CellNet: A Bottom-Up Approach to Network Design

Hassan, Hoda; Eltoweissy, Mohamed; Youssef, Moustafa

doi:10.1109/ntms.2009.5384680

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…NetMem targets minimizing resource consumption at entities and enhancing scalability in data and algorithms by limiting their storage at enormous entities to perform NetMem functionalities. Maintained data (i.e., SData and semantics) at NetMem are represented uniformly, associated in big relational tables [7], and classified into three networking concerns, namely application, communication and resource concern as those concerns are defined in [6]. At the same time, data are abstracted via FBS engineering framework [20] to functional, behavioral, and structural (FBS) aspects.…”

Section: Netmem Systemmentioning

confidence: 99%

“…StM stores temporarily SData of different network elements in various domains. Data in StM are classified to application, communication, and resource concern, as defined in [6]. We propose four big relational structured tables for StM, namely service data structured table (SDST), concerns data structured table (CDST), concerns index table (CIT), and composite concerns index table (CCIT).…”

Section: Short-term Memory (Stm)mentioning

confidence: 99%

“…Unfortunately, the current Internet [2] and other proposed network architectures in the contemporary literature, for example [3] [6], in the most part, do not provide effective and efficient methodology for networking entities to discover, learn, store and utilize patterns of traffic behavior, particularly at runtime in an automated manner and over a long period of time. For example, the behavior of TCP, a reliable communication protocol, can be extracted by learning patterns of that protocol.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biologically-inspired Network “Memory” for Smarter Networking

Mokhtar

Eltoweissy

2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Emerging technologies such as the Internet of Things generate huge amounts of network traffic and data which lead to significant challenges in a) ensuring availability of resources ondemand, b) recognizing emergent and abnormal behavior, and c) making effective decisions for efficient network operations. Network traffic data exhibit spatiotemporal patterns. Learning and maintaining the currently elusive rich semantics based on analyzing such patterns would help in mitigating those challenges. In this paper, we propose the concept of a network "memory" (or NetMem) to support smarter data-driven network operations as a foundational component of next generation networks. NetMem will enable networking objects to understand autonomously, at real-time, on-demand, and at low cost semantics with different levels of granularity and related to various network elements. Guided by the fact that human activities exhibit spatiotemporal data patterns; and the human memory extracts and maintains semantics to enable accordingly learning and predicting new things, we design NetMem to mimic functionalities of that memory. NetMem provides capabilities for semantics management through uniquely integrating data virtualization for homogenizing massive data originating from heterogeneous sources, cloud-like scalable storage, associative rule learning to recognize data patterns, and hidden Markov models for reasoning and extracting semantics clarifying normal/abnormal behavior. NetMem provides associative access to data patterns and relevant derived semantics to enable enhancements in early anomaly detection, more accurate behavior prediction and satisfying QoS requirements with better utilization of resources. We evaluate NetMem using simulation. Preliminary results demonstrate the positive impact of NetMem on various network management operations.

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Section: Netmem Systemmentioning

confidence: 99%

Section: Short-term Memory (Stm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Biologically-inspired Network “Memory” for Smarter Networking

Mokhtar

Eltoweissy

2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Self Cite

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“…Since inception, the Internet has been founded on a layered architecture employing the end-toend principle and store and forward mechanisms to provide best effort service internetworking intelligent end-nodes through a dump core of nodes. Recently, many evolutionary cross-layer networking enhancements and clean-slate architectures have been proposed, for example [2][3][4][5]. These attempts have presented new design principles for architecting the next generation networks, proposed new basic building blocks for the architecture, and advocated separating the control plane from the data plane.…”

Section: Imentioning

confidence: 99%

Memory-enabled Autonomic Resilient Networking

Mokhtar¹,

Eltoweissy²

2011

Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

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It is becoming increasingly challenging for the contemporary Internet to satisfy the explosion in networking services and applications with their widely diverse and dynamic QoS requirements and resource demands. For smarter networking management and operations, we hypothesize that next generation networks should be endowed with "memory"-like (i.e., functionally similar to human memory) capabilities and should explicitly manage their resources and services. In this paper, we introduce SmartNet, a new memory-enabled autonomic network architecture that is scalable, resilient and adaptable at runtime. SmartNet's memory, we term NetMem, manages data related to multiple networking concerns, namely application, communication, and resource concerns. SmartNet's NetMem maintains "knowledge"-the fourth networking concern in our networking model-in a distributed knowledge plane that can be rapidly accessed in a simple and systematic way by SmartCells (SmartNet's basic building block) and the services they implement. Each SmartCell has intrinsic monitoring, control and analysis capabilities and can acquire resources on demand. For scalability and to facilitate inter-communications and services' support between SmartCells, regions, termed CellDomains, may be dynamically formed. CellDomains would incorporate favored services or resources. Our simulation results demonstrate an improvement in networking operations with our memory-enabled network management.

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“…Unfortunately, the current Internet [6] and other proposed network architectures in the contemporary literature, for example [7,8], in the most part, do not provide effective and efficient methodology for networking entities to discover, learn, store and utilize patterns of traffic behavior, particularly at runtime in an automated manner and over a long period of time. For example, the behavior of TCP, a reliable communication protocol, can be extracted by learning patterns of that protocol.…”

Section: Introductionmentioning

confidence: 99%

Network “memory” system for enhanced network services

Mokhtar

Eltoweissy

El-Sayed

2013

2013 9th International Conference on Innovations in Information Technology (IIT)

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The advent of the Internet of things will bring to bear an explosion in the number of interconnected heterogeneous objects as well as the diverse resources and services they may offer. A fundamental goal is to ensure the availability of resources and services to communicating objects ubiquitously, resiliently, on-demand and at low cost while satisfying users' QoS requirements. We hypothesize that to achieve this goal; there is a need to build capabilities for smarter networking to harvest the currently elusive rich semantics that emerge in interactions. In this paper, we propose the concept and primary architecture of a network "memory" (or NetMem) to support smarter data-driven network operations as a foundational component of next generation networks. Guided by the fact that networking activities exhibit spatiotemporal data patterns, we design NetMem to mimic functionalities of the human memory. NetMem provides capabilities for semantics management through integrating data virtualization, cloud-like scalable storage, associative rule learning and predictive analytics. NetMem provides associative access to data patterns and relevant derived semantics to enable enhancements in decision making, QoS guarantees and utilization of resources, early anomaly detection, and more accurate behavior prediction. We evaluate NetMem using simulation. Preliminary results demonstrate the positive impact of NetMem on various network management operations.

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CellNet: A Bottom-Up Approach to Network Design

Cited by 8 publications

References 7 publications

Biologically-inspired Network “Memory” for Smarter Networking

Biologically-inspired Network “Memory” for Smarter Networking

Memory-enabled Autonomic Resilient Networking

Network “memory” system for enhanced network services

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CellNet: A Bottom-Up Approach to Network Design

Cited by 8 publications

References 7 publications

Biologically-inspired Network “Memory” for Smarter Networking

Biologically-inspired Network “Memory” for Smarter Networking

Memory-enabled Autonomic Resilient Networking

Network &#x201C;memory&#x201D; system for enhanced network services

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Network “memory” system for enhanced network services