On-demand Resource Provisioning Research Articles

Latency and resource consumption analysis for serverless edge analytics

The serverless computing model, implemented by Function as a Service (FaaS) platforms, can offer several advantages for the deployment of data analytics solutions in IoT environments, such as agile and on-demand resource provisioning, automatic scaling, high elasticity, infrastructure management abstraction, and a fine-grained cost model. However, in the case of applications with strict latency requirements, the cold start problem in FaaS platforms can represent an important drawback. The most common techniques to alleviate this problem, mainly based on instance pre-warming and instance reusing mechanisms, are usually not well adapted to different application profiles and, in general, can entail an extra expense of resources. In this work, we analyze the effect of instance pre-warming and instance reusing on both application latency (response time) and resource consumption, for a typical data analytics use case (a machine learning application for image classification) with different input data patterns. Furthermore, we propose extending the classical centralized cloud-based serverless FaaS platform to a two-tier distributed edge-cloud platform to bring the platform closer to the data source and reduce network latencies.

Classification of Request-Based Mobility Load Balancing in Fog Computing

Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request. The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests. LBA does the operation based on these classifications. The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs. The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance, which was attained by (90.8%). Using LBA, several metrics may be examined, including Response Time (RT), delay (d), Energy Consumption (EC), and latency. Through the on-demand provisioning of necessary resources to IoT users, our suggested LBA assures effective resource usage.

A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisioning. In the cloud, a variety of metaheuristic algorithms are helpful for process allocation. Large-scale datacenters utilise a lot of electricity, which has an influence on the environment and the economy. Using a Micro-Genetic Algorithm, a stable combined process workload allocation method with Cat Swarm Optimization (MG-CSO) is introduced by addressing pre-convergence problems and optimal resource management. For optimal computing efficiency, the resources are dynamically consolidated and clustered. Additionally, the MG-CSO Algorithm produces positive outcomes when compared to other well-known algorithms, such as the Genetic Algorithm (GA) and the Bat Algorithm (BA).This research's focus is to reduce cost, time, Service Level Agreement(SLA), energy usage, and afford good Quality of Service(QoS). Our final scientific results were achieved with 91%.

Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.

Estimating the Resource Demand in Power-Aware Clusters by Regressing a Linearly Dependent Relation

Large-scale clusters are often built with over-provisioned service resources, so as to satisfy the huge demand raised by enormous users in cloud environments. By estimating the resource demand of workloads, an on-demand resource provisioning method can be realized in these clusters, thus improving the energy efficiency. However, to guarantee Quality of Service (QoS), the resource demand of workload should be accurately estimated so as to provide suitable resources. Many statistical approaches estimate actual resource demand based on some workload features. But the relations between actual resource demand and workload features are generally obscure, and it's a big challenge to gain an accurate estimation under an obscure relation. In this paper, by considering a cluster as a queueing system, we construct a linearly dependent relation between resource demand and multiple feature combinations. The linearly dependent relation is inconstant due to its variable coefficients. Then, to ascertain specific relations which match actual situations, we design a Basic Linear regression (BL) algorithm. BL can obtain the optimal values for these coefficients, thus determining the inconstant relation to specific ones. Finally, we propose a Constructed Linear regression (CL) approach to estimate actual resource demands. CL forms a two-layer neural network by using several processes of BL as the neurons. To evaluate CL, we realize an On-Demand Resource Provisioning (ODRP) method in a typical power-aware cluster. Several evaluation metrics are proposed for conducting extensive experiments. The experimental results show that CL is effective to make accurate estimations.

Quantum-inspired binary chaotic salp swarm algorithm (QBCSSA)-based dynamic task scheduling for multiprocessor cloud computing systems

Scheduling in multiprocessor computing systems is experiencing prolific challenges in datacenters due to the alarmingly growing need for dynamic on-demand resource provisioning. This problem has become a challenge for the cloud broker due to the involvement of the numerous conflicting performance metrics such as minimization of makespan, energy consumption and load balancing, and maximization of resource utilization. These challenges are to be alleviated by the practical assignments of tasks onto VMs in a way to disperse loads among VMs with high utilization of resources uniformly. In this research, authors propose a quantum-inspired binary chaotic salp swarm algorithm for scheduling the tasks in multiprocessor computing systems by considering the above conflicting objectives. The principles of quantum computing are amalgamated with the BCSSA with the aim to intensify the exploration capability. Besides, a load balancing approach is incorporated with the algorithm for uniformly dispersing the loads. This algorithm considers a multi-objective fitness function to evaluate the fitness of the particles in the problem space. The performance of the proposed algorithm is validated and analyzed through extensive experimental results using the synthetic as well as the benchmark datasets in both homogeneous and heterogeneous environments. It is evident that the proposed work shows considerable improvements over Bird Swarm Optimization, Modified Particle Swarm Optimization, JAYA, standard SSA, and GAYA (a hybrid approach) with the considered objectives.

A Utility Game Driven QoS Optimization for Cloud Services

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.

A dynamic load scheduling in IaaS cloud using binary JAYA algorithm

Datacenters receive dynamic workloads with disparate specifications to be scheduled on virtual machines (VMs). These unpredictable, alarmingly growing workloads with varying resource specifications may bring down the servers of datacenters into an imbalanced state. Thus, resulting in low resource utilization and high energy consumption among the servers. To cater to the need of fluctuating on-demand resource provisioning, it is essential to scale up the ability and capacityof existing infrastructure through virtualization. Moreover, due to the involvement of conflicting scheduling constraints, load scheduling in cloud computing fall under NP-hard problem. An effective scheduling mechanism in amalgamation with a load balancing strategy based on binary JAYA is implemented to alleviate the challenges as mentioned above. This technique not only improves resource utilization but also brings down the degree of energy consumption and makespan while keeping the whole system balanced. At first, it focuses on evoking a load balancing procedure to uniformly disperse the loads among VMs based on the compatibility between the tasks and VMs and secondly, JAYA algorithm is executed to find the best possible mapping of tasks onto VMs. In order to appraise the efficacy of the proposed algorithm, the scheduling of independent and non-preemptive tasks is simulated in CloudSim using a benchmark parallel workload by NASA-iPSC. Experiments are conducted in both homogenous and heterogeneous environments. The proposed algorithm is statistically validated using the Friedman test, followed by a Holm’s test. The proposed approach is compared over other algorithms such as Round Robin (RR), binary Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation results show a notable amelioration over other mentioned algorithms by an increase of resource utilization with 18.47% (GA), 12.65% (BPSO) and 4.18% (GA), 2.51% (BPSO) and a reduction of makespan by 6.47% (GA), 4.35% (BPSO) and 4.17% (GA), 2.20% (BPSO) with an increasing number of tasks and VMs in two different test cases respectively.

GMTA: A Geo-Aware Multi-Agent Task Allocation Approach for Scientific Workflows in Container-Based Cloud

Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.

Performability evaluation of transport modes for cloud-based inbound logistics: a study based on coffee industry

The adoption of cloud computing in supply chains has been growing over the years due to the benefits concerning on-demand resource provisioning and cost reduction. In the context of inbound logistics, cloud computing may also provide mechanisms for information sharing that allows better decisions, for instance, for selecting the best transport mode for a given set of goods and suppliers. However, cloud computing is still new to organisations in supply chain and evaluations should be carried out to assess the tangible gains in this paradigm. This paper presents an approach based on stochastic Petri nets (SPN) for performability assessment of transport modes in cloud-based inbound logistics taking into account sustainability issues and the coffee industry as case study.

On-demand resource provision based on load estimation and service expenditure in edge cloud environment

The trend of the Internet of Everything is deepening, and the amount of data that needs to be processed in the network is growing. Using the edge cloud technology can process data at the edge of the network, lowering the burden on the data center. When the load of the edge cloud is large, it is necessary to apply for more resources to the cloud service provider, and the resource billing granularity affects the cost. When the load is small, releasing the idle node resources to the cloud service provider can lower the service expenditure. To this end, an on-demand resource provision model based on service expenditure is proposed. The demand for resources needs to be estimated in advance. To this end, a load estimation model based on ARIMA model and BP neural network is proposed. The model can estimate the load according to historical data and reduce the estimation error. Before releasing the node resources, the user data on the node need to be migrated to other working nodes to ensure that the user data will not be lost. In this paper, when selecting the migration target, the three metrics of load balancing, migration time consumption and migration cost of the cluster are considered. A data migration model based on load balancing is proposed. Through the comparison of experimental results, the proposed methods can effectively reduce service expenditure and make the cluster in a state of load balancing.

Parasite cloud service providers: on-demand prices on top of spot prices

On-demand resource provisioning and elasticity are two of the main characteristics of the cloud computing paradigm. As a result, the load on a cloud service provider (CSP) is not fixed and almost always a number of its physical resources are not used, called spare resources. As the CSPs typically don't want to be overprovisioned at any time, they procure physical resources in accordance to a pessimistic forecast of their loads and this leads to a large amount of spare resources most of the time. Some CSPs rent their spare resources with a lower price called the spot price, which varies over time with respect to the market or the internal state of the CSP. In this paper, we assume the spot price to be a function of the CSP's load. We introduce the concept of a parasite CSP, which rents spare resources from several CSPs simultaneously with spot prices and rents them to its customers with an on-demand price lower than the host CSPs' on-demand prices. We propose the overall architecture and interaction model of the parasite CSP. Mathematical analysis has been made to calculate the amount of spare resources of the host CSPs, the amount of resources that the parasite CSP can rent (its virtual capacity) as well as the probability of SLA violations. We evaluate our analysis over pricing data gathered from Amazon EC2 services. The results show that if the parasite CSP relies on several host CSPs, its virtual capacity can be considerable and the expected penalty due to SLA violation is acceptably low.

CPU–RAM-based energy-efficient resource allocation in clouds

Cloud computing provides online services and solutions for dynamic and on-demand resource provisioning. These resources consume high energy leading to higher operational expenditures and carbon footprints in data centers. There are several research works performed on energy efficiency of data centers, but mostly focus on energy consumption of a single factor, i.e., CPU, leaving the RAM neglected. Recently, the researchers have focused on the impact of RAM’s energy consumption on the data centers. Studies show that RAM consumes 25% of a server’s overall energy. In this paper, we propose two sets of schemes that consider the server capacity for virtual machine consolidation to reduce the overall energy cost. The proposed techniques are implemented in CloudSim, and the results are compared with state-of-the-art solutions. Our proposed techniques reduce energy consumption and maintain a service level agreement to satisfy the customer requirements with a minimum cost.

SLA-RALBA: cost-efficient and resource-aware load balancing algorithm for cloud computing

Recently, Service-level agreement (SLA) is deemed to be an integral aspect for on-demand provisioning of scalable resources on Cloud. SLA defines important constraints for instance guaranteed quality of service (QoS), pricing, fault-tolerant availability, security, and period of service. Currently, there is a dire need of SLA-based scheduling that improves resources utilization on Cloud. Scheduling with reduced execution time and cost may adversely affect resource utilization. To overcome this issue, we present a cost-efficient SLA-based load balancing scheduler, namely SLA-RALBA, for heterogeneous Cloud infrastructures. The proposed technique supports three levels of SLA opted by the Cloud users. The proposed novel technique incorporates the execution cost for the successful execution of users’ services to elevate the resource utilization on Cloud. The SLA-RALBA is simulated for performance analysis using the benchmark GoCJ and HCSP datasets. The performance results of the SLA-RALBA are compared with the existing schedulers, namely Execution-MCT, Profit-MCT, SLA-MCT, Execution-Min-Min, Profit-Min-Min, and SLA-Min-Min in terms of average resource utilization, execution time, and cost of the Cloud services. The obtained results reveal that SLA-RALBA provides an even trade-off between execution time and cost of the services by guaranteeing a drastic improvement in resource utilization on Cloud than existing algorithms.

Securing and Self recovery of Virtual Machines in cloud with an Autonomic Approach using Snapshots

The advent of cloud computing has been so enormous that, cloud has been seen as the most enduring technology of today’s technology scenario. It is known to be the service oriented architecture, granted to the end-users as Infrastructure as a service, platform as a service, software as a service, business process as a service, security as a service and so on. Most of the underlying models used in cloud depend on the existing technologies for support; in particular, virtualization component imparts on-demand resource provisioning and multitenancy. Security of the resources provisioned in the form of images or instances play a major part in the security aspect of cloud. Users access software and hardware in cloud environment, through several virtual machines (VM) instances. These VM instances are isolated from each other, providing hardware utilization, easier management and migration compared to its physical counterpart. Services are shared among a group of service consumers, partners and vendors. The resources of a single physical machine are shared among the various created VM’s giving rise to new architectures and computing paradigms. Thus, security problems pose a major challenge in the virtual machines in the cloud environment. This paper presents an autonomic prediction model for auto recovery of the attacked VM instances in cloud. This approach would enable continuous progression of any process in the schedule without any job disruption. A method to propose the possible solution for auto recovery and self healing in the VM’s by an intelligent hypervisor is summarized.

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds (VCs) formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy onboard workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for VCs that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over VCs is formulated as a nonlinear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize the job completion time and data exchange cost. The problem is addressed for two scenarios: 1) low-traffic and 2) rush-hour scenarios. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree-based subgraph isomorphism extraction. The evaluation of the performance of both optimal and proposed randomized algorithms with two greedy-based baseline methods is carried out through extensive simulations.

Migrating parallel applications to the cloud: assessing cloud readiness based on parallel design decisions

Parallel applications are the computational backbone of major industry trends and grand challenges in science. Whereas these applications are typically constructed for dedicated High Performance Computing clusters and supercomputers, the cloud emerges as attractive execution environment, which provides on-demand resource provisioning and a pay-per-use model. However, cloud environments require specific application properties that may restrict parallel application design. As a result, design trade-offs are required to simultaneously maximize parallel performance and benefit from cloud-specific characteristics. In this paper, we present a novel approach to assess the cloud readiness of parallel applications based on the design decisions made. By discovering and understanding the implications of these parallel design decisions on an application’s cloud readiness, our approach supports the migration of parallel applications to the cloud. We introduce an assessment procedure, its underlying meta model, and a corresponding instantiation to structure this multi-dimensional design space. For evaluation purposes, we present an extensive case study comprising three parallel applications and discuss their cloud readiness based on our approach.

A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment

Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.

From Cloud Computing to Fog Computing

This article describes how in recent years, Cloud Computing has emerged as a fundamental computing paradigm that has significantly changed the approach of enterprises as well as end users towards implementation of Internet technology. The key characteristics such as on-demand resource provision, scalability, rapid elasticity, higher flexibility, and significant cost savings have influenced enterprises of all sizes in the wide and successful adoption of Cloud Computing. Despite numerous advantages, Cloud Computing has its fair share of downsides as well. One of those major concerns is latency issues which has relevance to the Internet of Things (IoT). A new computing paradigm has been proposed by Cisco in early 2014 and termed 'Fog Computing'. Fog Computing otherwise known as Edge Computing is the integration of Cloud Computing and IoT. Being located in close proximity to the IoT devices, the Fog assists with latency requirements of IoT related applications. It also meets the data processing needs of IoT devices which are resource constrained by bringing computation, communication, control and storage closer to the end users. Clouds continue to offer support for data analytics. One can think of the IoT-Fog-Cloud as being part of a continuum. This article surveys the current literature on Fog Computing and provides a discussion on the background, details and architecture of Fog Computing, as well as the application areas of Fog Computing. The article concludes with some recommendations in the areas of future research.