Distributed Cloud Environment Research Articles

The implementation and performance evaluation for a smart robot with edge computing algorithms

PurposeApart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.Design/methodology/approachCertainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.FindingsSpecifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.Originality/valueThe work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

A method for reducing cloud service request peaks based on game theory

The rapid development of Internet of Things (IoT) technology and the popularity of Artificial Intelligence (Al) technology research have brought new opportunities for the development of cloud computing (CC). With the increasing number of mobile Internet access devices and IoT access devices, the number of task requests from CC customers for AI services in the network has also experienced an explosive growth. In this paper, the focus is on the possible overload of cloud providers during the peak period of cloud service requests. Thetime attributes of cloud task execution are classified to avoid overloading the cloud provider as much as possible. In a distributed cloud environment, it is necessary to consider the time flexible attributes of cloud tasks to reasonably compete for cloud resources. In this work, game theory (GT) is introduced to formulate a cloud service scheduling game, in which participants are cloud customers who participate in the purchase of cloud services. The players' strategies are the time flexibility of each cloud task. The problem is formulated as minimizing the cost of scheduling cloud services and a noncooperative game among the customers (as players) is presented. Then the existence of the Nash equilibrium (NE) solution of the game has been proved and a new algorithm has been proposed in this paper to compute it. In addition, the analysis process of the convergence of the proposed PCA algorithm and the proof of its convergence to NE are also included in this paper. At the end of the paper, simulations were performed to verify the theoretical analysis presented. The experimental results show that the proposed PCA algorithm can converge to the Nash equilibrium very quickly, effectively reducing the peak value and increasing profit.

Study on Deduplication on Distributed Cloud Environment

Deduplication techniques were intended to annihilate duplicate data which achieve limit of single copies of data figuratively speaking. Data Deduplication reduces the circle space expected to store the back-ups in the additional room, tracks and kill the second copy of data inside the limit unit. It licenses so to speak one case data occasion to be taken care of at first and subsequently following events will be given reference pointer to the principal data set aside. In a Major data storing environment, massive proportion of data ought to be secure. For this real organization, work, distortion ID, examination of data security is a huge topic to be considered. This paper examines and surveys the normal deduplication methods and which are presented in plain construction. In this audit, it was seen that the mystery and security of data has been sabotaged at various levels in like manner techniques for deduplication. Though much investigation is being done in various zones of appropriated processing actually work connecting with this point is deficient. To dispose of copy information which brings about capacity of single duplicates of information, information deduplication methods were utilized. Information deduplication helps in diminishing stockpiling limit necessities and dispenses with additional duplicates of same information inside capacity unit. Legitimate administration, work, misrepresentation location, examination of information protection are the points to be considered in a major information stockpiling climate, since, huge measure of information should be secure. At many levels overall procedures for deduplication it is seen that wellbeing of information and privacy has been compromised. Despite the fact that more exploration is being completed in various areas of distributed computing actually business related to this subject is close to nothing.

Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review

As the functionality and services provided by cloud computing increase, control access to these services becomes more complex, and more security breaches are generated. This is mainly based on the emergence of new requirements and constraints in the open, dynamic, heterogeneous, and distributed cloud environment. Despite the importance of identifying these requirements for designing and evaluating access control models, the available studies do not provide a rigorous review of these requirements and the mechanisms that fulfill them. The purpose of this study was to conduct a literature review of the published articles that have dealt with cloud access control requirements and techniques. This paper allowed us to answer the following two research questions: What cloud access control security requirements have been presented in the published literature? What access control mechanisms are proposed to fulfill them? This review yielded 21 requirements and nine mechanisms, reported by 20 manuscripts. The identified requirements in this review will help researchers, academics and practitioners assess the effectiveness of cloud access control models and identify gaps that are not addressed in the proposed solutions. In addition, this review showed the current cloud access control mechanisms used to meet these requirements such as access control based on trust, risk, multi-tenant, and attribute encryption.

KeyPIn \u2013 mitigating the free rider problem in the distributed cloud based on Key, Participation, and Incentive

In a distributed cloud, unlike centralized resource management, users provide and share resources. However, this allows for the existence of free riders who do not provide resources to others, but at the same time use resources that others provide. In a distributed cloud, resource providers share resources in a P2P fashion. In this paper, we propose a 3-pronged solution KeyPIn—a Key-based, Participation-based, and Incentive-based scheme to mitigate the free rider problem in a distributed cloud environment. We propose an incentive-based scheme based on game theory for providers to participate in the cloud by providing resources. This participation will be low for free riders thereby limiting their access to resources. A secure time instant key is generated based on a key management scheme that enables good users’ more time to access resources as their participation is high, whereas free riders are given limited or no time as their participation is low. Simulation results show that our scheme is effective in mitigating the free rider problem in the distributed cloud.

Secure Data Deduplication and Data Portability in Distributed Cloud Server Using Hash Chaining and Lf-Wdo

An application of Cloud Computing (CC) technologies for interconnecting data as well as applications served as of manifold geographic locations is the distributed cloud. For distributed CC, managing a vast measure of data along with maintaining security are vital aspects. Though lots of techniques were introduced recently to manage and protect data on the distributed cloud, those have not rendered any desirable security. Thus, this paper proposes a secure Data Deduplication (DD) and Data Portability (DP) for a distributed cloud environment. To enhance the user data’s security, primarily, the inputted files are bifurcated into blocks and after that, an appropriate Cloud Server (CS) is chosen utilizing Hybrid Forest Genetic Algorithm (HFGA) centred upon split file features together with CS features. In a DD phase, the Whirlpool algorithm converts the split data into Hash Code (HC), and then, the hash chaining technique securely removes the duplicated data. Then, that split data is comp ressed, encrypted, together with amassed in the selected CS. Next, the Levy Flight – Wind Driven Optimization (LF-WDO) performs the DP to enhance the cloud data’s security. Investigational outcomes exhibit the proposed work's potential.

Fine-grained Access Control in Distributed Cloud Environment using Trust Valuation Model

Cloud computing has been in existence as an adaptable technology that gets integrated with IoT, Big-Data, and WSN to provide reliable, scalable and mesh-free services. However, because of its openness in nature, the privacy of the cloud is an important parameter for today’s research. The most important privacy factor in cloud is access control and user trust. Many articles related to access control and trust management were presented, but most of them include highly complex algorithms that result in network overhead. This proposed security framework is for a better and more effective system wherein multiple distributed centers are created with trust-based computing for authentication and validation of requests from users and their resources. The idea of trust here is for efficient decision-making and establishing reliable relationships among users and resources using least computations. Each user has different permissions for each file present in the cloud server. The simulated results shows improvement in the rate of successful transactions, time cost and network overhead.

Early Conflict Checking in Two-Phase Commit Protocol for Replicated State Machines

Representing an algorithmic workflow as a state machine is a frequently used technique in distributed systems. Replicating a state machine in a fault tolerant way is one of the main application areas under this context. When implementing a replicated state machine, a crucial problem is to maintain consistency among replicas that might handle various different requests arriving at each different replica. This problem requires maintaining a single consistent ordering of the distributed requests handled separately by replicas. Basic consensus protocols such as two-phase commit (2PC) can be used to maintain consistency between replicas whenever a request is to be processed. In this study, the authors modify 2PC protocol to take advantage of basic properties of a state machine and detect possible write conflicts earlier. The experiments on distributed cloud environments show that the modified 2PC protocol increases the throughput and decreases wasted write operations by a significant amount.

Study on Deduplication on Distributed Cloud Environment

Deduplication methods were designed to destroy copy information which bring about capacity of single duplicates of information as it were. Information Deduplication diminishes the circle space needed to store the back-ups in the extra room, tracks and kill the second duplicate of information inside the capacity unit. It permits as it were one case information event to be put away initially and afterward following occasions will be given reference pointer to the first information put away. In a Big information stockpiling climate, immense measure of information should be secure. For this legitimate administration, work, misrepresentation identification, investigation of information protection is an significant theme to be thought of. This paper inspects and assesses the common deduplication procedures and which are introduced in plain structure. In this review, it was seen that the secrecy and security of information has been undermined at numerous levels in common strategies for deduplication. Albeit much exploration is being done in different zones of distributed computing still work relating to this point is inadequate. To get rid of duplicate data which results in storage of single copies of data, data deduplication techniques were used. Data deduplication helps in decreasing storage capacity requirements and eliminates extra copies of same data inside storage unit. Proper management, work, fraud detection, analysis of data privacy are the topics to be considered in a big data storage environment, since, large amount of data needs to be secure. At many levels in general techniques for deduplication it is observed that safety of data and confidentiality has been compromised. Even though more research is being carried out in different areas of cloud computing still work related to this topic is little.

Blockchain Processing Technique Based on Multiple Hash Chains for Minimizing Integrity Errors of IoT Data in Cloud Environments.

As IoT (Internet of Things) devices are diversified in the fields of use (manufacturing, health, medical, energy, home, automobile, transportation, etc.), it is becoming important to analyze and process data sent and received from IoT devices connected to the Internet. Data collected from IoT devices is highly dependent on secure storage in databases located in cloud environments. However, storing directly in a database located in a cloud environment makes it not only difficult to directly control IoT data, but also does not guarantee the integrity of IoT data due to a number of hazards (error and error handling, security attacks, etc.) that can arise from natural disasters and management neglect. In this paper, we propose an optimized hash processing technique that enables hierarchical distributed processing with an n-bit-size blockchain to minimize the loss of data generated from IoT devices deployed in distributed cloud environments. The proposed technique minimizes IoT data integrity errors as well as strengthening the role of intermediate media acting as gateways by interactively authenticating blockchains of n bits into n + 1 and n − 1 layers to normally validate IoT data sent and received from IoT data integrity errors. In particular, the proposed technique ensures the reliability of IoT information by validating hash values of IoT data in the process of storing index information of IoT data distributed in different locations in a blockchain in order to maintain the integrity of the data. Furthermore, the proposed technique ensures the linkage of IoT data by allowing minimal errors in the collected IoT data while simultaneously grouping their linkage information, thus optimizing the load balance after hash processing. In performance evaluation, the proposed technique reduced IoT data processing time by an average of 2.54 times. Blockchain generation time improved on average by 17.3% when linking IoT data. The asymmetric storage efficiency of IoT data according to hash code length is improved by 6.9% on average over existing techniques. Asymmetric storage speed according to the hash code length of the IoT data block was shown to be 10.3% faster on average than existing techniques. Integrity accuracy of IoT data is improved by 18.3% on average over existing techniques.

S-MPEC: Sparse Matrix Multiplication Performance Estimator on a Cloud Environment

Sparse matrix multiplication (SPMM) is widely used for various machine learning algorithms. As the applications of SPMM using large-scale datasets become prevalent, executing SPMM jobs on an optimized setup has become very important. Execution environments of distributed SPMM tasks on cloud resources can be set up in diverse ways with respect to the input sparse datasets, distinct SPMM implementation methods, and the choice of cloud instance types. In this paper, we propose S-MPEC which can predict latency to complete various SPMM tasks using Apache Spark on distributed cloud environments. We first characterize various distributed SPMM implementations on Apache Spark. Considering the characters and hardware specifications on the cloud, we propose unique features to build a GB-regressor model and Bayesian optimizations. Our proposed S-MPEC model can predict latency on an arbitrary SPMM task accurately and recommend an optimal implementation method. Thorough evaluation of the proposed system reveals that a user can expect 44% less latency to complete SPMM tasks compared with the native SPMM implementations in Apache Spark.

Hierarchical Multipath Blockchain Based IoT Information Management Techniques for Efficient Distributed Processing of Intelligent IoT Information

As cloud technology advances, devices such as IoT (Internet of Things) are being utilized in various areas ranging from transportation, manufacturing, energy, automation, space, defense, and healthcare. As the number of IoT devices increases, the safety of IoT information, which is vulnerable to cyber attacks, is emerging as an important area of interest in distributed cloud environments. However, integrity techniques are not guaranteed to easily identify the integrity threats and attacks on IoT information operating in the distributed cloud associated with IoT systems and CPS (Cyber-Physical System). In this paper, we propose a blockchain-based integrity verification technique in which large amounts of IoT information processed in distributed cloud environments can be guaranteed integrity in security threats related to IoT systems and CPS. The proposed technique aims to ensure the integrity of IoT information by linking information from IoT devices belonging to subgroups in distributed cloud environments to information from specific non-adjacent IoT devices and blockchain. This is because existing techniques rely on third-party organizations that the data owner can trust to verify the integrity of the data. The proposed technique identifies IoT information by connecting the paths of IoT pre- and subsequent blocks into block chains so that synchronization can be achieved between subgroups in distributed cloud environments. Furthermore, the proposed technique uses probabilistic similarity information between IoT information blocks to react flexibly to subgroups that constitute distributed clouds so that IoT information blocks are not exploited maliciously by third parties. As a result of performance evaluation, the proposed technique averaged 12.3% improvement in integrity processing time over existing techniques depending on blockchain size. Furthermore, the proposed technique has to hash the IoT information that constitutes a subgroup with probability-linked information, validating the integrity of large-capacity IoT information, resulting in an average of 8.8% lower overhead than existing techniques. In addition, the proposed technique has an average improvement of 14.3% in blockchain-based integrity verification accuracy over existing techniques, depending on the hash chain length.

SDN 기반 분산 클라우드 오케스트레이션 시스템 구현

최근 클라우드 사용자 및 서비스의 확산과 다변화에 따라 기존의 클라우드 자원 운영 및 서비스 제공 방식에서 벗어나 보다 세분화된 형태의 가상화(Virtualization) 기술을 바탕으로 한 소프트웨어 기반의 자원 관리 및 통합 제어 시스템에 대한 수요가 증대되고 있다. 하지만 사용자가 요구하는 다양한 서비스를 제공하기 위하여 컴퓨팅, 네트워킹을 포함한 다양한 클라우드 자원의 관리 및 제어를 통합적으로 고려하기 어려우며, 더욱이 이를 기존의 시스템이 온디맨드로 신속하게 관리 및 제어하기에는 한계가 있다. 이에 따라, 본 논문에서는 분산 클라우드 환경에서 컨테이너 형태의 가상화된 서비스 자원과 소프트웨어 정의 네트워크 환경에서의 가상화된 네트워크 자원을 통합하여 효율적으로 제공할 수 있는 방안으로써 분산된 클라우드 자원들의 위치 및 부하 등을 고려한 통합 오케스트레이션 시스템을 제안한다. 또한 실제 구축되어 운용중인 SDN 광역망 환경에 제안하는 오케스트레이션 시스템을 구현하여 적용함으로써 제안 시스템을 실증하는 한편 사용자 요구에 따라 동적으로 생성된 컨테이너 간 데이터 전송 성능 측정하여 이의 결과를 보인다.

Multi-Objective based test case selection and prioritization for distributed cloud environment

Software testing is highly significant during the software development lifecycle for the earlier detection of errors and bugs. Test suite optimization is a selection of the smallest subset of the test cases. This enables quick recovery of the faults in the software product within a minimum time. Selection and prioritization of the test cases are the main approaches to solve the issues in the test case optimization. Various selection and prioritization techniques are developed in the recent days. However, the existing techniques require more cost and time consumption. Also, the increase in the number of objectives reduces the performance of the existing techniques. This paper proposes a multi-objective based test case selection and prioritization for distributed cloud environment. The Resemblance-Based Cluster Head (RBCH) algorithm is proposed to select the Cluster Head (CH) based on the overall similarity between the test cases. The Distance-Based Transposition (DBT) is proposed to prioritize the optimal test case clusters in the distributed environment. This proposed approach efficiently minimizes the time consumption for the testing process by reducing the number of iterations in the test case searching process. From the experimental result, it is observed that the proposed approach achieves higher fault detection rate, prioritization accuracy and lower execution time than the existing prioritization techniques.

Cluster load based content distribution and speculative execution for geographically distributed cloud environment

The scale of big data has shown an explosive growth, which makes the processing of big data put forward higher requirements on data centers, and a single data center can no longer meet the needs of big data processing. To deal with this situation, a geographically distributed cloud system needs to be built. However, in the geographically distributed cloud system, each data center is distributed in different geographic locations, which makes the data placement operations in the geographically distributed cloud system lead to greater overhead. To solve this problem, this paper proposes a data placement strategy. This strategy comprehensively considers the data transmission latency, bandwidth cost, cloud server storage capacity, and load capacity during the data placement process, and formulates a data placement problem that minimizes the energy consumption of data transmission. Then the minimum set cover method based on Lagrangian relaxation is used to solve this problem and obtain the optimal data placement scheme. On the other hand, in a geographically distributed cloud data center, the execution progress of the job submitted by the user will be affected by the straggler task. To solve this problem, this paper proposes a speculative execution strategy for the geographically distributed cloud system. This strategy performs different speculative execution operations according to the state of the cluster load, and then calculates the load capacity of the nodes in the cluster. The node with the strongest load capacity in the cluster is used to perform speculative execution operations. Experimental results show that the proposed data placement strategy can effectively improve the performance of the energy consumption, the data storage cost, the network transmission cost and the data transmission time. The proposed speculative execution strategy can effectively improve the performance of the job completion time, cluster throughput and QoS satisfaction rate.

LISP-Based Integrated Control Plane Framework for Service Function Chaining in Distributed Edge Clouds

Service function chaining, which provides a sequential set of multiple services in an end-to-end communication path, is a key technology for providing better service quality to users and efficient management of the network. Recently, as the services traditionally deployed in the data center have become more proximate to the user using a distributed cloud environment, the management of the service chain has become more complex for connecting services between clouds at a distance. In particular, when a user moves and resumes end-to-end communication at a new location, these complexities may lead to an increase in service downtime, which results in network performance degradation. In this paper, we propose an integrated control system to quickly recover the path to the service chain along with mobility management. The proposed system is based on the locator/identifier separation protocol to provide cost-effective mobility management by integrating the location information of users and services into the service chain mapping system. Based on the analysis of the operating cost and handover delay, it was determined that the proposed system can quickly restore a service chain path between distributed clouds with less signaling exchange and minimum delay when compared to previous approaches.

Distributed task allocation in Mobile Device Cloud exploiting federated learning and subjective logic

Mobile Device Cloud (MDC) has become a promising and lucrative cloud environment that exploit nearby mobile devices’ idle resources to improve compute-intensive applications. Computing code at nearby mobile devices rather than a distant master cloud helps improve real-time applications’ performance. However, it is non-trivial to motivate the worker devices to participate voluntarily in sharing their unused resources. In this paper, we have provided a distributed mobile device cloud environment by which workers make their auction decisions distributively and parallelly. We also introduce the federated learning and multi-weight subjective logic-based reputation scheme to measure worker mobile devices’ trustworthiness and reliability. Moreover, a novel utility function for the buyers is proposed considering the cost, Quality-of-Experience (QoE), and the workers’ reputation by which buyers select the most suitable worker in a distributed way. We have also proved that our proposed system achieves the desirable properties of computational efficiency, individual rationality, truthfulness, and budget balance. Empirical evaluations have been carried out in MATLAB that demonstrate the significant performance improvement in terms of QoE and utility of the buyers compared to other state-of-the-art works.

Cost- and Time-Based Data Deployment for Improving Scheduling Efficiency in Distributed Clouds

Abstract In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment

Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.