Arani Bhattacharya

Jyoti Shokhanda, Utkarsh Pal, Aman Kumar et al.

Optimizing tail latency while efficiently managing computational resources is crucial for delivering high-performance, latency-sensitive services in edge computing. Emerging applications, such as augmented reality, require low-latency computing services with high reliability on user devices, which often have limited computational capabilities. Consequently, these devices depend on nearby edge servers for processing. However, inherent uncertainties in network and computation latencies stemming from variability in wireless networks and fluctuating server loads make service delivery on time challenging. Existing approaches often focus on optimizing median latency but fall short of addressing the specific challenges of tail latency in edge environments, particularly under uncertain network and computational conditions. Although some methods do address tail latency, they typically rely on fixed or excessive redundancy and lack adaptability to dynamic network conditions, often being designed for cloud environments rather than the unique demands of edge computing. In this paper, we introduce SafeTail, a framework that meets both median and tail response time targets, with tail latency defined as latency beyond the 90^th percentile threshold. SafeTail addresses this challenge by selectively replicating services across multiple edge servers to meet target latencies. SafeTail employs a reward-based deep learning framework to learn optimal placement strategies, balancing the need to achieve target latencies with minimizing additional resource usage. Through trace-driven simulations, SafeTail demonstrated near-optimal performance and outperformed most baseline strategies across three diverse services.

Vasudevan Nagendra, Abhishek Pokala, Arani Bhattacharya et al.

Proactive detection of network configuration bugs is important to ensure its proper functioning and reduce cost of network administrator. In this research, we propose to build the control plane verification engine MAVERICK that detects the bugs in the network control plane i.e., network device configurations and control plane states. MAVERICK automatically infers signatures for the control plane configurations (e.g., ACLs, route-maps, route-policies and so on) and states that allows administrators to automatically detect bugs with minimal human intervention. MAVERICK achieves this by effectively leveraging any structural deviation i.e., outliers in the network configurations that is organized as simple or complexly nested key-value pairs. The outliers that are calculated using signature-based outlier detection mechanism are further characterized for its severity and ranked or re-prioritized according to their criticality. We consider a wide set of heuristics and domain expertise factors for effectively to reduce both false positives and false negatives.Our evaluation on four medium to large-scale enterprise networks show that MAVERICK can automatically detect the bugs present in the network with approximately 75% accuracy. Further-more, With minimal administrator input i.e., with a few minutes of signature re-tuning, MAVERICK allows the administrators to effectively detect approximately 94 - 100% of the bugs present in the network, thereby ranking down less severe bugs and removing false positives.

Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran et al.

Consumer IoT is characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and security management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables conflict-free policy specification and enforcement in IoT environments. VISCR converts the topology of the IoT infrastructure into a tree-based abstraction and translates existing policies from heterogeneous vendor-specific programming languages such as Groovy-based SmartThings, OpenHAB, IFTTT-based templates, and MUD-based profiles into a vendor-independent graph-based specification. Using the two, VISCR can automatically detect rouge policies, conflicts, and bugs for coherent automation. Upon detection, VISCR infers new policies and proposes them to users as alternatives to existing policies for fine-tuning and conflict-free enforcement. We evaluated VISCR using a dataset of 907 IoT apps, programmed using heterogeneous automation specifications in a simulated smart-building IoT infrastructure. In our experiments, among 907 IoT apps, VISCR exposed 342 of IoT apps as exhibiting one or more violations. VISCR detected 100% of violations reported by existing state-of-the-art tool, while detecting new types of violations in an additional 266 apps. In terms of performance, VISCR can generate 400 abstraction trees (used in specifying policies) with 100K leaf nodes in <1.2sec. In our experiments, VISCR took 80.7 seconds to analyze our infrastructure of 907 apps; a 14.2X reduction compared to the state-of-the-art. After the initial analysis, VISCR is capable of adopting new policies in sub-second latency to handle changes.