is designed to run on all major cloud computing services. CloudDMSS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To satisfy the design requirements of our service architecture, we propose four important algorithms: content replication, system recovery for Hadoop distributed multimedia streaming, management for cloud multimedia management, and streaming resource-based connection (SRC) for streaming job distribution. To evaluate the proposed system, we conducted several different performance tests on a local testbed: transcoding, streaming job distribution using SRC, streaming service deployment and robustness to data node and task failures. In addition, we performed three different tests in an actual cloud computing environment, Cloudit 2.0: transcoding, streaming job distribution using SRC, and streaming service deployment.     

The comet assay: a comprehensive review

The comet assay is a sensitive and rapid method for DNA strand break detection in individual cells. Its use has increased significantly in the past few years. This paper is a review of the studies published to date that have made use of the comet assay. The principles of strand break detection using both the alkaline and neutral versions of the technique are discussed, and a basic methodology with currently used variations is presented. Applications in different fields are reviewed and possible future directions of the technique are briefly explored.     

Codling moth granulovirus: a comprehensive review

Codling moth (CM), Cydia pomonella (L), is regarded as the most serious insect pest of apple worldwide. A variety of problems associated with the traditional use of non-selective insecticides for its control include: untoward environmental effects, insecticide resistance, negative impacts on natural enemies, and safety for pesticide applicators and the food supply. Concerns about these consequences have increased the interest in and development of alternative means for CM control that have little or no impact on humans, beneficial organisms and sensitive ecosystems. An effective and selective alternative to chemical insecticides for CM control is the CM granulovirus (CpGV). The virus was first isolated in Mexico and subsequently studied and evaluated in Europe and North America. A variety of research including pathology, pathogenesis and histopathology of the virus, determination of virulence, development of production methods, field use, factors that influence efficacy, commercial development, formulation, and CM resistance to the virus has been conducted. Commercial products of CpGV are now produced in Europe and North America and used by orchardists worldwide. In this paper we present a comprehensive review of the CpGV literature and the role of the virus in integrated pest management.