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.

     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

Cost-oriented proactive fault tolerance approach to high performance computing (HPC) in the cloud

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

Priori information and sliding window based prediction algorithm for energy-efficient storage systems in cloud

One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

Resource provisioning for cloud applications: a 3-D,provident and flexible approach

The scalability feature of cloud computing attracts application service providers (ASPs) to use cloud application hosting. In cloud environments, resources can be dynamically provisioned on demand for ASPs. Autonomic resource provisioning for the purpose of preventing resources over-provisioning or under-provisioning is a widely investigated topic in cloud environments. There has been proposed a lot of resource-aware and/or service-level agreement (SLA)-aware solutions to handle this problem. However, intelligence solutions such as exploring the hidden knowledge on the Web users’ behavior are more effective in cost efficiency. Most importantly, with considering cloud service diversity, solutions should be flexible and customizable to fulfill ASPs’ requirements. Therefore, lack of a flexible resource provisioning mechanism is strongly felt. In this paper, we proposed an autonomic resource provisioning mechanism with resource-aware, SLA-aware, and user behavior-aware features, which is called three-dimensional mechanism. The proposed mechanism used radial basis function neural network in order to provide providence and flexibility features. The experimental results showed that the proposed mechanism reduces the cost while guarantees the quality of service.     

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.     

Model-driven optimal resource scaling in cloud

Cloud computing offers the flexibility to dynamically size the infrastructure in response to changes in workload demand. While both horizontal scaling and vertical scaling of infrastructure are supported by major cloud providers, these scaling options differ significantly in terms of their cost, provisioning time, and their impact on workload performance. Importantly, the efficacy of horizontal and vertical scaling critically depends on the workload characteristics, such as the workload’s parallelizability and its core scalability. In today’s cloud systems, the scaling decision is left to the users, requiring them to fully understand the trade-offs associated with the different scaling options. In this paper, we present our solution for optimizing the resource scaling of cloud deployments via implementation in OpenStack. The key component of our solution is the modeling engine that characterizes the workload and then quantitatively evaluates different scaling options for that workload. Our modeling engine leverages Amdahl’s Law to model service timescaling in scale-up environments and queueing-theoretic concepts to model performance scaling in scale-out environments. We further employ Kalman filtering to account for inaccuracies in the model-based methodology and to dynamically track changes in the workload and cloud environment.     

Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka

Scientific applications require large computing power, traditionally exceeding the amount that is available within the premises of a single institution. Therefore, clouds can be used to provide extra resources whenever required. For this vision to be achieved, however, requires both policies defining when and how cloud resources are allocated to applications and a platform implementing not only these policies but also the whole software stack supporting management of applications and resources. Aneka is a cloud application platform capable of provisioning resources obtained from a variety of sources, including private and public clouds, clusters, grids, and desktops grids. In this paper, we present Aneka’s deadline-driven provisioning mechanism, which is responsible for supporting quality of service (QoS)-aware execution of scientific applications in hybrid clouds composed of resources obtained from a variety of sources. Experimental results evaluating such a mechanism show that Aneka is able to efficiently allocate resources from different sources in order to reduce application execution times.     

Cloud workflow scheduling with hybrid resource provisioning

Resource provisioning strategies are crucial for workflow scheduling problems which are widespread in cloud computing. The main challenge lies in determining the amounts of reserved and on-demand resources to meet users’ requirements. In this paper, we consider the cloud workflow scheduling problem with hybrid resource provisioning to minimize the total renting cost, which is NP-hard and has not been studied yet. An iterative population-based meta-heuristic is developed. According to the shift vectors obtained during the search procedure, timetables are computed quickly. The appropriate amounts of reserved and on-demand resources are determined by an incremental optimization method. The utilization of each resource is balanced in a swaying way, in terms of which the probabilistic matrix is updated for the next iteration. The proposed algorithm is compared with modified existing algorithms for similar problems. Experimental results demonstrate effectiveness and efficiency of the proposed algorithm.     

Empirical evaluation of a cloud computing information security governance framework

ContextCloud computing is a thriving paradigm that supports an efficient way to provide IT services by introducing on-demand services and flexible computing resources. However, significant adoption of cloud services is being hindered by security issues that are inherent to this new paradigm. In previous work, we have proposed ISGcloud, a security governance framework to tackle cloud security matters in a comprehensive manner whilst being aligned with an enterprise’s strategy.ObjectiveAlthough a significant body of literature has started to build up related to security aspects of cloud computing, the literature fails to report on evidence and real applications of security governance frameworks designed for cloud computing environments. This paper introduces a detailed application of ISGCloud into a real life case study of a Spanish public organisation, which utilises a cloud storage service in a critical security deployment.MethodThe empirical evaluation has followed a formal process, which includes the definition of research questions previously to the framework’s application. We describe ISGcloud process and attempt to answer these questions gathering results through direct observation and from interviews with related personnel.ResultsThe novelty of the paper is twofold: on the one hand, it presents one of the first applications, in the literature, of a cloud security governance framework to a real-life case study along with an empirical evaluation of the framework that proves its validity; on the other hand, it demonstrates the usefulness of the framework and its impact to the organisation.ConclusionAs discussed on the paper, the application of ISGCloud has resulted in the organisation in question achieving its security governance objectives, minimising the security risks of its storage service and increasing security awareness among its users.     

An Evaluation of the Cost and Performance of Scientific Workflows on Amazon EC2

Workflows are used to orchestrate data-intensive applications in many different scientific domains. Workflow applications typically communicate data between processing steps using intermediate files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. As a result, the efficient management of data is a key factor in achieving good performance for workflow applications in distributed environments. In this paper we investigate some of the ways in which data can be managed for workflows in the cloud. We ran experiments using three typical workflow applications on Amazon’s EC2 cloud computing platform. We discuss the various storage and file systems we used, describe the issues and problems we encountered deploying them on EC2, and analyze the resulting performance and cost of the workflows.     

An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.     

Cloud Computing: Distributed Internet Computing for IT and Scientific Research

Cloud computing is a disruptive technology with profound implications not only for Internet services but also for the IT sector as a whole. Its emergence promises to streamline the on-demand provisioning of software, hardware, and data as a service, achieving economies of scale in IT solutions' deployment and operation. This issue's articles tackle topics including architecture and management of cloud computing infrastructures, SaaS and IaaS applications, discovery of services and data in cloud computing infrastructures, and cross-platform interoperability. Still, several outstanding issues exist, particularly related to SLAs, security and privacy, and power efficiency. Other open issues include ownership, data transfer bottlenecks, performance unpredictability, reliability, and software licensing issues. Finally, hosted applications' business models must show a clear pathway to monetizing cloud computing. Several companies have already built Internet consumer services such as search, social networking, Web email, and online commerce that use cloud computing infrastructure. Above all, cloud computing's still unknown "killer application" will determine many of the challenges and the solutions we must develop to make this technology work in practice.     

Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.     

Provably secure authenticated key agreement scheme for distributed mobile cloud computing services

With the rapid development of mobile cloud computing, the security becomes a crucial part of communication systems in a distributed mobile cloud computing environment. Recently, in 2015, Tsai and Lo proposed a privacy-aware authentication scheme for distributed mobile cloud computing services. In this paper, we first analyze the Tsai–Lo’s scheme and show that their scheme is vulnerable to server impersonation attack, and thus, their scheme fails to achieve the secure mutual authentication. In addition, we also show that Tsai–Lo’s scheme does not provide the session-key security (SK-security) and strong user credentials’ privacy when ephemeral secret is unexpectedly revealed to the adversary. In order to withstand these security pitfalls found in Tsai–Lo’s scheme, we propose a provably secure authentication scheme for distributed mobile cloud computing services. Through the rigorous security analysis, we show that our scheme achieves SK-security and strong credentials’ privacy and prevents all well-known attacks including the impersonation attack and ephemeral secrets leakage attack. Furthermore, we simulate our scheme for the formal security analysis using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool, and show that our scheme is secure against passive and active attacks including the replay and man-in-the-middle attacks. More security functionalities along with reduced computational costs for the mobile users make our scheme more appropriate for the practical applications as compared to Tsai–Lo’s scheme and other related schemes. Finally, to demonstrate the practicality of the scheme, we evaluate the proposed scheme using the broadly-accepted NS-2 network simulator.     

Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

Cloud computing is on-demand provisioning of virtual resources aggregated together so that by specific contracts users can lease access to their combined power.Here we hypothesize a new form of service contract by means of which users do not explicitly require resources, but simply supply information about their time-consuming multitask applications and specify their needs through some quality of service (QoS) parameters. The individuation of the virtual machines (VMs) onto which map and execute them is left to the cloud manager. Unfortunately the task/node mapping, already known as NP-hard for conventional parallel systems, becomes more challenging when application tasks must be run on VMs hosted on heterogeneous and shared cloud nodes, and when it must comply with QoS requests too. To support this new cloud service, a novel mapper tool, based on a multiobjective Differential Evolution algorithm, is proposed. Such a tool defines the mapping of the tasks on the VMs with the aim to exploit as much as possible the available cloud resources without penalizing the execution time of the submitted applications and, at the same time, to respect users’ QoS requests.To reveal the robustness of this evolutionary tool, an experimental analysis on artificial time-consuming parallel applications, modeled as task interaction graphs, has been effected.     

A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing

Dynamic resource provisioning is a challenging technique to meet the service level agreement (SLA) requirements of multi-tier applications in virtualization-based cloud computing. Prior efforts have addressed this challenge based on either a cost-oblivious approach or a cost-aware approach. However, both approaches may suffer frequent SLA violations under flash crowd conditions. Because they ignore the benefit gained that a multi-tier application continuously guarantees the SLA in the new configuration. In this paper, we propose a benefit-aware approach with feedback control theory to solve this problem. Experimental results based on live workload traces show that our approach can reduce resource provisioning cost by as much as 30% compared with a costoblivious approach, and can effectively reduce SLA violations compared with a cost-aware approach.     

云计算在检察机关的应用前景浅析

随着云计算从概念走向实用,各行业都对云计算进行一定地研究。文章从检察机关云计算应用探索入手,针对检察机关业务需求特性,探讨了云计算在我国检察机关应用的前景及需要解决的安全问题。