Showing papers on "Service-level agreement published in 2021"
TL;DR: Various reputation-based trust management systems are reviewed, including trust management in cloud computing, peer-to-peer system, and Adhoc system, which aims to build trust between cloud services.
Abstract: The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system.
88 citations
TL;DR: This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance.
54 citations
TL;DR: In this paper, the authors introduce the key innovations of the 5G Growth service platform to empower vertical industries with an AI-driven automated 5G end-to-end slicing solution that allows industries to achieve their service requirements.
Abstract: This article introduces the key innovations of the 5Growth service platform to empower vertical industries with an AI-driven automated 5G end-to-end slicing solution that allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, transportation, and energy), identify the key 5G requirements to enable them, and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control; (ii) an AI-driven closed-loop for automated service management with service level agreement assurance; and (iii) multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.
51 citations
TL;DR: In this paper, the authors proposed an algorithm to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation.
Abstract: Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature’s findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.
47 citations
TL;DR: An efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly, and facilitates power saving, by minimizing the number of active servers, VM migrations and maximizing the resource utilization.
Abstract: The elasticity of cloud resources allow cloud clients to expand and shrink their demand of resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance and excessive power consumption. To address these issues and to improve the performance of datacenter, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations and maximizing the resource utilization. An online resource prediction system, is developed and installed at each VM, to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within datacenter. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster Dataset, Planet Lab and Bitsbrain traces. The comparison of proposed framework with the state-of-art approaches, reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3% over the Best-Fit approach.
41 citations
09 Jun 2021
TL;DR: ResTune as discussed by the authors leverages the tuning experience from the history tasks and transfers the accumulated knowledge to accelerate the tuning process of the new tasks, which significantly reduces the tuning time by a meta-learning based approach.
Abstract: Modern database management systems (DBMS) contain tens to hundreds of critical performance tuning knobs that determine the system runtime behaviors. To reduce the total cost of ownership, cloud database providers put in drastic effort to automatically optimize the resource utilization by tuning these knobs. There are two challenges. First, the tuning system should always abide by the service level agreement (SLA) while optimizing the resource utilization, which imposes strict constrains on the tuning process. Second, the tuning time should be reasonably acceptable since time-consuming tuning is not practical for production and online troubleshooting. In this paper, we design ResTune to automatically optimize the resource utilization without violating SLA constraints on the throughput and latency requirements. ResTune leverages the tuning experience from the history tasks and transfers the accumulated knowledge to accelerate the tuning process of the new tasks. The prior knowledge is represented from historical tuning tasks through an ensemble model. The model learns the similarity between the historical workloads and the target, which significantly reduces the tuning time by a meta-learning based approach. ResTune can efficiently handle different workloads and various hardware environments. We perform evaluations using benchmarks and real world workloads on different types of resources. The results show that, compared with the manually tuned configurations, ResTune reduces 65%, 87%, 39% of CPU utilization, I/O and memory on average, respectively. Compared with the state-of-the-art methods, ResTune finds better configurations with up to ~18x speedups.
39 citations
TL;DR: A dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment is proposed.
Abstract: In recent years, cloud data centers are rapidly growing with a large number of finite heterogeneous resources to meet the ever-growing user demands with respect to the SLA (service level agreement). However, the potential growth in the number of large-scale data centers leads to large amounts of energy consumption, which is constantly a major challenge. In addition to this challenge, intensive number of VM (virtual machine) migrations can decrease the performance of cloud data centers. Thus, how to minimize energy consumption while satisfying SLA and minimizing the number of VM migrations becomes an important challenge classified as NP-hard optimization problem in data centers. Most VM scheduling schemes have been proposed for this problem, such as dynamic VM consolidation. However, most of them failed in low time complexity and optimal solution. Hence, this paper proposes a dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment. Specifically, the proposed approach consists of four methods which include: BPSO meta-heuristic-based load balancing to impact on energy consumption and number of host shutdowns, overloading host detection and VM placement-based Pearson correlation coefficient to impact on SLA, and VM selection based on imbalance degree to impact on number of VM migration. Moreover, Pearson correlation coefficient and imbalance degree correlate CPU, RAM and bandwidth, respectively, in each host and each VM. Through extensive analysis and simulation experiments using real PlanetLab and random workloads, the performance results demonstrate that the proposed approach exhibits excellent results for the NP-problem.
35 citations
TL;DR: In this article, a hybrid recurrent neural network (RNN) based prediction model named BHyPreC is proposed to predict future CPU usage workload of cloud's VM, which enhances the nonlinear data analysis capability of Bi-LSTM, LSTM and GRU separately and demonstrates better accuracy compared to other statistical models.
Abstract: With the advancement of cloud computing technologies, there is an ever-increasing demand for the maximum utilization of cloud resources. It increases the computing power consumption of the cloud’s systems. Consolidation of cloud’s Virtual Machines (VMs) provides a pragmatic approach to reduce the energy consumption of cloud Data Centers (DC). Effective VM consolidation and VM migration without breaching Service Level Agreement (SLA) can be attained by taking proactive decisions based on cloud’s future workload prediction. Effective task scheduling, another major issue of cloud computing also relies on accurate forecasting of resource usage. Cloud workload traces exhibit both periodic and non-periodic patterns with the sudden peak of load. As a result, it is very challenging for the prediction models to precisely forecast future workload. This prompted us to propose a hybrid Recurrent Neural Network (RNN) based prediction model named BHyPreC. BHyPreC architecture includes Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the stacked Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Here, BHyPreC is used to predict future CPU usage workload of cloud’s VM. Our proposed model enhances the non-linear data analysis capability of Bi-LSTM, LSTM, and GRU models separately and demonstrates better accuracy compared to other statistical models. The effect of variation of historical window size and training-testing data size on these models is observed. The experimental result shows that our model gives higher accuracy and performs better in comparison to Autoregressive Integrated Moving Average (ARIMA), LSTM, GRU, and Bi-LSTM model for both short-term ahead and long-term ahead prediction.
35 citations
TL;DR: In this paper, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals is proposed.
Abstract: Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one application may contain hundreds of microservices, which constitute an enormous search space that is unfeasible to explore exhaustively. Thus, we propose RAMBO, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals. Experiments conducted on a real microservice workload demonstrate that RAMBO can correctly characterize each microservice and efficiently discover Pareto-optimal solutions. We envision that the proposed methodology and results will benefit future resource planning, cluster orchestration, and job scheduling.
32 citations
TL;DR: A new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement to reduce the power consumption in cloud data centers by condensing the number of active physical machines.
Abstract: Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.
32 citations
TL;DR: This article proposes a profit model which involves both access delay and energy consumption and devise a particle swarm optimization-based algorithm to optimize the profit, which stands out in terms of achieving the highest profit.
Abstract: In 5G network, mobile edge computing plays a key role in providing low access delay services. The placement of edge servers not only determines the quality of services on the user side, but also affects the profit of running a mobile edge computing system. In this paper, we study how to properly place edge servers so as to guarantee the access delay and maximize the profit of edge providers. We first propose a profit model which involves both access delay and energy consumption. In this model, we take 5G User Plane Function (UPF) into consideration to calculate access delay for the first time. Then we devise a particle swarm optimization based algorithm to optimize the profit. In the algorithm, we introduce a weight value q to guarantee the access delay and assign base stations properly. Moreover, a service level agreement is adopted to balance the trade-off between access delay and energy consumption. We take advantage of our 5G network emulator called mini5Gedge and dataset from Shanghai Telecom to conduct massive experiments. The results show that our algorithm stands out in terms of achieving the highest profit.
TL;DR: The number of service offerings in cloud manufacturing continues to grow, so that service level agreement (SLA) within cloud manufacturing is increasingly used to ensure the coordination of the cro... as discussed by the authors.
Abstract: The number of service offerings in cloud manufacturing continues to grow, so that service level agreement (SLA) within cloud manufacturing is increasingly used to ensure the coordination of the cro...
TL;DR: In this paper, the authors proposed an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing, which considers the pheromones' repulsion instead of attraction.
Abstract: Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones' repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.
TL;DR: This work jointly investigates the load distribution and placement of IoT applications to minimize Service Level Agreement violations and proposes a multi-objective genetic algorithm with the initial population based on random and heuristic solutions to obtain near-optimal solutions.
TL;DR: A new virtual machine (VM) consolidation technique called Nature-inspired Meta-heuristic Threshold based firefly optimized lottery scheduling (NMT-FOLS) Technique is proposed, which gets better task scheduling performance to balance normal, timely and bursty workloads in CS with lesser time.
Abstract: Scheduling is a considerable problem in cloud to increase the quality of service provisioning with higher resource efficiency. The conventional task scheduling algorithms designed for balancing load in a cloud environment. But, minimizing the Service level agreement (SLA) violation, resource wastage and the energy consumption during the task scheduling process was not solved effectively. In order to resolve these limitations, a new virtual machine (VM) consolidation technique called Nature-inspired Meta-heuristic Threshold based firefly optimized lottery scheduling (NMT-FOLS) Technique is proposed. Initially, user requests are transmitted to the cloud server (CS). Next, NMT-FOLS Technique utilizes Adaptive Regressive Holt–Winters Workload Predictor to discover the workload state as normal or timely or bursty. Using the workload predictor result, NMT-FOLS Technique exploits task scheduler to allocate user requested tasks to optimal VMs. NMT-FOLS Technique applies multi-objective firefly optimization based task scheduling algorithm in normal workload state and multi-objective firefly optimized lottery scheduling algorithm in timely and bursty workload situations. At last, the selected scheduling algorithm in NMT-FOLS Technique assigns the user requested task to best VMs in CS to perform the demanded services. Hence, NMT-FOLS Technique gets better task scheduling performance to balance normal, timely and bursty workloads in CS with lesser time. NMT-FOLS Technique decreases the SLA violation in cloud through scheduling of user tasks to optimal VM. NMT-FOLS performs an experimental process using metrics such as SLA violation, task scheduling efficiency (TSE), makespan, energy utilization and memory usage with number of user-requested tasks over the considered Amazon dataset. From the experimental result, the NMT-FOLS technique improves scheduling efficiency up to 94.6% and reduces the SLA violations and energy utilization from different test cases on an average to 78%, and 63% compared to state-of-the-art works.
TL;DR: This paper proposes scalable and automatic admission control and profit optimization resource scheduling algorithms, which effectively admit data analytics requests, dynamically provision resources, and maximize profit for AaaS providers, while satisfying QoS requirements of queries with Service Level Agreement (SLA) guarantees.
Abstract: The value that can be extracted from big data greatly motivates users to explore data analytics technologies for better decision making and problem solving in various application domains. Analytical solutions can be expensive due to the demand for large-scale and high-performance computing resources. To provision online big data Analytics-as-a-Service (AaaS) to users in various domains, a general purpose AaaS platform is required to deliver on-demand services at low cost and in an easy to use manner. Our research focuses on proposing efficient and automatic admission control and resource scheduling algorithms for AaaS platforms in cloud environments. In this paper, we propose scalable and automatic admission control and profit optimization resource scheduling algorithms, which effectively admit data analytics requests, dynamically provision resources, and maximize profit for AaaS providers, while satisfying QoS requirements of queries with Service Level Agreement (SLA) guarantees. Moreover, the proposed algorithms enable users to trade-off accuracy for faster response times and less resource costs for query processing on large datasets. We evaluate the algorithm performance by adopting a data splitting method to process smaller data samples as representatives of the original big datasets. We conduct extensive experiments to evaluate the proposed admission control and profit optimization scheduling algorithms. Experimental evaluation shows the algorithms perform significantly better compared to the state-of-the-art algorithms in enhancing profits, reducing resource costs, increasing query admission rates, and decreasing query response times.
TL;DR: The results show that the two-stage policy outperforms other capacity allocation policies when a disruption is anticipated and that partial disruption is more manageable than full disruption in terms of meeting the SLAs.
Abstract: This paper studies the impact of capacity/inventory disruption on a supplier's cost, where the supplier has heterogeneous service level agreements (SLAs) in place with multiple customers (retailers...
TL;DR: Wang et al. as mentioned in this paper proposed a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling, which can effectively reduce the energy consumption of cloud data centers while maintaining the lowest SLA violation rate.
TL;DR: In this article, the authors proposed a normalization-based VM consolidation (NVMC) strategy that aims at placing virtual machines in an online manner while minimizing energy consumption, SLA violations, and the number of VM migrations.
Abstract: The cloud computing environments rely heavily on virtualization that enables the physical hardware resources to be shared among cloud users by creating virtual machines (VMs). With an overloaded physical machine, the resource requests by virtual machines may not be fulfilled, which results in Service Level Agreement (SLA) violations. Moreover, the high performance servers in cloud data centers consume large amount of energy. The dynamic VM consolidation techniques use live migration of virtual machines to optimize resource utilization and minimize energy consumption. An excessive migration of virtual machines may however deteriorate application performance due to the overhead incurring at runtime. In this paper, we propose a normalization-based VM consolidation (NVMC) strategy that aims at placing virtual machines in an online manner while minimizing energy consumption, SLA violations, and the number of VM migrations. The proposed strategy uses resource parameters for determining over-utilized hosts in a virtualized cloud environment. The comparative capacity of virtual machines and hosts is incorporated for determining over-utilized hosts, while the cumulative available-to-total ratio (CATR) is used to find under-utilized hosts. For migrating virtual machines to appropriate hosts, the VM placement uses a criteria based on normalized resource parameters of hosts and virtual machines. For evaluating the performance of VM consolidation, we have performed experimentation with a large number of virtual machines using traces from the PlanetLab workloads. The results show that the NVMC approach outperforms other well-known approaches by achieving a significant improvement in energy consumption, SLA violations, and number of VM migrations.
TL;DR: SLAMIG as mentioned in this paper is a set of algorithms that composes deadline-aware multiple migration grouping algorithm and on-line migration scheduling to determine the sequence of VM/VNF migrations.
TL;DR: A Robust Hybrid Auto-Scaler (RHAS) is presented for web applications that achieves a reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.
Abstract: The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current resource requirement. The proposed auto-scaling technique is designed with the threshold-based rules and queuing model. The security mechanism is used to secure the user’s request and response to the web-applications deployed in cloud environment. The designed approach has been tested with two real-time web application workloads of ClarkNet and NASA. The proposed technique achieves $$14\%$$
reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.
TL;DR: A new iterative budget algorithm in which a budget heuristic and a multi-stage selection strategy are designed to find suitable migration objects and targets simultaneously and provides a substantial improvement over other typical heuristics and metaheuristic algorithms in reducing the energy consumption, the number of migrated virtual machines, the overall communication overhead, as well as the decision time.
Abstract: Virtualization is a crucial technology of cloud computing to enable the flexible use of a significant amount of distributed computing services on a pay-as-you-go basis. As the service demand continuingly increases to a global scale, efficient virtual machine consolidation becomes more and more imperative. Existing heuristic algorithms targeted mostly at minimizing either the rate of service level agreement violations or the energy consumption of the cloud. However, the communication overhead among different virtual machines and the decision time of virtual machine consolidation are rarely considered. To reduce both the over-utilized nodes and the under-utilized nodes with the consideration of migration cost, communication overhead, and energy consumption, this paper presents a new iterative budget algorithm in which a budget heuristic and a multi-stage selection strategy are designed to find suitable migration objects and targets simultaneously. Experiments show that the proposed algorithm provides a substantial improvement over other typical heuristics and metaheuristic algorithms in reducing the energy consumption, the number of migrated virtual machines, the overall communication overhead, as well as the decision time.
TL;DR: The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.
TL;DR: OnSlicing as mentioned in this paper proposes an online end-to-end network slicing system to achieve minimal resource usage while satisfying slices' SLA, which allows individualized learning for each slice and maintains its SLA by using a novel constraint-aware policy update method and proactive baseline switching mechanism.
Abstract: Network slicing allows mobile network operators to virtualize infrastructures and provide customized slices for supporting various use cases with heterogeneous requirements. Online deep reinforcement learning (DRL) has shown promising potential in solving network problems and eliminating the simulation-to-reality discrepancy. Optimizing cross-domain resources with online DRL is, however, challenging, as the random exploration of DRL violates the service level agreement (SLA) of slices and resource constraints of infrastructures. In this paper, we propose OnSlicing, an online end-to-end network slicing system, to achieve minimal resource usage while satisfying slices' SLA. OnSlicing allows individualized learning for each slice and maintains its SLA by using a novel constraint-aware policy update method and proactive baseline switching mechanism. OnSlicing complies with resource constraints of infrastructures by using a unique design of action modification in slices and parameter coordination in infrastructures. OnSlicing further mitigates the poor performance of online learning during the early learning stage by offline imitating a rule-based solution. Besides, we design four new domain managers to enable dynamic resource configuration in radio access, transport, core, and edge networks, respectively, at a timescale of subseconds. We implement OnSlicing on an end-to-end slicing testbed designed based on OpenAirInterface with both 4G LTE and 5G NR, OpenDayLight SDN platform, and OpenAir-CN core network. The experimental results show that OnSlicing achieves 61.3% usage reduction as compared to the rule-based solution and maintains nearly zero violation (0.06%) throughout the online learning phase. As online learning is converged, OnSlicing reduces 12.5% usage without any violations as compared to the state-of-the-art online DRL solution.
TL;DR: A feedback control method is designed based on a combination of varying-processing-rate queuing model and linear-model to provision containers elastically which improves the accuracy of output errors by learning reference models for different arrival rates automatically and mapping output errors from reference models to the queued model.
Abstract: Container orchestration platforms such as Kubernetes and Kubernetes-derived KubeEdge (called Kubernetes-based systems collectively) have been gradually used to conduct unified management of Cloud, Fog and Edge resources. Container provisioning algorithms are crucial to guaranteeing quality of services (QoS) of such Kubernetes-based systems. However, most existing algorithms focus on placement and migration of fixed number of containers without considering elastic provisioning of containers. Meanwhile, widely used linear-performance-model-based feedback control or fixed-processing-rate based queuing model on diverse platforms cannot describe the performance of containerized Web systems accurately. Furthermore, a fixed reference point used by existing methods is likely to generate inaccurate output errors incurring great fluctuations encountered with large arrival-rate changes. In this paper, a feedback control method is designed based on a combination of varying-processing-rate queuing model and linear-model to provision containers elastically which improves the accuracy of output errors by learning reference models for different arrival rates automatically and mapping output errors from reference models to the queuing model. Our approach is compared with several state-of-art algorithms on a real Kubernetes cluster. Experimental results illustrate that our approach obtains the lowest percentage of service level agreement (SLA) violation (decreasing no less than 8.44%) and the second lowest cost.
28 Jun 2021
TL;DR: In this article, the authors provide a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing, and further compare existing approaches to identify and highlight gaps in them.
Abstract: Sentiments are the emotions or opinions of an individual encapsulated within texts or images. These emotions play a vital role in the decision-making process for a business. A cloud service provider and consumer are bound together in a Service Level Agreement (SLA) in a cloud environment. SLA defines all the rules and regulations for both parties to maintain a good relationship. For a long-lasting and sustainable relationship, it is vital to mine consumers' sentiment to get insight into the business. Sentiment Analysis or Opinion Mining refers to the process of extracting or predicting different point of views from a text or image to conclude. Various techniques, including Machine Learning and Deep Learning, strives to achieve results with high accuracy. However, most of the existing studies could not unveil hidden parameters in text analysis for optimal decision-making. This work discusses the application of sentiment analysis in the cloud-computing paradigm. The paper provides a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing. The paper further compares existing approaches to identify and highlight gaps in them.
TL;DR: In this paper, the study of various available resource allocation methods, load balancing techniques, scheduling techniques and admission control techniques for cloud computing as well as analysis of advantages and disadvantages of each method is presented.
TL;DR: In this paper, a Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm is proposed to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines.
Abstract: In cloud computing, resource provisioning is a key challenging task due to dynamic resource provisioning for the applications. As per the workload requirements of the application’s resources should be dynamically allocated for the application. Disparities in resource provisioning produce energy, cost wastages, and additionally, it affects Quality of Service (QoS) and increases Service Level Agreement (SLA) violations. So, applications allocated resources quantity should match with the applications required resources quantity. Load balancing in cloud computing can be addressed through optimal scheduling techniques, whereas this solution belongs to the NP-Complete optimization problem category. However, the cloud providers always face resource management issues for variable cloud workloads in the heterogeneous system environment. This issue has been solved by the proposed Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm, which can estimate the application’s upcoming resource demands. This research contributes towards developing the prediction-based model for efficient and dynamic resource provisioning in a heterogamous system environment to fulfill the end user’s requirements. Existing algorithms fail to meet the user’s Quality of Service (QoS) requirements such as makespan minimization and budget constraints satisfaction, or to incorporate cloud computing principles, i.e., elasticity and heterogeneity of computing resources. In this paper, we proposed a PMHEFT algorithm to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines. Experimental results show that our proposed algorithm’s makespan, efficiency, and power consumption are better than other algorithms.
TL;DR: In this article, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization.
Abstract: The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.
01 Apr 2021
TL;DR: To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results and performed better in terms of Quality of Service parameters.
Abstract: Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.