Towards Trustworthy Multi-Cloud Services Communities: A Trust-Based Hedonic Coalitional Game

Self Cite

Communities that gather functionally identical or complementary cloud services aim to provide better visibility, efficiency, and market share. This paper investigates the issue of forming these communities in distributed decision-making settings under incomplete information. By incomplete information, we mean only partial information about the individual performance of cloud services within communities and about how they will behave within these communities is available. Forming communities in these particular settings is still an open problem. Most of the existing models require real-time global knowledge about the services and high computational complexity, which makes the community formation extremely hard and time-consuming. In this paper, we propose a strategic Distributed Decision-making Mechanism (DDM) that regulates the cloud services decision-making process. DDM first generates an initial set of data based on information obtained from existing cloud services regarding their single and cooperative efficiency. By analyzing this set and on the basis of a distance function, the decision-making mechanism with regard to which community to form is implemented as a decision profile of strategies and their expected utility computed in terms of computational efficiency. DDM efficiently and systematically helps 1) communities find appropriate cloud services to invite as new members and 2) single services find suitable communities to join. To evaluate the proposed mechanism, we performed experiments using real data including 142 users and 4,000 cloud services obtained from the CloudArmor, CloudHarmony, and WS-DREAM datasets. The experimental results show that our algorithms outperform the existing solutions. KEYWORDScloud services, community of services, distributed decision-making INTRODUCTIONModern applications are nowadays developed using service-oriented architecture and web services technology where the cloud's software as a service (SaaS) is de facto model for software delivery. To make these applications more efficient and adaptive to the new requirements of being flexible, cloud services including SaaS, platform as a service (PaaS), infrastructure as a service (IaaS), and backend as a service (BaaS) are shifted, from the perspective of design and engineering, from passive and individual components to intelligent, autonomous, and collaborative-based components having common goals. 1-4 Being collaborative entails new challenges towards analyzing the overall system's performance in achieving these common goals. 5-8 One of the initiatives towards strengthening this collaboration among services is capitalizing on the concept of virtual communities. 9-11 A virtual community of services is defined as a group of services offering similar and complementary functionalities with diverse non-functional properties that can ensure the inter-community and intra-community interactions.Despite the existence of the current services technologies (e.g., WSDL and SOAP) and cloud developments that enable developing virtual commun...

Section: Motivationsmentioning

confidence: 99%

Section: Architecture Of Cloud Service Communitiesmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

Section: Cloud Servicesmentioning

confidence: 99%

Section: Architecture Of Cloud Service Communitiesmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic formation of service communities in the cloud under distribution and incomplete information settings

Khosrowshahi-Asl¹,

Estrada

Otrok

et al. 2017

Self Cite

Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment

Anandaraj¹,

Indumathi²

2019

Summary For the purpose of obtaining an answer to a spoofing IP issue and related problems in the cloud computing (CC) environment, a novel path identifier is recommended, in which a new packet approach with a new identifier (ID) is produced to every user or client system. For the purpose of identification of the spoofing attack, any watermark image pattern is introduced. Following this, the hypervisor is employed to ascertain plausible trust relationships among the VMs by considering personal sources that can be trusted along with utilizing empirical Bayesian inference (EBI) for the purpose of combining the VMs. With a restricted budget of resources for the maximized detection of attacks, an optimal detection load distribution strategy by improved cuckoo search (ICS) over VMs can be used, which thus provides the intended solution to the hypervisor. The ICS algorithm directs a hypervisor so as to assert among the VMs the optimal load distribution, taking into consideration real time, which causes maximization of the DDoS attacks and detection of transmission control protocol (TCP) flood attacks. Lastly, a fuzzy extreme learning machine (FELM) classifier is proposed in order to detect the attacks.

A trust‐based hypervisor framework for preventing DDoS attacks in cloud

Vetha

Devi

2019

Summary Distributed Denial of Service (DDoS) attack is one of the major attacks that incur large financial loss in the cloud system. This motivated the research community to develop various detection techniques for controlling the effects of the DDoS attack. However, the existing techniques are not mature to satisfy the requirements of a cloud‐based attack detection system, as they manage the devious strategies that exploit the elastic and multi‐tenant properties of the cloud and ignore the resource constraints of the cloud system. This paper proposes a new solution that allows the hypervisor to establish trust‐based relationships towards the guest Virtual Machines (VMs). The Bayesian inference is applied to aggregate the objective and subjective trust sources. A trust‐based maximin game between DDoS attackers is designed. A hypervisor tries to maximize the attack minimization under a limited amount of resources. The game solution guides the hypervisor to determine the distribution of optimal detection load among VMs to improve the real‐time detection rate of DDoS attack. The Least Squares Support Vector Machine (LS‐SVM) classification is applied to classify the normal VMs and malicious VMs. The file is allocated to the VM based on the storage capacity of the VM. The experimental result shows that the proposed approach achieves high DDoS attack detection rate with minimum false positive and negative rate, when compared to the existing attack detection models.