Workflows are the de facto standard for managing business processes and allow businesses to automate interactions between business locations and partners residing anywhere on the planet. This, however, requires the workflows to be executed in a dynamic environment, where device and communication failures occur frequently, making availability a key concern. In this work, we propose the replicated execution of workflows for ensuring availability in the presence of failures. The replicated execution has to yield the same result as a non-replicated execution of that workflow. Thus, we formally define Single-Execution-Equivalence and present a replication scheme that adheres to this definition. We implement a proof-of-concept using an open-source workflow engine for demonstrating the compatibility with current workflow technology. Our evaluations on Amazon EC2, OpenStack, and PlanetLab show that workflow replication ensures availability while being scalable and incurring low overhead in terms of execution time.

In recent years, the graph partitioning problem gained importance as a mandatory preprocessing step for distributed graph processing on very large graphs. Existing graph partitioning algorithms minimize partitioning latency by assigning individual graph edges to partitions in a streaming manner - at the cost of reduced partitioning quality. However, we argue that the mere minimization of partitioning latency is not the optimal design choice in terms of minimizing total graph analysis latency, i.e., the sum of partitioning and processing latency. Instead, for complex and long-running graph processing algorithms that run on very large graphs, it is beneficial to invest more time into graph partitioning to reach a higher partitioning quality - which drastically reduces graph processing latency. In this paper, we propose ADWISE, a novel window-based streaming partitioning algorithm that increases the partitioning quality by always choosing the best edge from a set of edges for assignment to a partition. In doing so, ADWISE controls the partitioning latency by adapting the window size dynamically at run-time. Our evaluations show that ADWISE can reach the sweet spot between graph partitioning latency and graph processing latency, reducing the total latency of partitioning plus processing by up to 23-47 percent compared to the state-of-the-art.

With the vision of Internet of Things gaining popularity at a global level, efficient publish/subscribe middleware for communication within and across data centers is extremely desirable. In this respect, the very popular Software-Defined Networking, which enables publish/subscribe middleware to perform line-rate filtering of events directly on hardware, can prove to be very useful. While deploying content filters directly on switches of a software-defined network allows optimized paths, high throughput rates, and low end-to-end latency, it suffers from certain inherent limitations with respect to number of bits available on hardware switches to represent these filters. Such a limitation affects expressiveness of filters, resulting in unnecessary traffic in the network. In this paper, we explore various complementary techniques to represent content filters expressively while being limited by hardware. We implement and evaluate techniques that i) use workload, in terms of events and subscriptions, to represent content, and ii) efficiently select attributes to reduce redundancy in content. Our detailed performance evaluations show the potential of these techniques in reducing unnecessary traffic when subjected to different workloads. Furthermore, the techniques proposed in this paper require significant updates to the network, i.e., the data plane, which must be performed in a consistent manner to ensure desired system behavior. As a result, in this paper, we, also, design and evaluate a light-weight approach that ensures data plane consistency in the presence of dynamic network updates.

Distributed graph processing systems such as Pregel, PowerGraph, or GraphX gained popularity due to their superior performance of data analytics on graph-structured data. These systems employ partitioning algorithms to parallelize graph analytics while minimizing inter-partition communication. Recent partitioning algorithms, however, unrealistically assume a uniform and constant amount of data exchanged between graph vertices (i.e., uniform vertex traffic) and homogeneous network costs between workers hosting the graph partitions. This leads to suboptimal partitioning decisions and inefficient graph processing. To this end, we developed Grapes, the first graph processing system using vertex-cut graph partitioning that considers both, diverse vertex traffic and heterogeneous network costs. The main idea is to avoid frequent communication over expensive network links using an adaptive edge migration strategy. Our evaluations show an improvement of 10 percent in graph processing latency and 60 percent in communication costs compared to state-of-the-art partitioning approaches.

Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance; then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores.

With the increasing popularity of software-defined networking (SDN), ternary content-addressable memory of switches can be directly accessed by a publish/subscribe middleware to perform filtering operations at low latency. In this way, three important requirements for a publish/subscribe middleware can be fulfilled, namely, bandwidth efficiency, line-rate performance, and low latency in forwarding messages between producers and consumers. Nevertheless, it is challenging to sustain line-rate performance in the presence of dynamically changing interests of producers and consumers. In this paper, we realize a scalable, SDN-based publish/subscribe middleware, called PLEROMA, that performs efficient forwarding at line-rate. Moreover, PLEROMA offers methods to efficiently reconfigure a deployed topology in the presence of dynamic subscriptions and advertisements. We evaluate the performance of both the data plane and the control plane of PLEROMA to support our claim. Furthermore, we evaluate and benchmark the performances of SDN-compliant hardware and software switches in the context of our middleware.

Over the past few decades, content-based publish/subscribe has been primarily implemented as an overlay network of software brokers. Even though such systems provide the possibility of bandwidth efficient expressive filtering in software, they cannot match up to the performance (in terms of end-to-end latency and throughput) of communication protocols implemented on the network layer. To exploit network layer performance benefits, recently, content-based publish/subscribe was realized using the capabilities of Software-defined Networking (SDN). While SDN allows line-rate forwarding of events by content filters directly installed on switches, it suffers from inherent hardware limitations (w.r.t. flow table size, limited availability of bits in header fields) that adversely impact expressiveness of these filters, resulting in unnecessary network traffic. In this paper, we strike a balance between purely application-layer-based and purely network-layer-based publish/subscribe implementations by realizing the first hybrid content-based middleware that enables filtering of events in both layers. Moreover, we provide different selection algorithms with varying degrees of complexity to determine the events to be filtered at each layer such that unnecessary network traffic can be minimized while also considering delay requirements of the middleware. Our hybrid middleware offers full flexibility to configure it according to the performance requirements of the system. We provide a detailed performance evaluation of the proposed selection algorithms to determine their impact on the performance of the designed hybrid middleware which we further compare to state-of-the art solutions.

Vertex-centric graph processing systems such as Pregel, PowerGraph, or GraphX recently gained popularity due to their superior performance of data analytics on graph-structured data. These systems exploit the graph structure to improve data access locality during computation, making use of specialized graph partitioning algorithms. Recent partitioning techniques assume a uniform and constant amount of data exchanged between graph vertices (i.e., uniform vertex traffic) and homogeneous underlying network costs. However, in real-world scenarios vertex traffic and network costs are heterogeneous. This leads to suboptimal partitioning decisions and inefficient graph processing. To this end, we designed GrapH, the first graph processing system using vertex-cut graph partitioning that considers both, diverse vertex traffic and heterogeneous network, to minimize overall communication costs. The main idea is to avoid frequent communication over expensive network links using an adaptive edge migration strategy. Our evaluations show an improvement of 60% in communication costs compared to state-of-the-art partitioning approaches.

In order to be able to successfully defend an IT system it is useful to have an accurate appreciation of the cyber threat that goes beyond stereotypes. To effectively counter potentially decisive and skilled attackers it is necessary to understand, or at least model, their behavior. Although the real motives for untraceable anonymous attackers will remain a mystery, a thorough understanding of their observable actions can still help to create well-founded attacker profiles that can be used to design effective countermeasures and in other ways enhance cyber defense efforts. In recent work empirically founded attacker profiles, so-called attacker personas, have been used to assess the overall threat situation for an organization. In this paper we elaborate on 1) the use of attacker personas as a technique for attacker profiling, 2) the design of tailor-made cyber defense exercises for the purpose of obtaining the necessary empirical data for the construction of such attacker personas, and 3) how attacker personas can be used for enhancing the situational awareness within the cyber domain. The paper concludes by discussing the possibilities and limitations of using cyber defense exercises for data gathering, and what can and cannot be studied in such exercises.

Knowledge-intensive organizations are characterized by their dependency on highly skilled personnel who perform their daily work in a decentralized manner. In these organizations it is the users who make the important decisions, and therefore the organization's information security awareness is upheld by and depends on its users' combined security awareness. To assess the overall organizational security awareness it therefore becomes interesting to assess both the users' individual level of security awareness, as well as their level of consistency and conformity with regard to other users' awareness. In the present case study, 15 semi-structured interviews have been undertaken within a large telecommunication company in order to understand how significant IT security aspects are understood within the organization. The study highlights a number of perception differences where the technical IT staff and the ordinary users do not share the same understanding. It is suggested that these perception differences result from a paradoxical situation where the users' possibility to uphold security awareness is hindered because of security concerns.

With the increasing popularity of Software-defined networks (SDN), TCAM memory of switches can be directly accessed by a publish/subscribe middleware to perform filtering operations at low latency. This way two important requirements for a publish/subscribe middleware can be fulfilled: namely bandwidth efficiency and line-rate performance in forwarding messages between producers and consumers. Nevertheless, it is challenging to sustain line-rate performance in the presence of dynamic changes in the interest of producers and consumers. In this paper, we propose and evaluate the PLEROMA middleware to realize publish/subscribe at line-rate and bandwidth efficiently in SDN. PLEROMA offers methods to efficiently reconfigure a deployed topology in the presence of dynamic subscriptions and advertisements. Furthermore, PLEROMA ensures interoperability and independent reconfiguration of multiple controlled SDN networks.

The provisioning of basic security mechanisms such as authentication and confidentiality is highly challenging in a content-based publish/subscribe system. Authentication of publishers and subscribers is difficult to achieve due to the loose coupling of publishers and subscribers. Likewise, confidentiality of events and subscriptions conflicts with content-based routing. This paper presents a novel approach to provide confidentiality and authentication in a broker-less content-based publish/subscribe system. The authentication of publishers and subscribers as well as confidentiality of events is ensured, by adapting the pairing-based cryptography mechanisms, to the needs of a publish/subscribe system. Furthermore, an algorithm to cluster subscribers according to their subscriptions preserves a weak notion of subscription confidentiality. In addition to our previous work , this paper contributes 1) use of searchable encryption to enable efficient routing of encrypted events, 2) multicredential routing a new event dissemination strategy to strengthen the weak subscription confidentiality, and 3) thorough analysis of different attacks on subscription confidentiality. The overall approach provides fine-grained key management and the cost for encryption, decryption, and routing is in the order of subscribed attributes. Moreover, the evaluations show that providing security is affordable w.r.t. 1) throughput of the proposed cryptographic primitives, and 2) delays incurred during the construction of the publish/subscribe overlay and the event dissemination.

SUMMARY Current distributed publish/subscribe systems consider all participants to have similar QoS requirements and contribute equally to the system's resources. However, in many real‐world applications, the message delay tolerance of individual participants may differ widely. Disseminating messages according to individual delay requirements not only allows for the satisfaction of user‐specific needs, but also significantly improves the utilization of the resources that participants contribute to a publish/subscribe system. In this article, we propose a peer‐to‐peer‐based approach to satisfy the individual delay requirements of subscribers in the presence of bandwidth constraints. Our approach allows subscribers to dynamically adjust the granularity of their subscriptions according to their bandwidth constraints and delay requirements. Subscribers maintain the overlay in a decentralized manner, exclusively establishing connections that satisfy their individual delay requirements, and that provide messages exactly meeting their subscription granularity. The evaluations show that for many practical workloads, the proposed publish/subscribe system can scale up to a large number of subscribers and performs robustly in a very dynamic setting. Copyright © 2011 John Wiley & Sons, Ltd.