Towards a framework for monitoring cloud application platforms as sensor networks

Abstract

With the continued growth in software environments on cloud application platforms, self-management at the Platform-as-a-Service (PaaS) level has become a pressing concern, and the run-time monitoring, analysis and detection of critical situations are all fundamental requirements if we are to achieve autonomic behaviour in complex PaaS environments. In this paper we focus on cloud application platforms offering their customers a range of generic built-in re-usable services. By identifying key characteristics of these complex dynamic systems, we compare cloud application platforms to distributed sensor networks, and investigate the viability of exploiting these similarities with a case study. We treat cloud data storage services as "virtual" sensors constantly emitting monitoring data, such as numbers of connections and storage space availability, which are then analysed by the central component of a monitoring framework so as to detect and react to SLA violations. We discuss the potential benefits, as well as some shortcomings, of adopting this approach.

Formalizing and simulating cross-layer elasticity strategies in Cloud systems

“…Authors of [13] proposed a framework to perform Cloud systems' elastic behaviors and to monitor these behaviors at runtime. In their approach, authors interpreted service-based Cloud application platforms as sensor networks in order to apply sensor web techniques for PaaS-level autonomic management.…”

“…Similarly to [20], we propose an autonomic MAPE-based control loop to simulate the runtime of a Cloud system according to the designed elasticity strategies. Regarding the monitoring methodoloy, our solution reaches the philosophy of [13] in their search for identifying virtual sensors to gather information about the system state. Precisely, our proposed modeling approach enables representing a Cloud system cross-layer configuration as a forest of overlapping nodes which enables isolating sets of services according to their host VMs, for example.…”

Energy consumption monitoring of Smart grid based on semantic stream data analysis

“…Для представления показаний интеллектуальных счетчиков и сообщений о нештатных или аварий-ных ситуациях используются несколько верхнеуровневых и предметных онтологий:  верхнеуровневая онтология сети семантических сенсоров SSN (Semantic Sensor Networks Ontology) 1 ;  предметная онтология электрических счетчиков EM (ElectricMeter Ontology) 2 , являющаяся расшире-нием SSN;  верхнеуровневая онтология событий DUL (DOLCE+DnS Ultralite ontology) 3 . На рис.…”

“…Ранее данный подход был использован в таких областях, как управ-ление инфраструктурой облачных вычислений [2], анализ социальных сетей [3,4], мониторинг состояния окружающей среды [5][6][7], мониторинг движения транспорта [8], обнаружение событий в контексте умно-го города [9,10] и др.…”

QoS guarantee protocol based on combination of opportunistic dynamic cloud service and cooperative multimedia stream for wireless sensor networks

“…Misra and Chatterjee [22] studied the fundamental issues in context aggregation of health data within the mobile monitoring nodes and researched the channelization of the aggregated data by dynamic selection of the cloud gateways. By identifying key characteristics of the dynamic systems, Dautov et al [23] studied the cloud application platforms to distributed sensor networks and analyzed the viability of exploiting the similarities.…”