Cloud Workloads Research Articles

PureEdgeSim: A simulation framework for performance evaluation of cloud, edge and mist computing environments

Edge and Mist Computing are two emerging paradigms that aim to reduce latency and the Cloud workload by bringing its applications close to the Internet of Things (IoT) devices. In such complex environments, simulation makes it possible to evaluate the adopted strategies before their deployment on a real distributed system. However, despite the research advancement in this area, simulation tools are lacking, especially in the case of Mist Computing [11], where heterogeneous and constrained devices cooperate and share their resources. Motivated by this, in this paper, we present PureEdgeSim, a simulation toolkit that enables the simulation of Cloud, Edge, and Mist Computing environments and the evaluation of the adopted resources management strategies, in terms of delays, energy consumption, resources utilization, and tasks success rate. To show its capabilities, we introduce a case study, in which we evaluate the different architectures, orchestration algorithms, and the impact of offloading criteria. The simulation results show the effectiveness of PureEdgeSim in modeling such complex and dynamic environments.

Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems

Cloud computing is one of the rapidly growing distributed computing technologies, and cloud-based applications have increased significantly in recent years. The amount of cloud resources and the number of cloud user are important metrics that affect the management of the cloud-based applications. Since the volume of traffic to cloud-based applications grows, the resource provisioning as one of challenging issues to serve time-varying and heterogeneous workloads in resource management scope to be considered. In this paper, we propose a workload clustering-based resource provisioning mechanism for executing cloud-based applications with heterogeneous workloads. Our proposed mechanism utilized biogeography-based optimization (BBO) technique with K-means clustering to classify the cloud workloads according to their quality of service (QoS) requirements. Besides, we used Bayesian learning technique to specify suitable resource provisioning actions to satisfy the QoS requirements of cloud-based applications. The simulation results obtained through simulation demonstrate that the proposed solution reduces the delay, SLA violation ratio, cost, and energy consumption compared with workload clustering-based resource provisioning mechanisms.

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Abstract For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

Enabling Efficient, Secure and Privacy-Preserving Mobile Cloud Storage

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this article, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) protocol as the underlying primitive of the proposed mobile cloud storage scheme. OSU is based on onion additively homomorphic encryption with constant encryption layers and enables the client to obliviously retrieve an encrypted data item from the cloud and update it with a fresh value by generating a small encrypted vector, which significantly reduces the client’s computation as well as the communication overheads. Compared with previous works, our presented work has valuable properties, such as fine-grained data structure (small item size), lightweight client-side computation (a few of additively homomorphic operations) and constant communication overhead, which make it more suitable for MCS scenario. Moreover, by employing the “verification chunks” method, our scheme can be verifiable to resist malicious cloud. The comparison and evaluation indicate that our scheme is more efficient than existing oblivious storage solutions with the aspects of client and cloud workloads, respectively.

A comprehensive test framework for cryptographic accelerators in the cloud

Abstract To alleviate the performance degradation introduced by cryptographic operations in web-based services, dedicated hardware accelerators have emerged for calculation offloading. However, accelerators from different vendors can be endowed with variable run-time modes and optimized for different workload types. It is necessary but challenging to fully and fairly quantify the associated acceleration benefits and determine usages for cloud tasks. To this end, we establish a common test framework oriented for cryptographic accelerators. The micro benchmarks of our framework rely on the classic OpenSSL speed tool. The macro benchmarks of our framework present a server-client architecture. The server side modifies the Nginx web server and integrates it with Redis memory storage as well as a MySQL database to mimic practical cloud workloads in a customizable manner. The client side modifies the classic ApacheBench tool to support concurrent server access as well as automatic collection and analysis of test results. Our framework supports diverse running modes and workload types to comprehensively locate and present the capability characteristics of accelerators. Tests are conducted on three accelerators, i.e., Intel QAT, Cavium Nitrox and Exar DX2040a. These accelerators have similar performance as reported by their vendors. The result analyses and product-to-product comparisons show that our framework can benefit cloud providers, application programmers and accelerator vendors simultaneously by providing important conclusions such as whether to offload, how to offload, and potential optimizations for more efficient offloading.

Secure your cloud workloads with IBM Secure Execution for Linux on IBM z15 and LinuxONE III

With the growth of IBM Z and LinuxONE in the cloud, customers are expecting their workloads and data to have the same levels of security, isolation, and privacy as running on-premise. In order to achieve these levels of trust, the IBM z15 and LinuxONE III provide the IBM Secure Execution for Linux facility, which isolates customers’ data from each other, as well as from the cloud administrators. Unlike other solutions in the industry, IBM Secure Execution does not require remote attestation, thus simplifying the deployment of applications into the protected environment. Also, unlike some other solutions in the industry, the integrity of data is protected end-to-end, that is, from the boot image on disk to memory as it is paged by the hypervisor and throughout execution. The isolation and integrity are provided by hardware and trusted firmware known as the ultravisor. In this article, we describe the security model of IBM Secure Execution, the functionality of the hardware and ultravisor, as well as the required changes to the hypervisor in order to support protected virtual machines.

Containerization for shipping Scientific Workloads in Cloud

Containerization for shipping Scientific Workloads in Cloud

Resource Usage Cost Optimization in Cloud Computing Using Machine Learning

Cloud computing is gaining popularity among small and medium-sized enterprises. The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue. Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e., compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. This article presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration. It is a complete solution which works in a closed loop without the need for external supervision or initialization, builds knowledge about the usage patterns of the system being optimized and filters out anomalous situations on the fly. Our solution can adapt to changes in both system load and the cloud provider’s pricing plan. It was tested in Microsoft’s cloud environment Azure using data collected from a real-life system. Experiments demonstrate that over a period of 10 months, a cost reduction of 85 percent was achieved.

An Approach to Optimise Resource Provision with Energy-Awareness in Datacentres by Combating Task Heterogeneity

Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes increased complexities in achieving an energy efficient management of the Cloud jobs. The phenomenon of a few proportions of tasks characterising increased resource intensity within a given job usually lead the providers to over-provision all the encompassed tasks, resulting in majority of the tasks incurring an increased proportions of resource idleness. To this end, this paper proposes a novel analytics framework which integrates a resource estimation module to estimate the resource requirements of tasks a priori, a straggler classification module to classify tasks based on their resource intensity, and a resource optimisation module to optimise the level of resource provision depending on the task nature and various runtime factors. Performance evaluations conducted both theoretically and through practical experiments prove that the proposed methodology performs better than the compared statistical resource estimation methods and existing models of straggler mitigation, and further demonstrate the effectiveness of the proposed methodology in achieving energy conservation by postulating appropriate level of resource provisioning for task execution.

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviors at the datacenters are being the focus of recent researches in the context of sustainable datacenters. But prediction analytics of cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-means based Rand Variable Learning Rate Backpropagation Neural Network (K-RVLBPNN) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and Backpropagation Neural Network (BPNN) algorithm. Experiments conducted on real-world Cloud datasets shows that the proposed model shows better prediction accuracy, outperforming the traditional Hidden Markov Model, Naive Bayes Classifier, and our earlier RVLBPNN model, respectively.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

Security-Aware Autonomic Allocation of Cloud Resources

Cloud computing has emerged as a dominant platform for computing for the foreseeable future. A key factor in the adoption of this technology is its security and reliability. Here, this article addresses a key challenge which is the secure allocation of resources. The authors propose a security-based resource allocation model for execution of cloud workloads called STARK. The solution is designed to ensure security against probing, User to Root (U2R), Remote to Local (R2L) and Denial of Service (DoS) attacks whilst the execution of heterogeneous cloud workloads. Further, this paper highlights the promising directions for future research.

A Comparative Study and Analysis of Time Series Forecasting Techniques

Time series data abound in many realistic domains. The proper study and analysis of time series data help to make important decisions. Study of such data is very useful in many applications where there are trendy changes with time or specific seasonality as in electricity demand, cloud workload, weather and sales, cost of business products, etc. By understanding the nature of the time series and the objective of analysis, we have used different approaches to learn and extract meaningful information that can satisfy the business needs. The present paper covers and compares various forecasting algorithmic approaches and explores their limitations and usefulness for different types of time series data in different domains.

Resource provisioning using workload clustering in cloud computing environment: a hybrid approach

In recent years, cloud computing paradigm has emerged as an internet-based technology to realize the utility model of computing for serving compute-intensive applications. In the cloud computing paradigm, the IT and business resources, such as servers, storage, network, and applications, can be dynamically provisioned to cloud workloads submitted by end-users. Since the cloud workloads submitted to cloud providers are heterogeneous in terms of quality attributes, management and analysis of cloud workloads to satisfy Quality of Service (QoS) requirements can play an important role in cloud resource management. Therefore, it is necessary for the provisioning of proper resources to cloud workloads using clustering of them according to QoS metrics. In this paper, we present a hybrid solution to handle the resource provisioning issue using workload analysis in a cloud environment. Our solution utilized the Imperialist Competition Algorithm (ICA) and K-means for clustering the workload submitted by end-users. Also, we use a decision tree algorithm to determine scaling decisions for efficient resource provisioning. The effectiveness of the proposed approach under two real workloads traces is evaluated. The simulation results demonstrate that the proposed solution reduces the total cost by up to 6.2%, and the response time by up to 6.4%, and increases the CPU utilization by up to 13.7%, and the elasticity by up to 30.8% compared with the other approaches.

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

Workload Mining in Cloud Computing using Extended Cloud Dempster–Shafer Theory (ECDST)

Cloud computing is a growing technology where the resources are provided as a service on demand basis. The services offered are Infrastructure as a Service, Platform as a Service, Software as a Service, Network as a Service etc., Based on the requests or the workloads received from the customer side, the resources are fairly allocated to the cloud customers to complete their jobs in time. As there exists huge volume of resources in cloud computing, plenty of workloads from various users are submitted to the cloud workload analyzer. Identifying and analyzing the huge volume of workloads in the cloud computing environment within a particular time is found to be an important and highly complexity. Hence this paper proposes an Extended Cloud Dempster–Shafer Theory based clustering algorithm for identifying, analyzing, classifying and clustering the workloads efficiently. The experimental result demonstrates that the proposed Extended Cloud Dempster–Shafer Theory based clustering algorithm performs clustering accurately and also reduces the execution time of cloud workloads efficiently by comparing its performance with QoS attribute’s weight based clustering algorithm.

A Cost-Efficient Container Orchestration Strategy in Kubernetes-Based Cloud Computing Infrastructures with Heterogeneous Resources

Containers, as a lightweight application virtualization technology, have recently gained immense popularity in mainstream cluster management systems like Google Borg and Kubernetes. Prevalently adopted by these systems for task deployments of diverse workloads such as big data, web services, and IoT, they support agile application deployment, environmental consistency, OS distribution portability, application-centric management, and resource isolation. Although most of these systems are mature with advanced features, their optimization strategies are still tailored to the assumption of a static cluster. Elastic compute resources would enable heterogeneous resource management strategies in response to the dynamic business volume for various types of workloads. Hence, we propose a heterogeneous task allocation strategy for cost-efficient container orchestration through resource utilization optimization and elastic instance pricing with three main features. The first one is to support heterogeneous job configurations to optimize the initial placement of containers into existing resources by task packing. The second one is cluster size adjustment to meet the changing workload through autoscaling algorithms. The third one is a rescheduling mechanism to shut down underutilized VM instances for cost saving and reallocate the relevant jobs without losing task progress. We evaluate our approach in terms of cost and performance on the Australian National Cloud Infrastructure (Nectar). Our experiments demonstrate that the proposed strategy could reduce the overall cost by 23% to 32% for different types of cloud workload patterns when compared to the default Kubernetes framework.

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning long-term memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using real-world workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads.

Coupling energy efficiency and quality for consolidation of cloud workloads

Efficient usage of IT equipment in Data Centers requires modulating their power-consumption according to the actual workload. The most effective strategy to this aim consists in consolidating as many applications as possible on the smallest number of servers, so that idle devices can be shut down or put in low-power states. Usually, this process is driven by computing and networking resources requested by each application (e.g., CPU and RAM) and applies a certain degree of overcommitment, assuming that such resources are not fully used continuously. However, this approach is critical with real workload patterns, which usually change over time; as a matter of fact, consolidation in real scenarios often leads to either low efficiency or violations of Quality of Service (QoS) constraints, depending on the level of overcommitment.In this paper, we investigate a novel consolidation strategy based on an enhanced system model for the Infrastructure-as-a-Service cloud paradigm, which targets a better trade-off between Energy Efficiency and Quality of Service. We explicitly target modular cloud applications, which design is split into multiple components deployed in Virtual Machine (VM)s or containers. Our consolidation strategy allows to “freeze” parts of the application which are not currently used, making them available when requested with minimal latency. This improves energy saving with respect to other approaches, especially when idle VMs are present for backup or redundancy purposes, without degrading the service level. We compare multiple heuristics available in Optaplanner to solve our consolidation problem, and investigate improvements with respect to a more traditional approach. Our evaluation includes both simulations and experimentation in a real test-bed. The comparison shows that the Late Acceptance algorithm on average finds better solutions than other alternatives and energy efficiency improves up to 40% with respect to more conventional strategies, with deterioration of QoS indexes below 1%.