Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

Cloud computing and big data: Technologies and applications

Scientific workflows benefit from the cloud computing paradigm, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis. Minimizing resources costs to meet user’s budget is very important in a cloud environment. Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing. However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and metaheuristic as an optimization method. Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature. Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed. In this approach, first, the datasets and workflow tasks are modeled as a conditional probability matrix (CPM). Second, several data transformation and hierarchical clustering are applied to the CPM structure to determine the minimum number of virtual machines needed for the workflow execution. In this approach, the hierarchical clustering is done with respect to the budget imposed by the user. After data transformation and hierarchical clustering, the amount of data transmitted between clusters can be reduced, which can improve cost and makespan of the workflow by optimizing the use of virtual resources and network bandwidth. The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches. The results demonstrate that our approach reduces resources cost with respect to the user budgets.

With the widespread application of cloud computing technology, the energy consumption problem of data centers is becoming increasingly severe. This research is dedicated to presenting a highly efficient and smart solution for optimizing energy usage in cloud computing systems, especially since effectively managing energy consumption through resource allocation and task scheduling has now become a critical challenge in the field. In view of this, the study first constructs a resource allocation model based on redundant resource recovery to dynamically identify and recover redundant resources. Second, a task scheduling optimization method based on dual deep reinforcement learning is established, which utilizes directed acyclic graphs to model task dependencies and optimizes task execution order and resource allocation strategies through reinforcement learning agents. In the testing of the resource allocation optimization model, when the number of tasks is 500, the redundant resource recovery rate is 87.0%, and the computational complexity is 16.7 s. The response time in the regional resource environment is 2.28 s, and the load adaptability is 95.8%. In the task scheduling method test, the proposed method has a memory occupancy of 85 MB and a scheduling delay of 0.13 s in a high-bandwidth environment of 300 Mbps. The results show that the proposed method outperforms traditional scheduling strategies in terms of resource utilization and energy consumption, and contributes to achieving the goal of green cloud computing. The research aims to provide an efficient and intelligent solution for energy optimization in cloud computing systems.

Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget $$ (erb) $$ for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan.

In this period, cloud computing is considered a very ultra-modern and eminent technology on the web. It provides on-demand computing services, such as Google, Microsoft, etc. by this world-wise category. Cloud computing is providing both provisioning and de-provisioning on-demand network services as well as assisting any organization to cut the capital costs of software and hardware. Cloud service providers require handling a huge demand for scaling up the size of the cloud. But now it faces grave challenges in its glorious future. These challenges create a problem for exploring its perfect realization. Resource allocation is one of the vital issues in that case. It is a procedure to distribute total workloads among individual systems of distributed web systems to get better the proper usage of web resources and job execution time. Resource allocation should be done properly as a breakdown in any node that can lead to the unavailability of data. Resource allocation mechanisms can be divided into different categories, such as centralized or distributed, dynamic or static, etc. There are already various research works that have been done on resource allocation using various formulating techniques to decrease the execution time and average waiting time for giving a better quality of services in cloud computing.

Workflow technology has been adopted in scientific domains to orchestrate and automate scientific processes in order to facilitate experimentation. Such scientific workflows often involve large data sets and intensive computation that necessitate the use of the Grid, which offers supercomputing power through shared distributed resources. To execute a scientific workflow in the Grid, tasks within the workflow that represent steps in the scientific process are assigned to Grid resources for execution. To ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources. Although many Grid workflow scheduling techniques exist, they are mainly designed for the execution of a single workflow. This is not the case with parameter sweep workflows which focus on parametric study and parameter optimisation. A parameter sweep workflow is executed numerous times with different input parameters in order to determine the effect of each parameter combination on the experiment. While executing multiple instances of a parameter sweep workflow in parallel can reduce the time required for the overall execution, this execution introduces new challenges to Grid workflow scheduling. Not only is a scheduling algorithm that is able to manage multiple workflow instances required, but this algorithm also needs the ability to schedule tasks across multiple workflow instances judiciously, as tasks may require the same set of Grid resources. Without appropriate resource allocation, resource competition problem could arise. In the thesis, we propose a new Grid workflow scheduling technique for parameter sweep workflow called the Besom scheduling algorithm. The scheduling decision of our algorithm is based on the resource dependencies of tasks in the workflow, as well as conventional Grid resource-performance metrics. In addition, the proposed technique is extended to handle loop structures in scientific workflows without using existing loop-unrolling techniques. We evaluate the Besom algorithm under a variety of conditions. A comparison between the simulation results of the Besom algorithm and of the three existing Grid workflow scheduling algorithms shows that the Besom algorithm is able to perform better than the existing algorithms for workflows that have complex structures and that involve overlapping resource dependencies of tasks. The Besom scheduling algorithm advances the ability to schedule parallel execution of parameter sweep workflows in the Grid. The outcomes of this thesis justify continuing research in this area to increase our understanding of scheduling multiple Grid workflow instances and to provide support to those involved in parametric study and scientific workflow management.

In distributed computing an escalating trend is Cloud computing which mainly focuses on directly delivering computational power to the end-user regardless of when and where. Delivering of services to the users is there such as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Cloud computing is having an IaaS model which is a desired platform for workflow execution of scientific applications for example bioinformatics, astronomy, geophysics etc. Hence, several researches are recommencing scientific workflows execution in Infrastructure as a Service cloud computing environment. An algorithm ‘Rank-Based Task Scheduling Algorithm’ (RBTSA) which is proposed by Shilpa Rana et al. have future research scope that is the performance of RBTSA can be tested by calculating the rank of virtual machines by considering different characteristic of bandwidth, physical memory etc. of virtual machines which is considered in this paper. Herein paper an algorithm termed as ‘Multi Criteria Rank-Based Task Scheduling Algorithm’ (MCRBTSA) is planned for scientific workflows tasks scheduling to increase resources utilization in IaaS cloud computing environment. The experimental simulation of research work which is mentioned above is done on ‘WorkflowSim-1.0’ and scientific workflow execution results of proposed MCRBTSA Algorithm are contrasted with the existing ‘Rank-Based Task Scheduling Algorithm’ (RBTSA). Experimentally it shows that MCRBTSA which is proposed have utilization of resources better while scheduling workflow tasks as compared to existing RBTSA and workflow makespan reduction is also there.

Scheduling is a process that manages the workflow tasks during execution on different resources. Virtual infrastructure is a dynamic mapping of system resources to applications in order to maximize its utilization. In today's technological world, cloud has taken a long stride on the success towards maximum throughput as well as highest qualitative services to its consumers. Yet, approaches for maximizing the utilization of cloud resources are at peak demand. Each cloud service provider focuses on maximum utilization with minimum consumption of cloud resources, although managing and providing computational resources to maximum number of users and to execute such huge applications is a challenging one. In this paper, a scheduling algorithm with name TBW (Tabu Bayesian Whale Optimization) has been proposed. Basically, the algorithm is used to target the improvement in scheduling of scientific workflows. The complete framework has firstly used a ranking algorithm named distributed HEFT ranking and then applied TBW algorithm on ranked tasks of input workflows. The work has been executed for five scientific workflows LIGO, MONTAGE, Epigenomics, SIPHT and Cybershake. TBW is using tabu method on workflow tasks for fast local search in cloud system, and Bayesian Optimization is used to find out best possible combinations of resources where tasks are mapped and then whale optimization maps the tasks on the resources in a smart way. In the whole process, total execution time and cost parameters are minimized under deadline constraints.KeywordsCloud VMsTask mappingTabu searchBayesian optimizationWhale optimization

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

There are many tasks in cloud computing that should be executed by available resources to acquire high performance, reduce task completion time, increase utilisation of resources etc. Since task scheduling problem in cloud computing is an NP-hard problem, designing an efficient scheduling strategy to achieve the intentions listed above is challenging. Task scheduling is the process of allocating tasks to available resources such that performance metrics are improved. This paper proposes a dynamic scheduling algorithm that uses hill climbing algorithm. It tries to minimise completion time of tasks while maximising throughput and utilisation of resources. This algorithm allocates independent tasks to available resources to achieve load balance. The simulation results show that the algorithm can achieve load balance and reduces completion time of tasks.

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

With the phenomenal growth of Internet, the technologies associated with the Internet like cloud computing and fog computing also grown massively. At the same time, the issues in this technology also need to be addressed while allocating and deploying resources. Regardless of rapid growth of cloud computing, plenty of issues are there which are necessary to be solved due to ingrained property of cloud computing such as location awareness, unreliable delay and lack of mobility support. Fog computing means bringing the centralized computing cloud resources to the edge of the network. Fog computing copes with the problems in cloud computing by giving flexible resources and services to end users, even cloud computing is more approximately imparting resource dispersed in the core network. The differences between compute-intensive applications and resource-limited gadgets result in restricting the quality of the system. In fog technology, the logical inconsistency should be tackled by task scheduling. Because of the mushrooming of Internet of things, effective means of allocating resource for the clients whenever it is requested is very challenging task in fog technology. This paper gives a survey of different mechanisms proposed for scheduling the tasks and allocating the resources in fog technology. The various techniques proposed by different authors are reviewed in detail. Finally, an analysis is made by comparing the disadvantages of each technique and suggestions are given for betterment in task scheduling and resource allocation in fog effectively.

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

BackgroundVarious population segmentation tools have been developed to inform the design of interventions that improve population health. However, there has been little consensus on the core indicators and purposes of population segmentation. The existing frameworks were further limited by their applicability in different practice settings involving stakeholders at all levels. The aim of this study was to generate a comprehensive set of indicators and purposes of population segmentation based on the experience and perspectives of key stakeholders involved in population health.MethodsWe conducted in-depth semi-structured interviews using purposive sampling with key stakeholders (e.g. government officials, healthcare professionals, social service providers, researchers) involved in population health at three distinct levels (micro, meso, macro) in Singapore. The interviews were audio-recorded and transcribed verbatim. Thematic content analysis was undertaken using NVivo 12.ResultsA total of 25 interviews were conducted. Eight core indicators (demographic characteristics, economic characteristics, behavioural characteristics, disease state, functional status, organisation of care, psychosocial factors and service needs of patients) and 21 sub-indicators were identified. Age and financial status were commonly stated as important indicators that could potentially be used for population segmentation across three levels of participants. Six intended purposes for population segmentation included improving health outcomes, planning for resource allocation, optimising healthcare utilisation, enhancing psychosocial and behavioural outcomes, strengthening preventive efforts and driving policy changes. There was consensus that planning for resource allocation and improving health outcomes were considered two of the most important purposes for population segmentation.ConclusionsOur findings shed light on the need for a more person-centric population segmentation framework that incorporates upstream and holistic indicators to be able to measure population health outcomes and to plan for appropriate resource allocation. Core elements of the framework may apply to other healthcare settings and systems responsible for improving population health.Trial registrationThe study was approved by the SingHealth Institutional Review Board (CIRB Reference number: 2017/2597).

Task scheduling in cloud computing environments is crucial for optimizing resource allocation and enhancing system efficiency.In this paper, we present a systematic analysis environment for evaluating various task scheduling algorithms.We focus on three prominent algorithms: Ant Colony Optimization (ACO), Round Robin, and Genetic Algorithm (GA).Each algorithm offers unique strengths and trade-offs, making them suitable for different cloud computing scenarios.Firstly, we delve into the principles of Ant Colony Optimization, leveraging the collective intelligence of artificial ants to find optimal task assignments in a distributed manner.Secondly, Round Robin, a simple yet effective algorithm, cyclically allocates tasks among available resources, ensuring fair utilization.Lastly, Genetic Algorithm, inspired by natural selection processes, evolves task scheduling solutions over successive generations, adapting to dynamic workload conditions.