Multi-cloud Environment Research Articles

Distributed Data Integrity Verification Scheme in Multi-Cloud Environment.

Most existing data integrity auditing protocols in cloud storage rely on proof of probabilistic data possession. Consequently, the sampling rate of data integrity verification is low to prevent expensive costs to the auditor. However, in the case of a multi-cloud environment, the amount of stored data will be huge. As a result, a higher sampling rate is needed. It will also have an increased cost for the auditor as a consequence. Therefore, this paper proposes a blockchain-based distributed data integrity verification protocol in multi-cloud environments that enables data verification using multi-verifiers. The proposed scheme aims to increase the sampling rate of data verification without increasing the costs significantly. The performance analysis shows that this protocol achieved a lower time consumption required for verification tasks using multi-verifiers than a single verifier. Furthermore, utilizing multi-verifiers also decreases each verifier's computation and communication costs.

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment. Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives. Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.

Optimizing Resource Allocation Framework for Multi-Cloud Environment

Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and rational resource allocation is to attain the quality of service. It also includes identifying critical parameters to develop a resource allocation mechanism. A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment. The three main parameters of the proposed framework are data accessibility, optimization, and collaboration. Using an optimization technique, these three segments are further divided into subsets for resource allocation and long-term service quality. The CloudSim simulator has been used to validate the suggested framework. Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS. The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

Handling security issues by using homomorphic encryption in multi-cloud environment

Taking advantage of the high performance and powerful data processing capabilities of cloud computing technology, externalizing data to the cloud platform is considered as an inevitable trend in the digital field today. However, ensuring the security and privacy of data remains a major challenge. To overcome this drawback, a multi-cloud platform is proposed to improve privacy and high availability of data. A multi-cloud platform that integrates public, private, and managed clouds with a single user interface. Cloud-hosted data is distributed among different data centers in a multi-cloud environment based on cloud reliability and data sensitivity. In terms of security, current encryption algorithms are considered to be very efficient, but it requires a lot of resources to handle this, which is expensive and time consuming. In addition, they also make the data impossible to process without first decoding. To be specific, traditional public key encryption requires data to be decrypted before it can be analyzed or manipulated. In contrast, homomorphic encryption is an encryption method that allows data to be encrypted while it is being processed and manipulated. It allows user or a third party, which can be cloud provider, to apply functions on encrypted data without revealing the data's values. In this paper, we explore existing multi-cloud-based security solutions using homomorphic encryption to identify open issues and opportunities for further enhancement.

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and loosely-coupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.

Task offloading in hybrid-decision-based multi-cloud computing network: a cooperative multi-agent deep reinforcement learning

AbstractMulti-cloud computing is becoming a promising paradigm to provide abundant computation resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the real-time computing requirements, frequently varied wireless channel gains and changeable network scale, make the system more dynamic. It is critical to satisfy the dynamic nature of network with different constraints of IoT devices in multi-cloud environment. In this paper, we establish a continuous-discrete hybrid decision offloading model, each device should learn to make coordinated actions, including cloud server selection, offloading ratio and local computation capacity. Therefore, both continuous-discrete hybrid decision and coordination among IoT devices are challenging. To this end, we first develop a probabilistic method to relax the discrete action (e.g. cloud server selection) to a continuous set. Then, by leveraging a centralized training and distributed execution strategy, we design a cooperative multi-agent deep reinforcement learning (CMADRL) based framework to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of cloud servers. Each IoT device acts as an agent, which not only learns efficient decentralized policies, but also relieves IoT devices’ computing pressure. Experimental results demonstrate that the proposed CMADRL could efficiently learn dynamic offloading polices at each IoT device, and significantly outperform the four state-of-the-art DRL based agents and two heuristic algorithms with lower system cost.

Blockchain-Based Service Orchestration for 5G Vertical Industries in Multicloud Environment

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.

Improving Health WSN with Distinguished Cloud Computing for Multi-Sensor Analytics

In the twenty-first century, automation has become more prevalent in large part due to the Internet of Things (IoT). The integration of IoT, Wireless Sensor Networks (WSNs) and health monitoring system with multi-cloud environment can optimize a lot of parameters like bandwidth, communication protocols and redundancy of the sensor network. The proposed architecture is introduced keeping in mind the current health infrastructure and monitoring system with different types of network framework. We have also discussed about machine to machine protocols for health monitoring with common entities linked with building communication protocols. The sensors data analytics carried out can sustain multi-level computation is the idea behind this research prospect to give greater depth about direct applicability in multi cloud sensor communication protocols. The offered prototype, when modelled with the unique communication system, produces a more efficient model than the standard way, with a system that is 98 percent more accurate.

Secure Communication Model for Dynamic Task Offloading in Multi-Cloud Environment

As the data is increasing day-by-day, the mobile device storage space is not sufficient to store the complete information and also the computation capacity also is a limited resource which is not sufficient for performing all the required computations. Hence, cloud computing technology is used to overcome these limitations of the mobile device. But security is the main concern in the cloud server. Hence, secure communication model for dynamic task offloading in multi-cloud environment is proposed in this paper. Cloudlet also is used in this model. Triple DES with 2 keys is used during the communication process between the mobile device and cloudlet. Triple DES with 3 keys is used by the cloudlet while offloading the data to cloud server. AES is used by the mobile device while offloading the data to the cloud server. Computation time, communication time, average running time, and energy consumed by the mobile device are the parameters which are used to evaluate the performance of the proposed system, SCM_DTO. The performance of the proposed system, SCM_DTO is compared with ECDH-SAHE and is proved to be performing better.

A Hybrid Multi-Cloud Framework Using the IBBE Key Management System for Securing Data Storage

Information storage and access in multi-cloud environments have become quite prevalent. In this paper, a multi-cloud framework is presented that secures users’ data. The primary goal of this framework is to secure users’ data from untrusted Cloud Service Providers (CSPs). They can collude with other malicious users and can hand over users’ data to these malicious users for their beneficial interests. In order to achieve this goal, the data are split into parts, and then each part is encrypted and uploaded to a different cloud. Therefore, client-side cryptography is used in this framework. For encrypting users’ data, the BDNA encryption technique is used. This framework presents a hybrid cryptographic approach that uses Identity-based Broadcast Encryption (IBBE) for managing the keys of the symmetric key algorithm (BDNA) by encrypting them with the particular version of IBBE. The work presented in this research paper is the first practical implementation of IBBE for securing encryption keys. Earlier, IBBE was only used for securely broadcasting data across many users over a network. The security of this hybrid scheme was proved through Indistinguishable Chosen-Ciphertext Attacks. This double encryption process makes the framework secure against all insiders and malicious users’ attacks. The proposed framework was implemented as a web application, and real-time storage clouds were used for storing the data. The workflow of the proposed framework is presented through screenshots of different working modules.

SCHEDULING ALGORITHMS IN MULTI CLOUD ENVIRONMENTS – A SHORT REVIEW

It is essential to schedule the workloads to cloud in an efficient manner. Whether user is using single cloud or multi cloud environments, according to the available resources and needs, the incoming jobs or tasks has to be scheduled. Hence this short review paper focuses on different available scheduling algorithms and its categories.

An Indicator-Based Algorithm for Task Scheduling in Multi-Cloud Environments

Cloud computing is the ability to scale various resources and services that can be dynamically configured by the cloud service provider (CSP) and delivered on demand by the customers. The objective of most of the task scheduling algorithms is to ensure that the overall processing time of all the tasks (i.e., makespan) is minimized. Here, minimization of makespan in no way guarantees the minimization of execution cost. In indicator-based (IBTS) task scheduling algorithm for the multi-cloud environment, we can outline the significant contributions as the following: (1) IBTS achieves multi-objective solutions while considering parameters, makespan, and execution cost. (2) IBTS proposes a normalization framework with time and cost length indicators for efficient task scheduling. (3) The efficacy of the IBTS algorithm is demonstrated using both the benchmark and synthetic datasets. (4) The simulation outcomes of the IBTS algorithm in comparison with three existing task scheduling algorithms, namely ETBTS, MOTS, and PBTS, clearly exhibit superiority, which proves acceptance of IBTS algorithm.

Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment

CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment

Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.

Correction: Towards a distributed SaaS management system in a multi-cloud environment

Correction: Towards a distributed SaaS management system in a multi-cloud environment

Towards a distributed SaaS management system in a multi-cloud environment

Towards a distributed SaaS management system in a multi-cloud environment

Energy-Aware Cloud Task Scheduling algorithm in heterogeneous multi-cloud environment

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.