Alexander Clemm

Integrated and autonomic cloud resource scaling

A Cloud is a very dynamic environment where resources offered by a Cloud Service Provider (CSP), out of one or more Cloud Data Centers (DCs) are acquired or released (by an enterprise (tenant) on-demand and at any scale. Typically a tenant will use Cloud service interfaces to acquire or release resources directly. This process can be automated by a CSP by providing auto-scaling capability where a tenant sets policies indicating under what condition resources should be auto-scaled. This is specially needed in a Cloud environment because of the huge scale at which a Cloud operates. Typical solutions are naïve causing spurious auto-scaling decisions. For example, they are based on only thresholding triggers and the thresholding mechanisms themselves are not Cloud-ready. In a Cloud, resources from three separate domains, compute, storage and network, are acquired or released on-demand. But in typical solutions resources from these three domains are not auto-scaled in an integrated fashion. Integrated auto-scaling prevents further spurious scaling and reduces the number of auto-scaling systems to be supported in a Cloud management system. In addition, network resources typically are not auto-scaled. In this paper we describe a Cloud resource auto-scaling system that addresses and overcomes above limitations.

Robust Monitoring of Network-wide Aggregates through Gossiping

We investigate the use of gossip protocols for continuous monitoring of network-wide aggregates under crash failures. Aggregates are computed from local management variables using functions such as SUM, MAX, or AVERAGE. For this type of aggregation, crash failures offer a particular challenge due to the problem of mass loss, namely, how to correctly account for contributions from nodes that have failed. In this paper we give a partial solution. We present G-GAP, a gossip protocol for continuous monitoring of aggregates, which is robust against failures that are discontiguous in the sense that neighboring nodes do not fail within a short period of each other. We give formal proofs of correctness and convergence, and we evaluate the protocol through simulation using real traces. The simulation results suggest that the design goals for this protocol have been met. For instance, the tradeoff between estimation accuracy and protocol overhead can be controlled, and a high estimation accuracy (below some 5% error in our measurements) is achieved by the protocol, even for large networks and frequent node failures. Further, we perform a comparative assessment of GGAP against a tree-based aggregation protocol using simulation. Surprisingly, we find that the tree-based aggregation protocol consistently outperforms the gossip protocol for comparative overhead, both in terms of accuracy and robustness.

Policy-enabled mechanisms for feature interactions: reality, expectations, challenges

This paper is based on the discussion during a panel that took place at the 7th Workshop on Feature Interactions in Telecommunications and Software Systems in Ottawa, Canada, June 2003. It presents a holistic picture on two paradigms, namely feature and policy, and their intertwining. The guest panelists brought examples from complementary areas and presented their experiences on using the concept of policy for defining features and for treating the feature interaction problem. The intrinsic interactions commonly called policy conflicts within policy-based systems were also discussed. The panelists considered methodological issues, such as the use of deontic logic and the representation of features through policies, as well as industrial applications, such as service provisioning and policy-based management applications for service bundling. They also brought out different views that reflect some disparity between the communities involved in this research.

Proceedings of the 9th IFIP/IEEE International Symposium on Integrated Network Management

The Ninth IFIP/IEEE International Symposium on Integrated Network Management (IM 2005) will be held 16-19 May 2005 in Nice, France. IM 2005 will present the latest technical advances in the area of management, operations and control of networks, networking services, networked applications, and distributed systems. Held in odd-numbered years since 1989 and taking turns with its sibling conference NOMS, IM 2005 will build on the successes of its predecessors and serve as the primary forum for technical exchange among the research, standards, vendor and user communities in the network management field. The symposium is sponsored by the International Federation for Information Processing (IFIP) Working Group 6.6 on Management of Networks and Distributed Systems, and by the IEEE Communications Society Technical Committee on Network Operations and Management (CNOM).

Profile-based subscriber service provisioning

Provisioning of residential voice and data subscriber services is a repetitive task and at the same time constitutes a key differentiation area for service providers. This paper presents an approach to subscriber service provisioning that is built around a concept of service and provision profiles. They allow services to be described in a network-independent manner and at the same time provide a formal method to define and derive the necessary network provisioning steps. Because they are machine interpretable, they can be leveraged to build provisioning systems that automate the subscriber provisioning process and are very easy to customize.

Decentralized computation of threshold crossing alerts

Threshold crossing alerts (TCAs) indicate to a management system that a management variable, associated with the state, performance or health of the network, has crossed a certain threshold. The timely detection of TCAs is essential to proactive management. This paper focuses on detecting TCAs for network-level variables, which are computed from device-level variables using aggregation functions, such as SUM, MAX, or AVERAGE. It introduces TCA-GAP, a novel protocol for producing network-wide TCAs in a scalable and robust manner. The protocol maintains a spanning tree and uses local thresholds, which adapt to changes in network state and topology, by allowing nodes to trade unused “threshold space”. Scalability is achieved through computing the thresholds locally and through distributing the aggregation process across all nodes. Fault-tolerance is achieved by a mechanism that reconstructs the spanning tree after node addition, removal or failure. Simulation results on an ISP topology show that the protocol successfully concentrates traffic overhead to periods where the aggregate is close to the given threshold.

Managing virtualization of networks and services

Session 1: Decentralized and Peer-to-Peer Management.- Botnets for Scalable Management.- Self-organizing Monitoring Agents for Hierarchical Event Correlation.- Market-Based Hierarchical Resource Management Using Machine Learning.- Session 2: Fault Detection and Diagnosis.- Probabilistic Fault Diagnosis Using Adaptive Probing.- Fault Representation in Case-Based Reasoning.- Fault Detection in Autonomic Networks Using the Concept of Promised Cooperation.- Session 3: Performance Tuning and Dimensioning.- On Fully Distributed Adaptive Load Balancing.- Smart Dimensioning of IP Network Links.- Managing Performance of Aging Applications Via Synchronized Replica Rejuvenation.- Session 4: Problem Detection and Mitigation.- Dependency Detection Using a Fuzzy Engine.- Bottleneck Detection Using Statistical Intervention Analysis.- Mitigating the Lying-Endpoint Problem in Virtualized Network Access Frameworks.- Session 5: Operations and Tools.- On the Risk Exposure and Priority Determination of Changes in IT Service Management.- Assessing Operational Impact in Enterprise Systems by Mining Usage Patterns.- Virtualization-Based Techniques for Enabling Multi-tenant Management Tools.- Session 6: Short Papers.- Offloading IP Flows onto Lambda-Connections.- Virtualized Interoperability Testing: Application to IPv6 Network Mobility.- NADA - Network Anomaly Detection Algorithm.- IT Service Management Automation - An Automation Centric Approach Leveraging Configuration Control, Audit Verification and Process Analytics.- Proposal on Network-Wide Rollback Scheme for Fast Recovery from Operator Errors.- Session 7: Service Accounting and Auditing.- AURIC: A Scalable and Highly Reusable SLA Compliance Auditing Framework.- Customer Service Management for Grid Monitoring and Accounting Data.- LINUBIA: A Linux-Supported User-Based IP Accounting.- Session 8: Web Services and Management.- Efficient Web Services Event Reporting and Notifications by Task Delegation.- Transactions for Distributed Wikis on Structured Overlays.

Management by network search

While networked systems hold and generate vast amounts of configuration and operational data, this data is not accessible through a simple, uniform mechanism. Rather, it must be gathered using a range of different protocols and interfaces. Our vision is to make all this data available in a simple format through a realtime search process which runs within the network and aggregates the data into a form needed by applications - a concept we call network search. We believe that such an approach, though challenging, is technically feasible and will enable rapid development of new management applications and advanced network functions. This paper motivates and formulates the concept of network search, compares it to related concepts like web search, outlines a search architecture, describes the design space and research challenges, and reports on a testbed implementation with management applications built for exploratory purposes of this new paradigm.

NETradamus: A forecasting system for system event messages

Proactive service assurance depends on the ability to anticipate problems in a network. This paper presents a system to forecast high-severity system event messages before they occur. Forecasts are based on a stream of syslog messages from devices, which is compared and scored against a set of codes that are representative of known problem event patterns. The system includes a component to mine relevant event patterns from past problem occurrences. In addition to describing the techniques and algorithms that the system is based on, the paper also provides an assessment of the effectiveness of those techniques.

DNA: An SDN framework for distributed network analytics

Analytics of network telemetry data helps address many important operational problems. Traditional Big Data approaches run into limitations even as they push scale boundaries for processing data further. One reason for this is the fact that in many cases, the bottleneck for analytics is not analytics processing itself but the generation and export of the data on which analytics depends. The amount of data that can be reasonably collected from the network runs into inherent limitations due to bandwidth and processing constraints in the network itself. In addition, management tasks related to determining and configuring which data to generate lead to significant deployment challenges. In order to address these issues, we propose a novel distributed solution to network analytics. Analytics processing is performed at the source of the data by specialized agents embedded within network devices, which also dynamically set up and reconfigure telemetry data sources as required by an analytics task. An SDN controller application orchestrates network analytics tasks across the network to allow users to interact with the network as a whole instead of individual devices one at a time. The solution has been implemented as a proof-of-concept, called DNA (Distributed Network Analytics).1