A fast and reliable method to detect faulty IoT devices is indispensable in IoT environments. In this paper, we present DICE, an automatic method to detect and identify faulty IoT devices with context extraction. Our system works in two phases. In a precomputation phase, the system precomputes sensor correlation and the transition probability between sensor states known as context. During a real-time phase, the system finds a violation of sensor correlation and transition to detect and identify the faults. In detection, we analyze the sensor data to find any missing or newly reacting IoT devices that are deviating from already grouped correlated sensors, and state transition to find the presence of an abnormal sequence. Then, the system identifies the faulty device by comparing the problematic context with the probable ones. We demonstrate that DICE identifies faulty devices accurately and promptly through the evaluation on various fault types and datasets.

Modern servers are actively deploying Solid-State Drives (SSDs). However, rather than just a fast storage device, SSDs are complex devices designed for device-specific goals (e.g., latency, throughput, endurance, cost) with their internal mechanisms undisclosed to users as the proprietary asset, which leads to unpredictable, irregular inter/intra-SSD access latencies. This unpredictable irregular access latency has been a fundamental challenge to server architects aiming to satisfy critical quality-of-service requirements and/or achieve the full performance potential of commodity SSDs. In this paper, we propose SSDcheck, a novel SSD performance model to accurately predict the latency of next access to commodity black-box SSDs. First, after analyzing a wide spectrum of real-world SSDs, we identify key performance-critical features (e.g., garbage collection, write buffering) required to construct a general SSD performance model. Next, SSDcheck runs diagnosis code snippets to extract static feature parameters (e.g., size, threshold) from the target SSD, and constructs its performance model. Finally, during runtime, SSDcheck dynamically manages the performance model to predict the latency of the next access. Our evaluations show that SSDcheck achieves up to 98.96% and 79.96% on-average prediction accuracy for normal-latency and high-latency predictions, respectively. Next, we show the effectiveness of SSDcheck by implementing a new volume manager improving the throughput by up to 4.29x with the tail latency reduction down to 6.53%, and a new I/O request handler improving the throughput by up to 44.0% with the tail latency reduction down to 26.9%. We then show how to further improve the results of scheduling with the help of an emerging Non-Volatile Memory (e.g., PCM). SSDcheck does not require any hardware modifications, which can be harmlessly disabled for any SSDs uncovered by the performance model.

Improvement in the security and availability is important for the success of the Internet of Things (IoT). Given that recent IoT devices are likely to have multiple functionalities and support third-party applications, this goal becomes challenging to achieve. Through an in-depth investigation of existing IoT frameworks, we focused on two inherent security flaws in their design caused by their device-centric approaches: (1) coarse-grained access control and (2) lack of resource isolation. Because of the coarse-grained access control, IoT devices suffer from over-privileged applications. Furthermore, the lack of resource isolation allows the possibility of Denial-of-Service attacks.

Recent efforts towards mobile cloud propose to offload mobile applications to cloud servers for the improved performance and battery life of mobile devices. However, existing schemes completely ignore the costs of cloud resources by assuming that idle servers are always available for free of charge. These unrealistic assumptions make each server run only a small load to achieve the guaranteed high offload performance. Therefore, these schemes cannot be applied to real-world commercial clouds which aim to minimize the operation costs by maximizing the server throughput, and then charge users for their resource usage. In this paper, we propose CMcloud, a novel cost-effective mobile-to-cloud offloading platform, which works nicely under the real-world cloud environments. CMcloud minimizes both the server costs and the user service fee by offloading as many mobile applications to a single server as possible, while satisfying the target performance of all applications. To achieve such goals, CMcloud exploits novel architecture performance modeling and server migration techniques. Our implementation shows that CMcloud can improve the data enter throughput by 84% over a conventional static light-load scheme (or a 2.7x higher per-socket throughput.) Alternatively, CMcloud reduces the number of service failures by 83% over a static high-load scheme, while even improving the throughput by 31%.

Cloud infrastructure providers deploy Dynamic Resource Management (DRM) to minimize the cost of datacenter operation, while maintaining the Service Level Agreement (SLA). Such DRM schemes depend on the capability to migrate virtual machine (VM) images. However, existing migration techniques are not suitable for highly utilized clouds due to their latency and bandwidth critical memory transfer mechanisms. In this paper, we propose guide-copy migration, a novel VM migration scheme to provide a fast and silent migration, which works nicely under highly utilized clouds. The guide-copy migration transfers only the memory pages accessed at the destination node in the near future by running a guide version of the VM at the source node and a migrated VM at the destination node simultaneously during the migration. The guide-copy migration's highly accurate and low-bandwidth memory transfer mechanism enables a fast and silent VM migration to maintain the SLA of all VMs in the cloud.