Cihan Tunc

Variation tolerant logic mapping for crossbar array nano architectures

Bottom-up self-assembly nanofabrication process yields nanodevices with significantly more variations compared to the conventional top-down lithography used in CMOS fabrication. This is in addition to an increased defect density expected for self-assembled nanodevices. Therefore, it is one of the major design challenges to tolerate variation, in addition to defect tolerance, in emerging nano architectures. In this paper, we present a solution for variation tolerant logic mapping for FET based crossbar array nano architectures using Simulated Annealing. Furthermore, we extended the framework for defect tolerance. Experimental results including comparison with exact method confirm the effectiveness of the proposed approach.

Autonomic Resilient Cloud Management (ARCM) Design and Evaluation

Cloud Computing is emerging as a new paradigm that aims delivering computing as a utility. For the cloud computing paradigm to be fully adopted and effectively used, it is critical that the security mechanisms are robust and resilient to faults and attacks. Securing cloud systems is extremely complex due to the many interdependent tasks such as application layer firewalls, alert monitoring and analysis, source code analysis, and user identity management. It is strongly believed that we cannot build cloud services that are immune to attacks. Resiliency to attacks is becoming an important approach to address cyber-attacks and mitigate their impacts. Resiliency for mission critical systems is demanded higher. In this paper, we present a methodology to develop an Autonomic Resilient Cloud Management (ARCM) based on moving target defense, cloud service Behavior Obfuscation (BO), and autonomic computing. By continuously and randomly changing the cloud execution environments and platform types, it will be difficult especially for insider attackers to figure out the current execution environment and their existing vulnerabilities, thus allowing the system to evade attacks. We show how to apply the ARCM to one class of applications, Map/Reduce, and evaluate its performance and overhead.

On-the-fly variation tolerant mapping in crossbar nano-architectures

In hybrid nano-architectures, self-assembled nanoscale crossbars are fabricated on top of a reliable CMOS subsystem. Bottom-up self-assembly nanofabrication process, used in nano-architectures, yields nanodevices with significantly more variations compared to the conventional top-down lithography used in CMOS fabrication. This is in addition to an increased defect density expected for self-assembled nanodevices. Therefore, it is one of the major design challenges to tolerate variation and defects in emerging nano architectures. In this paper, we present an alternative approach for variation and defect tolerant mapping in which no application-independent test and characterization (defect and variation map) is required. The variation tolerant mapping is done on-the-fly which can ultimately be transformed into built-in self-mapping. Different mapping algorithms are presented and their efficiencies in terms of variation and defect tolerance as well as mapping time are compared. The experimental results show that the proposed heuristic mapping algorithm can achieve the same success rate with the exhaustive method in terms of meeting required timing constraints with orders of magnitude fewer reconfiguration retries.

Value-Based Resource Management in High-Performance Computing Systems

We introduce a new metric, Value of Service (VoS), which enables resource management techniques for high-performance computing (HPC) systems to take into consideration the value of completion time of a task and the value of energy used to compute that task at a given instant of time. These value functions have a soft-threshold, where the value function begins to decrease from its maximum value, and a hard-threshold, where the value function goes to zero. Each task has an associated importance factor to express the relative significance among tasks. We define the value of a task as the weighted sum of its value of performance and value of energy, multiplied by its importance factor. We also consider the variation in value for completing a task at different time; the value of energy reduction can change significantly between peak and non-peak periods. We define VoS for a given workload to be sum of the values for all tasks that are executed during a given period of time. Our system model is based on virtual machines (VMs), where each dynamically arriving task will be assigned to a VM with a resource configuration based on number of homogenous cores and amount of memory. Based on VoS, we design, evaluate, and compare different resource management heuristics. This comparison is done over various simulation scenarios and example experiments on an IBM blade server based system.

Secure and Resilient Cloud Services for Enhanced Living Environments

It is critical to provide enhanced living environments (ELEs) to people with special needs (such as the elderly and individuals with disabilities) that offer 24/7 continuous monitoring and control of the environment and access to care services when needed. Recently, there has been a strong interest in building ELEs using implantable and wearable sensors, and wireless sensor networks that are supported by cloud computing. However, ELE technologies and information are vulnerable to cyberattacks and exploitations that can lead to life-threatening scenarios such as incorrect medical diagnoses. This article presents a platform that offers secure and resilient services for ELEs. The main components of the platform are the ELE end nodes, secure gateway, and a secure and resilient cloud computing system. End nodes collect ELE variables and human body signals that are stored securely in the cloud using a secure gateway. The secure gateway manages communication between the end nodes and the cloud services using biocyber metrics for authentication. In addition, the cloud architecture provides the required ELE services at any time and from anywhere in a resilient manner.

Automated Framework for Scalable Collection and Intelligent Analytics of Hacker IRC Information

Cyber security is a challenging research problem especially when one considers exponential growth in information technologies. Most previous cyber security research have generally centered on securing and protecting physical resources (computers, network devices, and mobile platforms), protocols and applications. However, little work has focused on the human side and behavior, what motivates cyber attackers to launch attacks, their goals, and where they get their hacking and attacking tools. In this paper, we present an automated approach to collect information about hackers, and attempt to understand their behaviors and goals. Internet Relay Chat (IRC) forums have been widely used by hackers to exchange data, tools and train new novice hackers. We present our approach to implement an automated framework that uses several bots to collect IRC messages from malicious forums and analyze them. A resilient botnet mechanism is utilized to ensure complete IRC data collection. In addition, we present an intelligent hacking language module based on Stanford CoreNLP to analyze hacker activity. Our experimental results show that our botnets can be used to effectively monitor, analyze, and predict hacker activities and goals.

Anomaly Behavior Analysis System for ZigBee in smart buildings

Smart Building (SB) exploits advances in information and communication technologies in order to provide the next generation of information and automation services that will significantly reduce operational costs and improve performance and efficiency. SB elements are typically interconnected using short range wireless communication technologies such as ZigBee, which is the most used wireless communication protocol for SBs. However, ZigBee protocol has multiple vulnerabilities that can be exploited by cyberattacks. In this paper, we present an Anomaly Behavior Analysis System (ABAS) for ZigBee protocol to be used in SBs. Our ABAS can detect both known and unknown ZigBee attacks with a high detection rate and low false alarms. Additionally, after detection, our system classifies the attack based on the impact, origin, and destination. We evaluate our approach by launching many attack scenarios such as DoS, Flooding, and Pulse DoS attacks, and then we compare our results with other intrusion detection systems such as secure HAN, signature IDS, and specification IDS.

Value of service based resource management for large-scale computing systems

Task scheduling for large-scale computing systems is a challenging problem. From the users perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing operation cost while providing the required service is critical. Therefore, it is important for task scheduling mechanisms to balance users’ performance requirements and energy efficiency because energy consumption is one of the major operational costs. We present a time dependent value of service (VoS) metric that will be maximized by the scheduling algorithm that take into consideration the arrival time of a task while evaluating the value functions for completing a task at a given time and the tasks energy consumption. We consider the variation in value for completing a task at different times such that the value of energy reduction can change significantly between peak and non-peak periods. To determine the value of a task completion, we use completion time and energy consumption with soft and hard thresholds. We define the VoS for a given workload to be the sum of the values for all tasks that are executed during a given period of time. Our system model is based on virtual machines, where each task will be assigned a resource configuration characterized by the number of the homogeneous cores and amount of memory. For the scheduling of each task submitted to our system, we use the estimated time to compute matrix and the estimated energy consumption matrix which are created using historical data. We design, evaluate, and compare our task scheduling methods to show that a significant improvement in energy consumption can be achieved when considering time-of-use dependent scheduling algorithms. The simulation results show that we improve the performance and the energy values up to 49% when compared to schedulers that do not consider the value functions. Similar to the simulation results, our experimental results from running our value based scheduling on an IBM blade server show up to 82% improvement in performance value, 110% improvement in energy value, and up to 77% improvement in VoS compared to schedulers that do not consider the value functions.

An Autonomic Workflow Performance Manager for Weather Research and Forecast Workflows

Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast (WRF) simulations. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying the parameter configuration that leads to most accurate prediction. We present an autonomic workflow performance manager that can automatically manage model initialization and workflow execution for a given resource allocation. We model the configuration selection of WRF workflow using Apache Storm and automate the process of model initialization, configuration and execution. We reduce the timescale of the configuration search workflow by a factor of 10x by using 20 threads when compared to serial workflow execution as it is typically performed by domain scientists.

Value Based Scheduling for Oversubscribed Power-Constrained Homogeneous HPC Systems

Power-aware scheduling has become a critical research thrust for deploying exascale High Performance Computing (HPC) systems with limited power budget. Time-varying pricing of electricity with respect to the market demand and dynamic HPC workloads can lead to unpredictable operational cost, which complicates the scheduling decisions further. For an oversubscribed HPC system, value based scheduling heuristics have been shown to be a more productive option for scheduling time-constrained tasks over priority and deadline based heuristics. However, oversubscribed HPC systems have higher probability of exceeding the power constraints. Earlier studies on value based heuristics do not take power constraints into account during scheduling decisions. In this study, we propose a methodology for deriving task-specific power-execution time models. These models are derived by interpolating the execution time and power consumption measurements over a configuration space parameterized with pairs of dynamic voltage frequency scaling and forced idleness values. We then propose two power-aware value based heuristics, which utilize those models for power capping the nodes and making resource allocation decisions in an oversubscribed homogeneous HPC system. We compare their performance with traditional value based heuristics under a defined power constraint on a real system using different synthetic traces of scientific computing routines. We show that, as power constraints become tighter, the proposed heuristics significantly outperform earlier heuristics in terms of value earning of the HPC system. We also compare the task completion percentage of proposed heuristics and relate the completion percentage with value earnings of the heuristics.