Addressing Application Latency Requirements through Edge Scheduling

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications

Improving service quality in cloud computing : from definition to deployment

Service quality is crucial in all stages of the Cloud service life cycle, from service acquisition, where Cloud consumers and providers negotiate for a mutual agreement, to service execution, where service management is driven by the agreed requirements. Much work has been devoted to specification and enforcement of service quality terms in the Cluster, Grid and Cloud domains. However, the dynamism present in Cloud services is ignored. We propose a theoretical and practical framework which addresses the first phases of the service life cycle: (i) the definition of service provision; (ii) the negotiation of offers/requests expressed and (iii) the service deployment, mainly focused on latency-sensitive applications. We introduce SLAC, a specification language for the definition of service requirements, the so-called service level agreements (SLAs), which allows us to define conditions and actions that can automatically modify those terms at runtime. Experimental results show that the use of SLAC can drastically reduce the service violations and penalties to the advantages of providers and consumers. Then, we define a novel matchmaking and negotiation framework, which evaluates the compatibility of SLAC requests/offers, and provides the modifications necessary to reach an agreement. Experiments demonstrate the effectiveness of our proposal. We also introduce a new scheduling algorithm for latency-sensitive services, in a Cloud/Edge computing scenario, which takes into account not only the service requirements but also network latency, bandwidth and computing capabilities. Again, experimental results confirm the advantages of this new approach over existing solutions

Learning efficiently in semantic based regularization

Semantic Based Regularization (SBR) is a general framework to integrate semi-supervised learning with the application specific background knowledge, which is assumed to be expressed as a collection of first-order logic (FOL) clauses. While SBR has been proved to be a useful tool in many applications, the underlying learning task often requires to solve an optimization problem that has been empirically observed to be challenging. Heuristics and experience to achieve good results are therefore the key to success in the application of SBR. The main contribution of this paper is to study why and when training in SBR is easy. In particular, this paper shows that exists a large class of prior knowledge that can be expressed as convex constraints, which can be exploited during training in a very efficient and effective way. This class of constraints provides a natural way to break the complexity of learning by building a training plan that uses the convex constraints as an effective initialization step for the final full optimization problem. Whereas previous published results on SBR have employed Kernel Machines to approximate the underlying unknown predicates, this paper employs Neural Networks for the first time, showing the flexibility of the framework. The experimental results show the effectiveness of the training plan on categorization of real world images

Smart Contract Negotiation in Cloud Computing

A smart contract is the formalisation of an agreement, whose terms are automatically enforced by relying on a transaction protocol, while minimising the need of intermediaries. Such contracts not only specify the service and its quality but also the possible changes at runtime of the terms of agreement. Although smart contracts provide a great deal of flexibility, analysing their compatibility and reaching agreements with this level of dynamism is considerably more challenging, due to the freedom of clients and providers in formulating needs/offers. We introduce a formal language to specify interactions between offers and requests and present a methodology for the autonomous negotiation of smart contracts, which analyses the cost and the necessary changes for reaching an agreement. Moreover, we describe a set of experiments that provides insights on the relative cost of dynamism in negotiating smart contracts and compare the request/offer matching rates of our solution with related works

Defining and guaranteeing dynamic service levels in clouds

In this paper, we introduce SLAC, a SLA definition language specifically devised for clouds as a formalism to support the whole SLA lifecycle. The main novelty of the language is the possibility of capturing within the SLA the dynamic aspects of the environment by defining the conditions and actions to change service levels at runtime. SLAC permits to make the most of cloud elasticity, reduces the need for renegotiation and provides guarantees for dynamic scenarios. The language has formal syntax and semantics, and it comes with effective software tools supporting the whole SLA management lifecycle. The impact of our language and of its software tools is assessed by considering a series of experiments that provide empirical evidences of the advantages of SLAC