Collaborative Cloud Computing Research Articles

An Efficient Scheduling Strategy for Collaborative Cloud and Edge Computing in System of Intelligent Buildings

Edge computing is a new computing method, and task scheduling is challenging work. Using edge computing in intelligent buildings for managing smart home devices has gained popularity because it can reduce the delay and network congestion brought by cloud computing. Edge computing has the advantage of fast response speeds, but its computing capacity is limited. To solve this practical problem, a system framework of collaborative cloud and edge computing is constructed for intelligent buildings. First, the communication time, task completion time, and CPU energy consumption are considered comprehensively, and a mathematical model of the system is developed. Considering the compute-intensity task, the splitting ratio is determined for tasks to achieve the collaboration of cloud computing and edge computing. Then, the search mechanism of a single gene mutation in the genetic algorithm (GA) is introduced to compensate for the defects of the salp swarm algorithm (SSA), while focusing on the search ability and optimization efficiency. Finally, the proposed strategy is theoretically analyzed and experimentally evaluated. The simulation results show that the hybrid algorithm of SSA-GA has better performance than other algorithms, and the proposed collaborative cloud and edge computing task scheduling strategy demonstrated a lower delay and makespan.

A Review on Trust-Based Resource Allocation in Cloud Environment: Issues Toward Collaborative Cloud

Due to the exponential rise in the usage of the internet and smart devices, there is a demand for enhanced network efficiency and user satisfaction in a cloud computing environment. Moreover, moving to the cloud systems, it mainly focuses on storage, computation and resources. Due to copious growth, there exist more challenges as well. Among those, resource allocation in cloud computing is the main study, which is essential to determine the QoS and improved performance concerning reliability, confidentiality, trust, security, user satisfaction, profits, etc. This paper plans to prepare a detailed review on trust-based resource allocation in the collaborative cloud. The cloud industry has been assessed in terms of trust-based and other important factors to produce a road plan for resource allocation. Many papers are reviewed here and give a substantial evaluation of cloud resources and their resource allocation models using machine learning and optimization models. First, this survey provides an elaborated study concerning the various cloud resources considering the performance and QoS. Eventually, it extends the research based on trust-based approaches, with the intention of motivating the researchers to focus on trust-based resource allocation on collaborative cloud computing (CCC) atmosphere.

Impact of I-GWO towards Trust based Resource Allocation in Collaborative Cloud

The cloud computing is arising as a popular computing paradigm, as it is good in offering its users an on-demand scalable resource based services over the internet. In the peak hours, a single cloud is not at all efficient in serving an application; therefore the collaborative cloud model has been introduced. The collaborative cloud computing (CCC) make use of the globally-scattered distributed cloud resources of the diverse organizations collectively in a co-operative manner to provide the required service to the user. The allocation as well as the management of the resources is being a challenging task in the CCC, due to the heterogeneity of the resources. On the other hand, the assurance of the Quality of Service (QoS) and reliability of these resources is challenging. Further, it would be efficient if the resources are provided based on the system behavior. In this research work, a novel trust computing model is developed, which predicts both the QoS and Trust via analyzing the system behavior. The proposed model encloses three major phases: trust- QoS behavior estimation, resource matching and resource allocation. Initially, the QoS as well as Trust behavior of the system is estimated via a Neural Network (NN) model. Subsequently, the resource allocation is performed using the parallel resource matching framework, which is based on the concept of Map-Reduce. More particularly, the precious resource allocation is achieved by an optimization logic called Improved Grey Wolf Optimizer (IGWO). Here, the improvement of GWO emphasis the consideration of both the best and worst fitness.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

An automated resource management framework for minimizing SLA violations and negotiation in collaborative cloud

Collaborative Cloud computing is an emergent and encouraging computational prototype for sharing on-demand cloud resources in a multi-cloud environment. In a collaborative cloud environment, we make use of unused computational resources within an interconnected network to provide seamless service to the customers. SLA (Service Level Agreement) means a legal agreement signed among the consumer and the cloud service provider to guarantee a better quality of service. Nowadays due to more user request, suppliers of cloud service are not able to deliver guaranteed Quality of Service (QoS) to the customers leading to SLA violation. Existing SLA- based resource allocation models in today's cloud market consider an agreement for each QoS parameters independently and follow fixed pricing models, which results in lacking an end-to-end approach for the client task that requires composing multiple services and inefficient utilization of computing resources, which has direct negative effect on performance and cost. In this paper, we have designed a framework model for the effective dynamic provision of resources considering various categories of SLAs laterally with their penalty. We used Hybrid Cuckoo Search algorithm for scheduling of user requests in collaborative cloud computing environment. The proposed framework services the customer requests from its interconnected cloud servers and has an SLA Negotiation policy to minimize the penalty cost and maximize customer satisfaction by reducing SLA violations.

The Possibility of Resource Scheduling for a Collaborative Cloud Computing Using Failure Workflow Control in IAAS Cloud

The Possibility of Resource Scheduling for a Collaborative Cloud Computing Using Failure Workflow Control in IAAS Cloud

Effects of Cloud Computing Tools, Study Type and Task Difficulty on Cognitive Load and Performance

The purpose of this research is to investigate the effects of using cloud computing technologies, study type and task difficulty on cognitive load and students’ performance. The research was conducted as 2x2x2 complex mixed design. The two experiment groups are the first factor of design. In the first experiment group students used non collaborative cognitive tools (NCCT), in the second group students used collaborative cloud computing (CCCT) for learning tasks. Second and third factors of the research as repeated measures are study type (individual – group) and task difficulty (easy – difficult). During the experiment process four different types of tasks were given to students. These tasks were individual and easy task (1), individual and difficult task (2), in group and easy task (3) and in group and difficult task (4). The depended variables of the research are cognitive load, performance and instructional efficiency scores. The experiment group consists of 57 females and 57 males totally 114 university students. 55 students used NCCT and 59 students used CCCT for learning tasks. Three factors mixed design ANOVA were used to analyze the gathered data. Analyzes showed that cloud computing improves learning performance and instructional efficiency while does not affect cognitive load. According to the results especially for the difficult tasks using cloud computing is suggested to improve students’ collaboration and performance.

Collaborative Cloud and Edge Computing for Latency Minimization

By performing data processing at the network edge, mobile edge computing can effectively overcome the deficiencies of network congestion and long latency in cloud computing systems. To improve edge cloud efficiency with limited communication and computation capacities, we investigate the collaboration between cloud computing and edge computing, where the tasks of mobile devices can be partially processed at the edge node and at the cloud server. First, a joint communication and computation resource allocation problem is formulated to minimize the weighted-sum latency of all mobile devices. Then, the closed-form optimal task splitting strategy is derived as a function of the normalized backhaul communication capacity and the normalized cloud computation capacity. Some interesting and useful insights for the optimal task splitting strategy are also highlighted by analyzing four special scenarios. Based on this, we further transform the original joint communication and computation resource allocation problem into an equivalent convex optimization problem and obtain the closed-form computation resource allocation strategy by leveraging the convex optimization theory. Moreover, a necessary condition is also developed to judge whether a task should be processed at the corresponding edge node only, without offloading to the cloud server. Finally, simulation results confirm our theoretical analysis and demonstrate that the proposed collaborative cloud and edge computing scheme can evidently achieve a better delay performance than the conventional schemes.

An Integrated Resource and Reputation Management Platform for Collaborative Cloud Computing

An Integrated Resource and Reputation Management Platform for Collaborative Cloud Computing

Resource Selection in Collaborative CloudComputing Based on User Preference

In many researches they have created one environment with multiple clouds for scalable capabilities. The demand for scalable resources in some applications has been rapidly increased. By understanding the mutual dependencies between resources, utilization of users, user preferences a collaborative cloud computing has been introduced, It can be achieve emphasized efficient management of resources and user satisfaction between distributed resources in Collaborative cloud computing. These which do collaboratively supported by many organizations (Google, Microsoft, Amazon). Collaborative cloud computing (CCC) is next logical step in evolution of cloud and service grid technologies. Here mainly discussing about how resource selection done in collaborative cloud, resource selection based on user preferences, which mostly user preferred resources. This environment is to point out the suitable resources, ability and capability of resources, user requirements, and availability of resources. So that users can choose credible cloud platform.

Combining Efficiency, Fidelity, and Flexibility in Resource Information Services

A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g., computing, storage, and data) distributed over the Internet. A resource information service, which collects resource data and provides resource search functionality for locating desired resources, is a crucial component of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service quality. Previously, a number of resource information service systems have been proposed based on Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined list of attributes. This paper presents a resource information service that offers high efficiency and fidelity without restricting resource expressiveness, while also providing a similar-matching service. Extensive simulation and PlanetLab experimental results show that the proposed service outperforms other services in terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant enhancements in efficiency and fidelity.

Litrature Survey on Collaborative Cloud ComputingFor Sharing Resource in Trustworthy Manner

Collaborative Cloud Computing (CCC) is the very new technique which makes the promising and secured way of transmitting the data over distributed cloud servers; the main objective for developing CCC is to create collected and cooperative development over cloud service providers Collaborative Cloud Computing method uses two different types of mechanism for activating the cloud server they are resource management and reputation management technique therefore we can avoid over node of traffic congestion hence we can generate high quality of double bandwidth over node. In the previous research work a single value will be added to the reputation management so that it can represent every node with the unique ID, even though the value has been assigned to the every node the server could not able to identify resources to individual types of user’s therefore QoS (Quality of Service) could not able to meet by the resources selection allocator. In order to find the solution for the problem we proposed new a method called Harmony in the Collaborative Cloud Computing platform. Harmony method used to access the resource and reputation technique with help of three different types of key value search method they are as follows, integrated multi – faceted resource/reputation management, multi – QoS oriented resource selection process and price – assisted resource/reputation control technique. Data has been traced out from different online trading platforms like EBay, Amazon, etc in order to implies major drawbacks in multi – faceted reputation and higher reputed selection of nodes in the cloud servers hence trace driven and simulation method is implemented and executed in the plant lab for getting trustworthy of sharing the resource management and reputation management in the Quality of Service (QoS). In this survey paper we compared various research parameters for Collaborative Cloud Computing (CCC).

An Efficient and Trustworthy Resource Sharing Platform for Collaborative Cloud Computing

Advancements in cloud computing are leading to a promising future for collaborative cloud computing (CCC), where globally-scattered distributed cloud resources belonging to different organizations or individuals (i.e., entities) are collectively used in a cooperative manner to provide services. Due to the autonomous features of entities in CCC, the issues of resource management and reputation management must be jointly addressed in order to ensure the successful deployment of CCC. However, these two issues have typically been addressed separately in previous research efforts, and simply combining the two systems generates double overhead. Also, previous resource and reputation management methods are not sufficiently efficient or effective. By providing a single reputation value for each node, the methods cannot reflect the reputation of a node in providing individual types of resources. By always selecting the highest-reputed nodes, the methods fail to exploit node reputation in resource selection to fully and fairly utilize resources in the system and to meet users' diverse QoS demands. We propose a CCC platform, called Harmony, which integrates resource management and reputation management in a harmonious manner. Harmony incorporates three key innovations: integrated multi-faceted resource/reputation management, multi-QoS-oriented resource selection, and price-assisted resource/reputation control. The trace data we collected from an online trading platform implies the importance of multi-faceted reputation and the drawbacks of highest-reputed node selection. Simulations and trace-driven experiments on the real-world PlanetLab testbed show that Harmony outperforms existing resource management and reputation management systems in terms of QoS, efficiency and effectiveness.

Design of SOA Based Framework for Collaborative Cloud Computing in Wireless Sensor Networks

WSN deployments are growing at a fast rate; however, current WSN architectures and setup do not promote the sharing of data on an inter-WSN basis. Cloud computing has emerged as a promising area to deal with participatory and collaborative data and services, and is envisaged that collaborative cloud computing WSN could be a viable solution for sharing data and services for WSN applications. In this paper, SOA based architecture has been proposed to support collaborating cloud computing in WSN. The architecture consists of layered service stack that has management, information, presentation and communication layers with all required services and repositories. Interactions between WSN, subscribers and other cloud are also presented as sequence diagrams. The proposed framework serves the cloud subscribers with wide range of queries on the data of multiple WSNs through suitable interface to solve large scale problems.