Recent studies show that, voltage scaling, which is an efficient energy management technique, has a direct and negative effect on system reliability because of the increased rate of transient faults (e.g., those induced by cosmic particles). In this work, we propose energy management schemes that explicitly take system reliability into consideration. The proposed reliability-aware energy management schemes dynamically schedule recoveries for tasks to be scaled down to recuperate the reliability loss due to energy management. Based on the amount of available slack, the application size and the fault rate changes, we analyze when it is profitable to reclaim the slack for energy savings without sacrificing system reliability.Checkpoint technique is further explored to efficiently use the slack. Analytical and simulation results show that, the proposed schemes can achieve comparable energy savings as ordinary energy management schemes while preserving system reliability. The ordinary energy management schemes that ignore the effects of voltage scaling on fault rate changes could lead to drastically decreased system reliability.

While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low-Rate DDoS (LR-DDoS) attacks are known to be difficult to detect, particularly in a software-defined network (SDN). Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e., J48, Random Tree, REP Tree, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM)) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real-world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR-DDoS attacks. INDEX TERMS DDoS attack mitigation, Low-rate DDoS (LR-DDoS) attacks, Machine learning, Software-defined network (SDN).

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Although the scheduling problem for multiprocessor real-time systems has been studied for decades, it is still an evolving research field with many open problems. In this work, focusing on periodic real-time tasks, we propose a novel optimal scheduling algorithm, namely boundary fair (Bfair), which follows the same line of research as the well-known Pfair scheduling algorithms and can also achieve full system utilization. However, different from the Pfair algorithms that make scheduling decisions at every time unit to enforce proportional progress (i.e., fairness) for each task, Bfair makes scheduling decisions and enforces fairness to tasks only at tasks' period boundaries. The correctness of the Bfair algorithm to meet the deadlines of all tasks' instances * A preliminary version of this paper appeared in IEEE RTSS 2003. This work is supported in part by NSF award CNS-0720651. 1 is formally proved. The performance of Bfair is evaluated through extensive simulations. The results show that, compared to that of the Pfair algorithms, Bfair can significantly reduces the number of scheduling points (by upto 94%) and the time overhead of Bfair is comparable to that of the most efficient Pfair algorithm (i.e., P D 2 ). Moreover, by aggregating the time allocation of tasks for the time interval between consecutive period boundaries, the resulting Bfair schedule needs dramatically reduced number of context switches and task migrations, as low as 18% and 15%, respectively, when compared to those of Pfair schedules.

While Dynamic Voltage Scaling (DVS) remains as a popular energy management technique for modern computing systems, recent research has identified significant and negative impacts of voltage scaling on system reliability. To preserve system reliability under DVS settings, a number of reliability-aware power management (RA-PM) schemes have been recently studied. However, the existing RA-PM schemes normally schedule a separate recovery for each task whose execution is scaled down and are rather conservative. To overcome such conservativeness, we study in this article novel RA-PM schemes based on the shared recovery (SHR) technique. Specifically, we consider a set of frame-based real-time tasks with individual deadlines and a common period where the precedence constraints are represented by a directed acyclic graph (DAG). We first show that the earliest deadline first (EDF) algorithm can always yield a schedule where all timing and precedence constraints are met by considering the effective deadlines of tasks derived from as late as possible (ALAP) policy, provided that the task set is feasible. Then, we propose a shared recovery based frequency assignment technique (namely SHR-DAG) and prove its optimality to minimize energy consumption while preserving the system reliability. To exploit additional slack that arises from early completion of tasks, we also study a dynamic extension for SHR-DAG to improve energy efficiency and system reliability at runtime. The results from our extensive simulations show that, compared to the existing RA-PM schemes, SHR-DAG can achieve up to 35% energy savings, which is very close to the maximum achievable energy savings. More interestingly, our extensive evaluation also indicates that the new schemes offer non-trivial improvements on system reliability over the existing RA-PM schemes as well.