Software defined neighborhood area network for smart grid applications

Information gathered from the Smart Grid (SG) devices located in end user premises provides a valuable resource that can be used to modify the behavior of SG applications. Decentralized and distributed deployment of neighborhood area network (NAN) devices makes it a challenge to manage SG efficiently. The NAN communication network architecture should be designed to aggregate and disseminate information among different SG domains. In this paper, we present a communication framework for NAN based on wireless sensor networks using the software defined networking paradigm. The data plane devices, such as smart meters, intelligent electronic devices, sensors, and switches are controlled via an optimized controller hierarchy deployed using a separate control plane. An analytical model is developed to determine the number of switches and controllers required for the NAN and the results of several test scenarios are presented. A Castalia based simulation model was used to analyze the performance of modified NAN performance

Cybersecurity in Power Grids: Challenges and Opportunities

Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identify a broad set of resulting attack vectors and attack scenarios that threaten the security of power grids. To address these challenges, we propose to rely on a defense-in-depth strategy, which encompasses measures for (i) device and application security, (ii) network security, and (iii) physical security, as well as (iv) policies, procedures, and awareness. For each of these categories, we distill and discuss a comprehensive set of state-of-the art approaches, as well as identify further opportunities to strengthen cybersecurity in interconnected power grids

Risk-based framework for SLA violation abatement from the cloud service provider's perspective

© The British Computer Society 2018. The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties

On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. 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Cheops, un service pour soufler les applications du nuage vers la périphérie

One question to answer the shift from the Cloud to the Edge computing paradigm is: how dis-tributed applications developed for Cloud platforms can benefit from the opportunities of the Edgewhile dealing with inherent constraints of wide-area network links?Our solution to this question is to give the illusion of “single service images” spreading over theEdge infrastructure. Thanks to the modularity of micro-service based applications, one can deploymultiple instances of the same service (one per edge site) and deliver collaborations between themaccording to each request. This non-invasive approach is made possible by (i) a DSL that extendsthe application API and allows DevOps to program where/how the execution of each request shouldbe executed, (ii) and its runtime, Cheops, a service that interprets and orchestrates each request inorder to satisfy the geo-distribution parameters, allowing collaborations in a transparent mannerfor the underlying application.We demonstrate the relevance of our proposal by illustrating how Cheops can successfully geo-distribute the Kubernetes vanilla code.Pour passer du paradigme de l’informatique en nuage à celui en périphérie, il faut sedemander comment les applications distribuées développées pour les plates-formes d’informatiqueen nuage peuvent-elles bénéficier de l’informatique en périphérie tout en faisant face à ses con-traintes inhérentes ?Notre solution est de donner l’illusion d’une "un service à image unique" sur l’infrastructureen périphérie. Grâce à la modularité des applications basées sur les microservices, il est possiblede déployer plusieurs instances d’un même service (une par site - en périphérie) et de fournirdes collaborations entre elles en fonction des demandes des utilisateurs. Cette approche noninvasive est rendue possible par (i) un DSL qui étend l’API de l’application et permet auxDevOps de programmer où et comment chaque demande doit être exécutée, (ii) et son serviceassocié, Cheops, qui interprète et orchestre chaque demande pour satisfaire les paramètres degéo-distribution, permettant des collaborations d’une manière transparente pour l’applicationsous-jacente.Nous démontrons la pertinence de notre proposition en illustrant comment Cheops peut géo-distribuer avec succès le code de Kubernetes sans avoir à le changer

Crowdsourced multi-view live video streaming using cloud computing

Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowdsourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch).This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 8-519-1-108.Scopu

Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines

A significant rise in the adoption of streaming applications has changed the decisionmaking processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache Storm, Spark, Heron, Samza, Flink, and others. Spark Streaming, a widespread open-source implementation, processes data-intensive applications that often require large amounts of memory. However, Spark Unified Memory Manager cannot properly manage sudden or intensive data surges and their related inmemory caching needs, resulting in performance and throughput degradation, high latency, a large number of garbage collection operations, out-of-memory issues, and data loss. This work presents a comprehensive performance evaluation of Spark Streaming backpressure to investigate the hypothesis that it could support data-intensive pipelines under specific pressure requirements. The results reveal that backpressure is suitable only for small and medium pipelines for stateless and stateful applications. Furthermore, it points out the Spark Streaming limitations that lead to in-memory-based issues for data-intensive pipelines and stateful applications. In addition, the work indicates potential solutions.N/