Spectrum sensing systems provide real-time feedback essential for spectrum sharing. However, the growth of spectrum sharing is limited by the capabilities of these spectrum sensors. Sharing a new frequency band is only possible if sensors can detect activity in that band with sufficient time granularity and signal fidelity to meet spectrum sharing policy requirements. In this work, we introduce Crescendo, a system design that shows we can achieve wideband, real-time, high-fidelity spectrum sensing using sweeping spectrum sensors. We first provide an analysis that demonstrates there are operating points of sweeping sensors that can sense multiple popular protocols. Then we demonstrate these sensors can be built in practice with an adaptive gain super-heterodyne RF frontend with high-fidelity LO generation, and evaluate a prototype built with COTS components. In our benchmarks, Crescendo outperforms prior wideband spectrum sensors, achieving a 30 dB increase in dynamic range and 10 dB increase in SNR.

Congestion in communication networks can be modeled as a percolation process, where congestion spreads minimally before a critical load and expands rapidly afterwards. Some studies identify predict onset of rapidly expanding congestion in time to alert network managers to take mitigating actions to avoid congestion collapse. The paper specifies five predictors: autocorrelation, variance, threshold, growth persistence, and growth rate. Predictor performance is measured for three simulated network models, under two traffic scenarios: increasing and steady load. Predictors are compared on implementation cost, accuracy, warning time, and persistence. The rates and types of prediction errors are also characterized. Results showed that: (1) predictor performance is influenced by network-model realism; (2) the autocorrelation and variance predictors performed poorly in some situations; (3) the threshold predictor yielded best overall accuracy, with mean warning time exceeding seven minutes for the most realistic network model. The paper also suggests a necessary condition to control false positives.

Setting the control parameters of a genetic algorithm to obtain good results is a long-standing problem. We define an experiment design and analysis method to determine relative importance and effective settings for control parameters of any evolutionary algorithm, and we apply this method to a classic binary-encoded genetic algorithm (GA). Subsequently, as reported elsewhere, we applied the GA, with the control parameter settings determined here, to steer a population of cloud-computing simulators toward behaviors that reveal degraded performance and system collapse. GA-steered simulators could serve as a design tool, empowering system engineers to identify and mitigate low-probability, costly failure scenarios. In the existing GA literature, we uncovered conflicting opinions and evidence regarding key GA control parameters and effective settings to adopt. Consequently, we designed and executed an experiment to determine relative importance and effective settings for seven GA control parameters, when applied across a set of numerical optimization problems drawn from the literature. This paper describes our experiment design, analysis, and results. We found that crossover most significantly influenced GA success, followed by mutation rate and population size and then by rerandomization point and elite selection. Selection method and the precision used within the chromosome to represent numerical values had least influence. Our findings are robust over 60 numerical optimization problems.

Simulations of large distributed systems, such as infrastructure clouds, usually entail a large space of parameters and responses that prove impractical to explore. To reduce the space of inputs, experimenters, guided by domain knowledge and ad hoc methods, typically select a subset of parameters and values to simulate. Similarly, experimenters typically use ad hoc methods to reduce the number of responses to analyze. Such ad hoc methods can result in experiment designs that miss significant parameter combinations and important responses, or that overweight selected parameters and responses. When this occurs, the experiment results and subsequent analyses can be misleading. In this paper, we apply an efficient sensitivity analysis method to demonstrate how relevant parameter combinations and behaviors can be identified for an infrastructure Cloud simulator that is intended to compare resource allocation algorithms. Researchers can use the techniques we demonstrate here to design experiments for large Cloud simulations, leading to improved quality in derived research results and findings.

Computer systems often exhibit degraded performance due to resource leakage caused by erroneous programming or malicious attacks, and computers can even crash in extreme cases of resource exhaustion. The advent of cloud computing provides increased opportunities to amplify such vulnerabilities, thus affecting a significant number of computer users. Using simulation, we demonstrate that cloud computing systems based on open-source code could be subjected to a simple malicious attack capable of degrading availability of virtual machines (VMs). We describe how the attack leads to VM leakage, causing orphaned VMs to accumulate over time, reducing the pool of resources available to users. We identify a set of orphan control processes needed in multiple cloud components, and we illustrate how such processes detect and eliminate orphaned VMs. We show that adding orphan control allows an open-source cloud to sustain a higher level of VM availability during malicious attacks. We also report on the overhead of implementing orphan control.

Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or tradeoffs between honoring service-level agreements and constraining provider operating costs. Even where similar problems are addressed, each individual research team evaluates proposed algorithms under distinct conditions, using various techniques, often targeted to a small collection of VMs and PMs. In this paper, we describe an objective method that can be used to compare VM-placement algorithms in large clouds, covering tens of thousands of PMs and hundreds of thousands of VMs. We demonstrate our method by comparing 18 algorithms for initial VM placement in on-demand infrastructure clouds. We compare algorithms inspired by open-source code for infrastructure clouds, and by the online bin-packing literature.

Abstract Many natural and man‐made systems exhibit self‐organization, where interactions among components lead to system‐wide patterns of behavior. This paper first introduces current, scientific understanding of self‐organizing systems and then identifies the main models investigated by computer scientists seeking to apply self‐organization to design large, distributed systems. Subsequently, the paper surveys research that uses models of self‐organization in wireless sensor networks to provide a variety of functions: sharing processing and communication capacity; forming and maintaining structures; conserving power; synchronizing time; configuring software components; adapting behavior associated with routing, with disseminating and querying for information, and with allocating tasks; and providing resilience by repairing faults and resisting attacks. The paper closes with a summary of open issues that must be addressed before self‐organization can be applied routinely during design and deployment of senor networks and other distributed, computer systems. Copyright © 2007 John Wiley & Sons, Ltd.

Creating defenses against flooding-based, distributed denial-of-service (DDoS) attacks requires real-time monitoring of network-wide traffic to obtain timely and significant information. Unfortunately, continuously monitoring network-wide traffic for suspicious activities presents difficult challenges because attacks may arise anywhere at any time and because attackers constantly modify attack dynamics to evade detection. In this paper, we propose a method for early attack detection. Using only a few observation points, our proposed method can monitor the macroscopic effect of DDoS flooding attacks. We show that such macroscopic-level monitoring might be used to capture shifts in spatial-temporal traffic patterns caused by various DDoS attacks and then to inform more detailed detection systems about where and when a DDoS attack possibly arises in transit or source networks. We also show that such monitoring enables DDoS attack detection without any traffic observation in the victim network.

Selected service-discovery systems allow clients to issue multicast queries to locate network devices and services. Qualifying devices and services respond directly to clients; thus in large network, potential exists for responses to implode on a client, overrunning available resources. To limit implosion, one service-discovery system, UPnP, permits clients to include a jitter bound in multicast (M-search) queries. Qualifying devices use the jitter bound to randomize timing of their responses. Initially, clients lack sufficient knowledge to select an appropriate jitter bound, which varies with network size. In this paper, we characterize the performance of UPnP M-search for various combinations of jitter bound and network size. In addition, we evaluate the performance and costs of four algorithms that might be used for adaptive jitter control. Finally, we suggest an alternative to M-search for large networks.

Distributed systems require strategies to detect and recover from failures. Many protocols for distributed systems employ a strategy based on leases, which grant a leaseholder access to data or services for a limited time (the lease period). Choosing an appropriate lease period involves tradeoffs among resource utilization, responsiveness, and system size. We investigate these issues for Jini Network Technology. First, we establish quantitative tradeoffs among lease period, bandwidth utilization, responsiveness, and system size. Then, we consider two self-adaptive algorithms that enable a Jini system, given a fixed allocation of resources, to vary lease periods with system size to achieve the best responsiveness. We compare performance of these self-adaptive algorithms against each other, and against fixed lease periods. We find that one of the self-adaptive algorithms proves easy to implement and performs reasonably well. We anticipate that similar procedures could add self-adaptive capability to other distributed systems that rely on leases.

This paper describes a knowledge-based approach to automate a software design method for concurrent systems. The approach uses multiple paradigms to represent knowledge embedded in the design method. Semantic data modeling provides the means to represent concepts from a behavioral modeling technique, called Concurrent Object-Based Real-time Analysis (COBRA), which defines system behavior using data/control flow diagrams. Entity-relationship modeling is used to represent a design metamodel based on a design method, called COncurrent Design Approach for Real-Time Systems (CODARTS), which represents concurrent designs as software architecture diagrams, task behavior specifications and module specifications. Production rules provide the mechanism for codifying a set of CODARTS heuristics that can generate concurrent designs based on semantic concepts included in COBRA behavioral models and on entities and relationships included in CODARTS design metamodels. Together, the semantic data model, the entity-relationship model, and the production rules, when encoded using an expert system shell, compose CODA, an automated designer's assistant. CODA is applied to generate 10 concurrent designs for four real-time problems. The paper reports the degree of automation achieved by CODA. The paper also evaluates the quality of generated designs by comparing the similarity between designs produced by CODA and human designs reported in the literature for the same problems. In addition, it compares CODA with four other approaches used to automate software design methods.

Software designers use visual models, such as data flow/control flow diagrams or object collaboration diagrams, to express system behavior in a form that can be understood easily by users and by pogrammers, and from which designers can generate a software architecture. The research described in this paper is motivated by a desire to provide an automated designer's assistant that can generate software architectures for concurrent systems directly from behavioral models expressed visually as flow diagrams. To achieve this goal, an automated designer's assistant must be capable of interpreting flow diagrams in semantic, rather than syntactic, terms. While semantic concepts can be attached manually to diagrams using labels, such as stereotypes in the Unified Model Language (UML), this paper considers the possibility of providing autmated assistance to infer appropriate tags for symbols on a flow diagram. The approach relies upon constructing an underlying metamodel that defines semantic concepts based upon (1) syntactic relationships among visual symbols and (2) inheritance relationships among semantic concepts. Given such a metamodel, a rule-based inference engine can, in many situations, infer the presence of semantic concepts on flow diagram, and can tag symbols accordingly. Futher, an object-oriented query system can compare semantic tags on digram instances for conformance with their definition in the metamodel. To illustrate the approach, the paper describes a metamodel for data flow/control flow diagrams used in the context of a specific software modeling method, Concurrent Object-Based Real-time Analysis (COBRA). The metamodel is implemented using an expert-system shell, CLIPS V6.0, which integrates an object-oriented language with a rule-based inference engine. The paper applies the implemented metamodel to design software for an automobile cruise-control system and provides an evaluation of the approach based upon results from four case studies. For the case studies, the implemented metamodel recognized, automatically and correctly, the existence of 86% of all COBRA semantic concepts within the flow diagrams. Varying degrees of human assistance were used to correctly identify the remaining semantic concepts within the diagrams: in two percent of the cases the implemented metamodel reached tentative classifications that a designer was asked to confirm or override; in four percent of the cases a designer was asked to provide additional information before a concept was classified; in the remaining eight percent of the cases the designer was asked to identify the concept.

A five-layer simulation model of OSI protocols is described and applied to predict transport performance on a local area network (LAN). Emphasis is placed on time-critical applications typical of a small, flexible manufacturing system. The results predict that, with current technology, OSI protocols can provide 1.5 Mbps throughput, one-way delays between 6 and 10 ms, and response times between 15 and 25 ms. The results also indicate that CSMA/CD is a reasonable access method for time-critical applications on small, factory LANS, if loads of less than 40% are anticipated. For loads between 40% and 70%, a token passing access method provides better performance for time-critical applications.

The NBS Protocol Performance Laboratory is developing enhanced protocol mechanisms for OSI class 4 transport that will improve the throughput efficiency achieved on a satellite channel. A selective acknowledgement mechanism has been shown to improve throughput efficiency by as much as 34%. Several alternative expedited data mechanisms have demonstrated throughput efficiency improvements as great as 38%. Most of the protocol mechanism enhancements considered require only minor changes to the international standard OSI transport protocol.