101,958 research outputs found
SLA based cloud service composition using genetic algorithm
Cloud computing tends to provide high quality on-demand services to the users. Numerous services are evolving today. Functionally similar services are having different non-functional properties such as reliability, availability, accessibility, response time and cost. A single service is inadequate for constructing the business process. Such business process is modeled as composite service. Composite service consists of several atomic services connected by workflow patterns. Selecting services for service composition with the constraints specified in Service Level Agreement is the NP-hard problem. Such a cloud service composition problem is modeled in this paper. Genetic based cloud service composition algorithm (GCSC) is proposed. Proposed algorithm is compared with the existing genetic based cloud service composition algorithm based on average utility rate and convergence time. It is proved that the proposed algorithm provides better performance as compared to the existing cloud service composition algorithm
ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources
Edge and fog computing have grown popular as IoT deployments become
wide-spread. While application composition and scheduling on such resources are
being explored, there exists a gap in a distributed data storage service on the
edge and fog layer, instead depending solely on the cloud for data persistence.
Such a service should reliably store and manage data on fog and edge devices,
even in the presence of failures, and offer transparent discovery and access to
data for use by edge computing applications. Here, we present Elfstore, a
first-of-its-kind edge-local federated store for streams of data blocks. It
uses reliable fog devices as a super-peer overlay to monitor the edge
resources, offers federated metadata indexing using Bloom filters, locates data
within 2-hops, and maintains approximate global statistics about the
reliability and storage capacity of edges. Edges host the actual data blocks,
and we use a unique differential replication scheme to select edges on which to
replicate blocks, to guarantee a minimum reliability and to balance storage
utilization. Our experiments on two IoT virtual deployments with 20 and 272
devices show that ElfStore has low overheads, is bound only by the network
bandwidth, has scalable performance, and offers tunable resilience.Comment: 24 pages, 14 figures, To appear in IEEE International Conference on
Web Services (ICWS), Milan, Italy, 201
Ultra-Reliable Cloud Mobile Computing with Service Composition and Superposition Coding
An emerging requirement for 5G systems is the ability to provide wireless
ultra-reliable communication (URC) services with close-to-full availability for
cloud-based applications. Among such applications, a prominent role is expected
to be played by mobile cloud computing (MCC), that is, by the offloading of
computationally intensive tasks from mobile devices to the cloud. MCC allows
battery-limited devices to run sophisticated applications, such as for gaming
or for the "tactile" internet. This paper proposes to apply the framework of
reliable service composition to the problem of optimal task offloading in MCC
over fading channels, with the aim of providing layered, or composable,
services at differentiated reliability levels. Inter-layer optimization
problems, encompassing offloading decisions and communication resources, are
formulated and addressed by means of successive convex approximation methods.
The numerical results demonstrate the energy savings that can be obtained by a
joint allocation of computing and communication resources, as well as the
advantages of layered coding at the physical layer and the impact of channel
conditions on the offloading decisions.Comment: 8 pages, 5 figures, To be presented at CISS 201
Providing secure and reliable communication for next generation networks in smart cities
© 2020 Elsevier Ltd Finding a framework that provides continuous, reliable, secure and sustainable diversified smart city services proves to be challenging in today\u27s traditional cloud centralized solutions. This article envisions a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand. The solution assumes that collaboration is determined based on nodes’ resource capabilities and cooperation willingness. Resource capabilities are defined using ontologies, while willingness to cooperate is described using a three-factor node criteria, namely: nature, attitude and awareness. A learning method is adopted to identify candidates for the service composition and delivery process. We show that the system does not require extensive training for services to be delivered correct and accurate. The proposed solution reduces the amount of unnecessary traffic flow to and from the edge, by relying on node-to-node communication protocols. Communication to the fog and cloud layers is used for more data and computing-extensive applications, hence, ensuring secure communication protocols to the cloud. Preliminary simulations are conducted to showcase the effectiveness of adapting the proposed framework to achieve smart city sustainability through service reliability and security. Results show that the proposed solution outperforms other semi-cooperative and non-cooperative service composition techniques in terms of efficient service delivery and composition delay, service hit ratio, and suspicious node identification
Context-oriented and transaction-based service provisioning
This paper presents our approach for service provisioning in pervasive computing environments. The presented approach is based on the usage of context-aware features and transactions during the discovery and the deployment of composite services. Context ensures that the best service offers are selected to participate in a service composition. Transactions help in improving the reliability and efficiency of the composite services
A Reputation-Based Approach to Self-Adaptive Service Selection
Service-orientation provides concepts and tools for flexible composition and management of largescale distributed software applications. The automated run-time management of such loosely coupled software systems, however, poses still major challenges and is therefore an active research area, including the use of novel computing paradigms. In this context, the dynamic and adaptive selection of best possible service providers is an important task, which can be addressed by an appropriate middleware layer that allows considering different service quality aspects when managing the adaptive execution of distributed service workflows dynamically. In such an approach, service consumers are enabled to delegate the adaptive selection of service providers at run-time to the execution infrastructure. The selection criteria used are based on the cost of a service provision and the continuous, dynamic evaluation of reputations of providers, i.e. maintained track records of meeting the respective service commitments. This paper discusses the design and operating principle of such an automatic service selection middleware extension. Its ability to balance different quality criteria for service selection, such as service cost vs. the reliability of provision, is empirically evaluated based on a multi-agent platform approach
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